1st commit

.env

@@ -0,0 +1,5 @@
+GOOGLE_API_KEY="AIzaSyC6_ZrpMn1mh7waGlySWABdeyS2mRu4PaI"
+PINECONE_APIKEY_TITLE="1e13ddee-5c44-4db5-a79e-e9f44695c7c9"
+PINECONE_APIKEY_CONTENT="0828f7a6-2534-48cf-a83b-38711659a68d"
+OPENAI_API_KEY="sk-proj-Dfo8UC2giPx0D2xCLVCET3BlbkFJHBzbH1GLBdSw14mfb7UX"
+JOY_API_KEY="sk-proj-aJ6WW5GTBul8jhViB3vST3BlbkFJNyZ0LJvvd82VUrOKgOnP"

Explore_papers.py

@@ -0,0 +1,82 @@
+import streamlit as st
+from utils import *
+from logger import logging
+from datetime import datetime
+
+present_year = datetime.now().year
+years = list(range(1998, present_year))
+timeframe = [str(year)+' till present' for year in years]
+
+if 'clicked_search' not in st.session_state:
+    st.session_state.clicked_search = False
+if 'search_results' not in st.session_state:
+    st.session_state.search_results = []
+if 'selected_paper' not in st.session_state:
+    st.session_state.selected_paper = False
+if 'summary' not in st.session_state:
+    st.session_state.summary = False
+
+def change_state():
+    st.session_state.clicked_search = True
+
+st.set_page_config(page_title='Explore papers', page_icon='📚')
+st.subheader('Explore varieties of papers present in OnePetro\'s Catalog')
+st.divider()
+
+# search container
+with st.container():    
+    col1, col2 = st.columns([0.7, 0.3])
+    with col1:
+        query = st.text_input('Search for papers', key='search_query', placeholder='Search for papers...')
+    with col2:
+        year = st.selectbox('Publication year', options=timeframe, index=None, placeholder='Select timeframe', key='query_date')
+    st.button('Search', on_click=change_state)
+st.divider()
+
+# define logic for search
+if st.session_state.clicked_search:
+    if query:
+        st.toast('Fetching similar papers...')
+        search_result, _ = find_similar_papers(paper_title=query)      # returns a list of lists containing paper title and presentation date
+        if not search_result:
+            st.toast('No search result was found')
+        st.session_state.search_results = search_result
+        st.session_state.clicked_search = False
+
+    elif query and year:
+        year = int(year.replace(' till present',''))
+        st.toast('Fetching similar papers...')
+        search_result, _ = find_similar_papers(paper_title=query, year=year)
+        if not search_result:
+            st.toast('No search result was found')
+        st.session_state.search_results = search_result
+        st.session_state.clicked_search = False
+
+    else:
+        st.toast('Enter a search query to get started')
+        st.session_state.clicked_search = False
+
+with st.sidebar:
+    if st.session_state.search_results:
+        options = st.selectbox('View similar papers', options=st.session_state.search_results, index=None, placeholder='Select a paper')
+        if st.button('Choose paper'):
+            if options:
+                st.session_state.selected_paper = options
+            else:
+                st.toast('No paper was selected')
+    else:
+        st.selectbox('View similar papers', disabled=True, index=None, placeholder='No search results found', options=[None])
+
+if st.session_state.selected_paper:
+    paper_title = st.session_state.selected_paper.split('(Year:')[0].strip()
+    paper_year = st.session_state.selected_paper.split('(Year:')[1].replace(')', '').strip()
+    try:
+        st.session_state.summary, metadata = summarize_paper(paper_title, paper_year)
+    except RuntimeError as e:
+        st.toast('An error occurred while fetching the paper. Please try another')
+        logging.error(e)
+        st.stop()
+    for key, value in metadata.items():
+        st.write(f"**{key}** - {value}")
+    st.text('')
+    st.write(st.session_state.summary)

README.md

@@ -5,7 +5,7 @@ colorFrom: pink
 colorTo: green
 sdk: streamlit
 sdk_version: 1.35.0
-app_file: app.py
+app_file: Explore_papers.py
 pinned: false
 license: apache-2.0
 ---

files/2022/A Comparison of Tidal Signal Extraction and Bourdet Smoothening for Removal of Tidal Effect Induced Artifacts in Pressure Transient Analysis.txt

@@ -0,0 +1,297 @@
+----- METADATA START -----
+Title: A Comparison of Tidal Signal Extraction and Bourdet Smoothening for Removal of Tidal Effect Induced Artifacts in Pressure Transient Analysis
+Authors: David Nnamdi, Karen Ochie, Rouzbeh Moghanloo
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212009-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The results of pressure transient analysis (PTA) are very important in reservoir characterization; however, this analysis can be affected by some non-reservoir behavior such as gas breakthrough, phase segregation in the wellbore, tidal effects, all of which can perturb the result accuracy. When data is acquired for PTA offshore, it can contain tidal effect, causing noise which can lead to misinterpretation when the test is analyzed, hence its impact should be accounted for in the analysis. Tides are experienced as the rise and fall of sea levels due to the variation in the earth's gravitational potential exerted by the moon and the sun, and the rotation of the Earth. Tidal signals have been observed to mask late time response for pressure build up tests and will significantly hinder correct interpretation of reservoir boundaries if left unaddressed. The effects of tidal pressure signals on the pressure derivative of pressure build-up tests are studied with the aim of comprehensively exploring the deviation from expected responses given known reservoir boundary conditions. Subsequently a refined method for pure tidal component removal from pressure derivative data is presented and compared to simpler Bourdet smoothening (L) and filtration of data points used in evaluation.This work focused on an efficient method to analyze data containing tidal effects. The Bourdet derivative and log cycle filtration was effective in removing tidal signal effects on late time boundary identification with the drawback being having multiple possible interpretations of the IARF. Extracting the tidal signal gave a more defined IARF period and late time boundary effect period with only minor oscillations in the late time but the rigor of extracting the tidal signal without sufficient regional tidal information may prove to major hindrance to this process.
+
+
+
+
+Keywords:
+pressure transient testing,
+society,
+fluid dynamics,
+reservoir characterization,
+petroleum engineer,
+filtration,
+efficiency,
+upstream oil & gas,
+pressure transient analysis,
+amplitude
+
+
+Subjects: 
+Reservoir Characterization,
+Reservoir Fluid Dynamics,
+Formation Evaluation & Management,
+Pressure transient analysis
+
+
+
+
+Introduction
+
+
+Several methods exist for the determination of information on reservoir characteristics such as seismic and geological studies, logging techniques, static pressure analysis, reservoir simulation or pressure transient analysis. Pressure Transient Analysis (PTA) is an inverse solution where the pressure response is analyzed to determine other information on the reservoir such as formation permeability, connected pore volume, degree of formation damage or stimulation, wellbore storage coefficient, fracture length and so on. The result from the PTA is very important in reservoir characterization, however this analysis can be affected by some non-reservoir behavior such as gas breakthrough, phase segregation in the wellbore, tidal effects, and so on, which can perturb the result accuracy. Long durations of some of these phenomena in the wellbore can also result in the reservoir being affected (Adrian, Chaco, Moreno, & UNICAMP, June 2016). The impact of these non-reservoir effects should be accounted for when interpreting well tests to ensure a representative well test analysis (Shchurenko, et al., 2018).
+
+
+Tidal effects in wells have been observed since AD77 and became more popular with the development of pressure gauges first in water wells and then petroleum reservoirs (Hailstone & Sasol, 2018). Tides are experienced as the rise and fall of sea levels due to the variation in the earth's gravitational potential exerted by the moon and the sun and the rotation of the Earth. While the sea level is rising in some location, it is falling in others and this causes a pressure loading and unloading which would be eventually transmitted to rocks below the seabed (El Faidouzi & ADMA-OPCO, 2017). Tidal effects are found as ocean tides offshore (Hemala & Balnaves, 1986) or as earth and barometric tides onshore (Hailstone & Sasol, 2018). The tide effects have distinct phases, frequencies, and amplitudes behaviors, however, in offshore wells, amplitude is the preeminent tide effect (Wu, Ling, & Liu, 2013). Tides can be classified into three types depending on their time periods and these are distributed around the world as shown in Figure 1. Semi diurnal tides have amplitudes with two highs and two lows during a day, diurnal tides have one high and one low in one day, and mixed tides have two highs and two lows in one day like the semi diurnal tides, but the tides have different heights (Gowtham T., Rouzbeh, Vamsi, & Srikanth, 2016). Ocean tides causes pressure variation that would be eventually felt by the reservoir with a time shift and attenuation and in turn affect the pore pressure and pore volume. The tidal efficiency which is directly proportional to the formation compressibility/total compressibility ratio is measured by the ratio of the change in pore pressure in the reservoir to the pressure change at the seabed, hence tidal signals would affect the oil build-up more in relation to gas wells with efficiencies as low as 10% in comparison to oil – as high as 40%. In high permeability formations, tidal effect can severely distort the pressure derivative in pressure transient analysis. The effect of ocean tides is generally deduced when the oscillation in the pressure derivative is present for times more than a few hours and this is more visible when the test duration is larger than the period of the tides that is close to 12 hours. Distortion of the pressure derivative caused by tidal effects may mask important reservoir information such as boundary conditions and should be removed before interpretation. The presence of boundaries in reservoir affects the well test data in very subtle way, hence, it can be affected by tidal signals.
+
+
+Figure 1View largeDownload slideTide Classification Around the World (beltoforion.de, 2021)Figure 1View largeDownload slideTide Classification Around the World (beltoforion.de, 2021) Close modal
+
+
+Several authors have proposed techniques for filtering and eliminating tidal effects from PTA data. (Levitan & Vinh, 2003) used a reference tidal signal from sea floor pressure or tidal potential function to minimize the amplitude and separate the tidal signal from real PTA data. (Chang & Firoozabadi, 2000); (Araujo, Campos, & and Moreno, 2012) used Fast Fourier Transforms (FFT) to analyze the pressure data in a frequency domain, hence, easily indicating the tidal component frequency. (Zhao & Reynolds, 2009) regression from already known tide components for estimation and this was a better approach than the Fast Fourier Transforms because it handles the tidal effect in wholesome rather than in partiality. The advantage of this technique is majorly that it is an inconsequential calculation, however, the downside is that there is a possibility of the entire tidal component not being filtered hence some harmonic component would be left in the provided pressure data.
+
+
+The effects of tidal pressure signals on the pressure derivative of pressure build-up tests are studied with the aim of comprehensively exploring the deviation from expected responses given known reservoir boundary conditions. Subsequently a refined method for pure tidal component removal from pressure derivative data is presented and compared to simpler Bourdet smoothening and filtration of data points used in evaluation. This work focuses on an efficient method to analyze data containing tidal effects.
+
+
+Methodology
+
+
+Tial Signal Simulation
+
+
+To generate the pure tidal signal at surface, a slightly modified version of the equation listed in (Gowtham T., Rouzbeh, Vamsi, & Srikanth, 2016) paper is utilized. This equation is shown below:
+
+
+TDsbp=Σi=1nAi*Cos(2*t*180Ti)(1)
+
+
+TDrp=Rt*TDsbp(2)
+
+
+Where:
+
+
+TDsbp = Pure tidal signal at seabed (psi)
+
+
+Ai = Amplitude of tidal signal (in psi)
+
+
+ t = Time (hrs)
+
+
+Ti =Harmonic period (hrs)
+
+
+TDrp = Pure tidal signal at reservoir depth (psi)
+
+
+Rt = Tidal efficiency (fraction)
+
+
+Equation 1 suggests there can be several tidal components, each having its unique harmonic period and amplitude that can result in the tidal signal observed. A list of the major global tidal components and their harmonic periods (Schwiderski, 1980)are listed below:
+
+
+Table 1Major global tidal components and their harmonic periods   View Large
+
+
+Tidal efficiency
+
+
+The tidal efficiency Rt is expressed at the ratio of change in loading pressure (pressure from overlaying sea water) to the change in the stress of the solid rock and change in pore pressure (Faidouzi, 2017). Rt is largely affected by porosity, formation and fluid compressibility and several methods for its estimation have been explored by authors such as (Van Der Kamp & Gale, 1983) and (Dean, Hardy, & Eltvik, 1994), and they may require lab experiments to be carried out on core samples to measure bulk compressibility under uniaxial and hydrostatic strain condition.
+
+
+Tidal Extraction Algorithm
+
+
+The process of extracting the pure tidal oscillatory components from the actual build-up pressure response is a 2-phase process. One of the methods presented here is an adaptation of equations put forward by (Acuna, 2016) Phase 1: Matching the linear pressure derivative late time signal.
+
+
+Using the measured shut-in bottomhole pressures and time, calculate the pressure change Δp and log pressure derivative Δp′ with zero smoothening. If Bourdet smoothening algorithm is used to calculate the log derivative, set lag time (L) = 0.Calculate the derivative of the pressure derivative in tidal region dpdt using:dpdt=Δp′torΔp′t+c(3)Where c is a vertical shift factor used to set the horizontal axis of symmetry of the tidal derivative to zero (Acuna, 2016) and may be positive or negative. Note that dpdt contains both the pressure and tidal response.Using informed Ti guesses based on analysis of the plot of dpdt vs t (explained further in  appendix 1), and estimates of Ai and Rm calculate the tidal signal match TDm with the following equation: TDm=Rm*Σi=1nAi*Cos(2*(t+s)*180Ti)(4)Where:Rm = the matched tidal efficiency (an arbitrary number with no real meaning)s = the phase shift of tidal signal (hrs)The values for Rm, Ai, s and c (if necessary) can be obtained using a simple least square regression optimization routine. This is available in excel as the "Solver" add-in and works by minimizing an objective function which is the difference Root Square Error (RSE) of data points in the late time region of the build-up test as this is most affected by tidal signal and a pure oscillatory component may be observed. RSE is calculated between dpdt and TDm.Calculate:Δp′corr=Δp′−(TDm*t)(5)This is the corrected log pressure derivative signal after removal of pure tidal signal.
+
+
+Phase 2: Matching and extracting the pure tidal signal.
+
+
+Using eqn (1) and (2), compute the pure tidal signal at reservoir depth TDrp, using the regressed values of Ai and the guessed Ti and an arbitrary guess of RtCalculate the corrected bottomhole pressure Pwscorr = Pws − TDrpCalculate the corrected pressure change Δpcorr and derivative Δp′corrCreate a new objective function (RSE) between Δp′corr and Δp′ and use Solver to determine RtThe resulting corrected bottomhole pressure, pressure change and pressure derivative (Pwscorr, Δpcorr & Δp′corr) can be plotted against original data to evaluate efficiency of tidal removal.
+
+
+The algorithm for tidal signal removal allows for adequate matching of the pressure derivative response before extracting the pure tidal signal. Visual aids such as linear plots of dpdt and TDm against time will help to qualitatively evaluate how effectively the pressure derivative response has been matched.
+
+
+Case Study
+
+
+To demonstrate the effects of tidal signals on late time response, pressure response was simulated for a highly permeable reservoir in the offshore Niger-Delta region of Nigeria given a known production rate and shut-in history. The choice of location is due to a refined knowledge of the regional tidal oscillations in Nigeria defined as semidiurnal with two inequalities with tidal range varying between 1m – 3m (Awosika & Folorunsho, 2000). For this study, the Principal Lunar and Principal Solar semidiurnal (M2 & S2) tidal components with were used. The amplitudes used to generate the tidal signal were 2.45psi and 0.5psi for M2 and S2 tidal components respectively and they reflect the pressure changes that may be observed at seabed due to changes in water level (water gradient assumed to be 0.45psi/ft).
+
+
+In generating the pure tidal signal for this study, Rt is assumed to be 0.25 (i.e., 25% of the seabed tidal signal is observed at the reservoir).
+
+
+The generated pure tidal signal at reservoir depth is shown in Figure 2 below.
+
+
+Figure 2View largeDownload slideGenerated pure tidal signal at reservoir depth.Figure 2View largeDownload slideGenerated pure tidal signal at reservoir depth. Close modal
+
+
+Reservoir pressure response simulation
+
+
+Kappa Saphir test design module was used to generate drawdown and shut-in pressures for a given rate history for 4 different boundary conditions. The boundary conditions are:
+
+
+Infinite acting reservoirClosed boundary (circle) with reservoir radius of 1300ftConstant pressure boundary at reservoir radius of 1300ftChannel with each fault lying 650ft from the wellbore.
+
+
+The reservoir fluid properties and flow history used for the simulation are listed in Table 2 below.
+
+
+Table 2Reservoir Parameters   View Large
+
+
+In all four cases, the well is designed to produce for 150hrs at 3000stb/d after which it is shut in for 72hrs. The gauge resolution is set to 0.02psi, the standard for quartz pressure gauges and a sampling frequency of 5seconds to 15mins were used in the early and late time periods of the test, respectively. The simulated tidal signal was used to adjust the Kappa shut-in response and a plot of pressure change and pressure derivative for all four boundaries is listed in Figure 3 a-d:
+
+
+Figure 3View largeDownload slideImpact of tidal signal noise on the pressure derivative plotFigure 3View largeDownload slideImpact of tidal signal noise on the pressure derivative plot Close modal
+
+
+Analysis of Figure 3 shows the impact of tidal signal noise on the pressure derivative plot. For all four boundary types, the data is distorted to show a unit slope line in the late time region which at the early stages may be incorrectly misinterpreted to be the onset of a channel boundary or intersection of 2 wedged sealing faults. In this study, we assume the data corrupted by tidal noise to be our measured data and based on the methodologies outlined earlier, attempt to remove the tidal component to correctly interpret reservoir boundary. Two boundary types were focused on for the rest of the study; (1) Closed boundary and (2) Channel boundary.
+
+
+Results
+
+
+Case 1: Closed boundary tidal component extraction
+
+
+An initial match is done to find the optimal match parameters 1. This is followed by a match on the Rt to extract the true tidal signal as described earlier. Table 3 a–d lists the initial and final guesses for the phase shift, matched tidal efficiency, M2 and S2 amplitudes of the phase signal, and tidal efficiency. The harmonic periods are assumed to be known; hence original values are used for matching.
+
+
+Table 3 a–dMatched parameters before and after the LSR optimization   View Large
+
+
+Analysis of Table 3 above shows that matched amplitudes and tidal efficiency differ from the parameters used to generate the pure tidal signal that was used to "corrupt" data initially. However, a very good match is obtained as shown in Figure 4. This indicates that the tidal signal solution is non-unique and different combinations of amplitudes and tidal efficiencies can give the same oscillatory pressure response observed in the reservoir. In the tidal signal matching/correction plot shown in Figure 4, pre- and post-match curves are shown for the pressure derivative, modeled signal and true tidal signal extracted.
+
+
+Figure 4View largeDownload slideTidal signal matching/correction plotFigure 4View largeDownload slideTidal signal matching/correction plot Close modal
+
+
+The log-log plot of the resulting Δp and Δp′ of the of the tidal signal dominated pressure vs corrected pressure signal is shown in Figure 5 below:
+
+
+Figure 5View largeDownload slideResulting plot of the of the tidal signal dominated pressure vs corrected pressure signalFigure 5View largeDownload slideResulting plot of the of the tidal signal dominated pressure vs corrected pressure signal Close modal
+
+
+Some noise is still observed in the late period as there are still oscillating pressure components not fully extracted, however, a clear dip in the pressure derivative for the corrected data is observed and can be effectively used to interpret a closed boundary system and its distance from the wellbore.
+
+
+Case 2: Channel Boundary system
+
+
+Here, separate initial guesses were made for the match parameters; (1) The guessed amplitude for the S2 tidal component was much larger than the M2 and (2) The guessed amplitude for the M2 and S2 signal were equal and smaller numbers. This was done to evaluate the influence of initial guesses on the final optimization solution. The matched parameters before and after the LSR optimization is shown in Table 4 a-h.
+
+
+Table 4Matched parameters before and after the LSR optimization   View Large
+
+
+Results shown in Table 4 suggests that the use of initially high estimates of the amplitudes can result in an unrealistic matched value after the application of the LSR optimization algorithm. The algorithm tries to offset these high amplitude numbers by using very small Rm and Rt values for the final match. The quality of the match in both scenarios are however comparable but still different from the parameters used to initially create the signal, again indicating the non-uniqueness of the solution. This is shown in Figure 6.
+
+
+Figure 6View largeDownload slideTidal signal matching/correction plotFigure 6View largeDownload slideTidal signal matching/correction plot Close modal
+
+
+A log-log plot of the resulting Δp and Δp′ of the of the tidal signal dominated pressure vs corrected pressure signal is shown in Figure 7 Again, some noise is observed at the very late time, but the channel boundary one-half slope line is clearly defined and can be interpreted successfully.
+
+
+Figure 7View largeDownload slideResulting plot of the of the tidal signal dominated pressure vs corrected pressure signalFigure 7View largeDownload slideResulting plot of the of the tidal signal dominated pressure vs corrected pressure signal Close modal
+
+
+Bourdet smoothening and log cycle filtration as an alternative to tidal signal removal.
+
+
+(Bourdet, Ayoub, & Pirard, 1989) defined a smoothening algorithm to calculate the pressure derivate function (Hosseinpour-Zonoozi, Ilk, & Blasingame, 2006). The algorithm is defined as follows:
+
+
+Δp'=(Δp1ΔX1ΔX2+Δp2ΔX2ΔX1)ΔX1+ΔX2
+
+
+Where Δp′ is the pressure derivative, Δp is the pressure change and ΔX is the log cycle lag time and can otherwise be defined as L. A representation of the Bourdet derivative is shown in Figure 8 below:
+
+
+Figure 8View largeDownload slideVariable's definition (Fekete.com, Conventional Analysis (Flow/Buildup or Injection/Falloff) > Derivative Analysis, n.d.)Figure 8View largeDownload slideVariable's definition (Fekete.com, Conventional Analysis (Flow/Buildup or Injection/Falloff) > Derivative Analysis, n.d.) Close modal
+
+
+In all the steps described for removing tidal signal in the previous subsection, L was assumed to be Zero (0) such that no smoothening of the data was done in the calculation of the pressure derivative. However, if an L value of 0.1-0.2 is chosen, significant improvements in the pressure derivative for the pressure data with tidal signal can be seen. In this case presented, the effect of smoothening is comparable to that of tidal signal removal as the boundary can be clearly interpreted. The effect of the tidal signal now only visibly affects the selection of the infinite acting radial flow (IARF) period with minor errors.
+
+
+Log cycle filtration describes a process of reducing the number of data points for analysis for any given log cycle. The advantage of doing this is further filtration of the data points that come from pressure gauges with high sampling frequency (such as quartz gauges) and can significantly reduce noise from tidal effects. Log-log plots of pressure change and pressure derivative for the tidal signal corrupted pressure data for the channel boundary is shown in in Figure 9. In 9a, the data is smoothened by L = 0.2 and in 9b the smoothened data is also filtered to 10 data points per log cycle. The filtration is done in Kappa Saphir.
+
+
+Figure 9View largeDownload slideSmoothening and Log cycle filtration for channel boundaryFigure 9View largeDownload slideSmoothening and Log cycle filtration for channel boundary Close modal
+
+
+Discussion and Recommendations
+
+
+Based on the study carried out, tidal signals have been observed to mask late time response for pressure build up tests and will significantly hinder correct interpretation of reservoir boundaries if left unaddressed. Two methods presented for removal of tidal oscillation effects are: (1) extraction of the underlying pure tidal signal from the measured pressure data and (2) using Bourdet smoothening and log cycle filtration to reduce oscillations shown in derivative plot.
+
+
+In extracting the tidal signal, selection of the harmonic period (Ti) to use is crucial to obtaining a suitable match and while this variable can be included in the LSR optimization routine, it is highly discouraged. This is because different regions globally have different tidal signals with known harmonic periods and varying amplitudes. It is suggested that the transient data interpreter studies the oscillatory signals of the late time data using a linear plot of dpdt vs time and compare them to known oscillatory signals for different harmonic periods before selecting suitable ones for a match.  Appendix A shows a plot of different pure tidal signals at different harmonic periods for such comparison.
+
+
+It is recommended that in running the LSR optimization, certain constraints (such as Rm ≤ 1) be included. This will aid in obtaining a better match. If the Bourdet derivative is used, the suggested log cycle lag time should not exceed 0.3 as crucial reservoir boundary information may be masked.
+
+
+An alternative method for tidal signal removal is the Fast Fourier Transform (FFT), a version of the Discrete Fourier Transform that is more computationally efficient. FFT transforms data from time domain to frequency domain and the tidal signal frequencies and their amplitudes can be visibly observed on a plot of amplitude vs frequency and facilitates easy extraction. This has been described by Wu et. al (2013). In their study, they observed that to filter out tidal signal, the frequency resolution (inverse of time of shut-in) must be 0.002 hour-1 or less indicating a shut in-time of at least 500hrs which is impracticable. Wu et al proposed a solution to this using a zero-padding technique which essentially adds zero amplitude samples to the data available before implementing FFT.
+
+
+In our study detailed earlier, the buildup is simulated for 72 hrs we found it superfluous to implement the zero-padding technique rather than utilizing the regression algorithm since the aim of the study was to comparatively evaluate Tidal signal extraction vs Bourdet smoothening for late time boundary interpretation.
+
+
+Conclusion
+
+
+This paper illustrates the effectiveness of Bourdet derivative and log cycle filtration in removing tidal signal effects on late time boundary identification with the drawback being having multiple possible interpretations of the IARF which gives the reservoir transmissibility information that is crucial to reservoir characterization. Extracting the tidal signal gives a more defined IARF period and late time boundary effect period with only minor oscillations in the late time but the rigor of extracting the tidal signal without sufficient regional tidal information may prove to major hindrance to that process.
+
+
+The combination of tidal signal extraction, Bourdet smoothening factor and log cycle filtration may yield the best dataset for obtaining quality reservoir information from a buildup test pressure data that is be-ladend with tidal noise, but this may not be possible in all cases. A trade off must then be made between both methods if all the required information for tidal signal extraction is not readily available.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgement
+
+
+The Carbon Utilization and Storage Partnership (CUSP) at the University of Oklahoma is immensely acknowledged for facilitating the publication of this paper.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion TDsbp= Pure tidal signal at seabed (psi) Ai= Amplitude of tidal signal (in psi) t= Time (hrs) Ti=Harmonic period (hrs) TDrp= Pure tidal signal at reservoir depth (psi) Rt= Tidal efficiency (fraction) Rm= the matched tidal efficiency (an arbitrary number with no real meaning) s= the phase shift of tidal signal (hrs)
+
+
+Appendix
+
+
+In figure A below, pure tidal signal plots for a few known harmonic periods are shown for use in comparing with linear plots of dpdt vs time. These plots should aid pressure transient data interpreter quickly identify common diurnal and semidiurnal tidal signals.
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+References
+
+
+Acuna, J. A. (2016). A Simple Method for the Removal of Tidal Effects in Pressure Transient Analysis. SPE Western Regional Meeting. Anchorage, Alaska, USA: Society of Petroleum Engineers. doi:https://doi.org/10.2118/180368-MSGoogle ScholarCrossrefSearch ADS  Adrian, P. M., C. Y., Moreno, R. B., & UNICAMP. (June2016). Second Semilog Pressure Derivative in Pressure Transient Analysis of Gas-Condensate Wells with Strong Phase Redistribution: Field Case Study. SPE Trinidad and Tobago Section Energy Resources Conference (pp. 1–13). Port of Spain, Trinidad and Tobago: Society of Petroleum Engineers. doi: 10.2118/180771-MSGoogle ScholarCrossrefSearch ADS  Araujo, M., Campos, W., & and Moreno, R. (2012). iltering of Tide Effects in Formation Evaluation Data. SPE Latin America and Caribbean Petroleum Engineering Conference. Mexico City, Mexico: Society of Petroleum Engineers. doi: 10.2118/153566-MSGoogle Scholar Awosika, L., & Folorunsho, R. (2000). Nigeria. The Ocean Data and Information Network of Africa, 127–133. Retrieved from http://fust.iode.org/sites/fust.iode.org/files/public/images/odinafrica/Chapter_7_14_Nigeria.pdfGoogle Scholar beltoforion.de. (2021, 264). Diurnal Tides and Semiduurnal Tides. Retrieved from beltoforion.de: https://beltoforion.de/en/tides/tidal_cycles.phpBourdet, D., Ayoub, J., & Pirard, Y. (1989). Use of Pressure Derivative in Well-Test Interpretation. SPE Formation Evaluation, 293–303. Retrieved from https://blasingame.engr.tamu.edu/z_zCourse_Archive/P648_19A/P648_19A_Reading_Portfolio/SPE_012777_(Bourdet)_Pressure_Derivative_for_PTA_(OCR)_(pdf).pdfGoogle Scholar Faidouzi, M. M. (2017). Contribution of Tidal Analysis to Reservoir Monitoring - Field Case Study in a Fractured Reservoir Offshore Abu Dhabi. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi, UAE: Society of Petroleum Engineers. doi:https://doi.org/10.2118/188837-MSGoogle ScholarCrossrefSearch ADS  Fekete.com, Conventional Analysis (Flow/Buildup or Injection/Falloff) > Derivative Analysis. (n.d.). Retrieved from Fekete: http://www.fekete.com/san/theoryandequations/welltesttheoryequations/derivative_analysis.htmGowtham, T., Rouzbeh, M. G., Vamsi, K. B., & Srikanth, P. (2016). Possible Misinterpretations in Well Test Analysis Due to Unfiltered Tidal Signal. SPE Western Regional Meeting. Anchorage, Alaska, USA: Society of Petroleum Engineers. doi: https://doi.org/10.2118/180464-MSGoogle ScholarCrossrefSearch ADS  Hailstone, J., & Sasol, I. E. (2018). Systematic Use of Tidal Effects for Reservoir Appraisal and Well Integrity Monitoring in a Near-Coast Onshore Environment. SPE Europec featured at 80th EAGE Conference and Exhibition. Copenhagen, Denmark: Society of Petroleum Engineers. doi: 10.2118/191354-MSGoogle ScholarCrossrefSearch ADS  Hemala, M. L., & Balnaves, C. (1986). Tidal Effect in Petroleum Well Testing. SPE Offshore South East Asia Conference and Exhibition. Singapore: Society of Petroleum Engineers. doi: 10.2118/14607-MSGoogle ScholarCrossrefSearch ADS  Chang, E., & Firoozabadi, A. (2000). Gravitational Potential Variations of the Sun and Moon for Estimation of Reservoir. Journal of Petroleum Technology, 5(4), 456–465. doi: 10.2118/67952-PA.Google Scholar Dean, G., Hardy, R., & Eltvik, P. (1994). Monitoring compaction and compressibility changes in offshore chalk reservoir. SPE Formation, 9(1), 73–76. doi: https://doi.org/10.2118/23142-PAGoogle ScholarCrossrefSearch ADS  El Faidouzi, M. M., & ADMA-OPCO. (2017). Contribution of Tidal Analysis to Reservoir Monitoring - Field Case Study in a Fractured Reservoir Offshore Abu Dhabi. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi, UAE: Society of Petroleum Engineers. doi: https://doi.org/10.2118/188837-MSGoogle ScholarCrossrefSearch ADS  Hosseinpour-Zonoozi, N., Ilk, D., & Blasingame, T. A. (2006). The Pressure Derivative Revisited — Improved Formulations and Applications. SPE Annual Technical Conference and Exhibition. San Antonio, Texas, U.S.A: Society of Petroleum Engineers. Retrieved from https://blasingame.engr.tamu.edu/0_TAB_Public/TAB_Publications/SPE_103204_(Zonoozi)_Pressure_Derivative_Revisited.pdfGoogle ScholarCrossrefSearch ADS  Levitan, M. M., & Vinh, P. (2003). Identification of Tidal Signal in Well Test Pressure Data. SPE Annual Technical Conference and Exhibition. Denver, Colorado: Society of Petroleum Engineers. doi: https://doi.org/10.2118/84376-MSGoogle ScholarCrossrefSearch ADS  Schwiderski, E. W. (1980). Ocean tides, part I: Global ocean tidal equations. Marine Geodesy, 3(1-4), 161–217. doi: 10.1080/01490418009387997Google ScholarCrossrefSearch ADS  Shchurenko, A., Arbatskii, T., Dadakin, N., Rymarenko, K., Nukhaev, M., & Musin, R. (2018). Features of the Well Test Interpretation in Complicated Conditions of Intensive Segregation of Phases in the Wellbore and the Manifestation of the Effects of Abnormal Pressure Growth. SPE Russian Petroleum Technology Conference (pp. 1–24). Russia, Moscow: Society of Petroleum Engineers. doi: 10.2118/191561-18RPTC-MSGoogle Scholar Van Der Kamp, G., & Gale, J. E. (1983). Theory of earth tide and barometric effects in porous formations with compressible grains. Advancing Earth and Space Science, 19(2), 538–544. doi: https://doi.org/10.1029/WR019i002p00538Google Scholar Wu, X., Ling, K., & Liu, D. (2013). Deepwater Reservoir Characterisation Using Tidal Signal Extracted from Permanent Downhole Pressure Gauge. International Petroleum Technology Conference. Beijing, China: Society of Petroleum Engineers. doi: 10.2523/IPTC-16711-MSGoogle ScholarCrossrefSearch ADS  Zhao, Y., & Reynolds, A. (2009). Estimation and Removal of Tidal Effects from Pressure Data. Journal of Petroleum Technology, 14(1), 144–152. doi: 10.2118/103253-PA.Google Scholar 
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212009-MS
+
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+

files/2022/A Finite Element Study of Integrity of FormationCasing Annulus Cement Sheath in Niger Delta.txt

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+----- METADATA START -----
+Title: A Finite Element Study of Integrity of Formation/Casing Annulus Cement Sheath in Niger Delta
+Authors: Jessica Etoh, Adewale Dosunmu, Boniface Oriji, Oloruntoba Moritiwon
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211946-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Cement sheath is a well barrier that prevents the unintentional and uncontrollable flow of fluids from, into a formation or back to its surface. However, during drilling and production operations, this cement is subjected to various stresses resulting from thermal stress, non-uniform geo-stress, compressive and tensile stresses. Therefore, this study describes a finite element analysis (FEA) simulation of a cement sheath of class G type under stress in a typical drilling and production scenario. With experiments, the rheological and mechanical properties of class G cement with varying water-cement ratio of 0.4, 0.5 and 0.6 were prepared and analyzed for their performance and workability. From the results, it showed that the cement system with the lowest water-cement ratio of 0.4, demonstrated the highest mechanical strength. This was attributed to lesser water in the mix triggering efficient interaction with cement. Hence, based on this study, 0.4 cement ratio is recommended if the ability to withstand compressive and tensile forces is desired. In cases where it is to be used for drilling and production operations characterised by fatigue and cyclic forces, its composition should be designed such that it is more ductile and flexible. An FEA software, ANSYS Mechanical APDL (ANSYS Parametric Design Language) was used to analyze stress to convergence. Material properties of 0.4 cement ratio was adopted for simulation based on experimental results. Also, well loading conditions were cycled at temperature and pressure of 0-104 0C and 250-290 bar simultaneously. Simulation results showed the time changes of equivalent (Von-Mises), maximum, minimum and shear stresses. Time changes of equivalent elastic strain, total deformation and stress intensity were also recorded. From the simulation results, it can be concluded that the yield point of the material occurred at a time (t) =1.3245×10−4 s under continuous stress. It is recommended that the contact point between the casing and the cement be monitored for deformation due to high stress response during stress analysis.
+
+
+
+
+Keywords:
+upstream oil & gas,
+wellbore design,
+cement,
+sheath,
+cement chemistry,
+cement property,
+deformation,
+petroleum engineer,
+cement formulation,
+society
+
+
+Subjects: 
+Wellbore Design,
+Casing and Cementing,
+Wellbore integrity,
+Cement formulation (chemistry, properties)
+
+
+
+
+Introduction
+
+
+NORSOK D-010 explains that Well Integrity involves the use of organized and technically sound solutions to mitigate the risk of unrestrained release of formation fluids during the well's lifecycle. As the petroleum industry enters more complex and demanding environments, it is necessary that the use of a two-barrier (primary and secondary) philosophy be employed and hence standardized. There are four distinct phases of a well's lifecycle namely: Drilling, production, Intervention, Plug and Abandon. With exception to the drilling phase where the drilling mud is the only primary barrier, the cement sheath serves as a primary and secondary barrier in all phases.
+
+
+Cement sheath as a component of casing-cement-formation system
+
+
+During the drilling phase or when a well is to be plugged and abandoned, the cement slurry is placed into the annulus between the casing and open hole. When the cement slurry hardens and sets, it creates a seal such that it isolates the well flow from unwanted formation fluids while permanently positioning the casing in place. The cement sheath is a crucial element in sustaining well integrity because it provides both zonal and hydraulic isolation, provides support and protection to the casing. However, if the cement slurry is not placed properly or has poor characteristics, it could fail during the well's service life. Furthermore, operations such as drilling, completion, well stimulation, production and so on can result in cement failure.
+
+
+Researches investigating the cement's durability are classified into two major groups:
+
+
+Experimental - LaboratoryModelling methods - Finite element analysis
+
+
+These researches have shown that cement sheath's failure occurs when the applied stress on the cement is more than the yield strength of the cement.
+
+
+Finite element analysis
+
+
+It is the use of computer software to understand and predict how a structure reacts to real-world conditions such as heat, fluid flow, forces and other physical effects. It is used to locate potential problems in a design and show whether a product will break, wear out or work the way it was designed. It is used in every engineering discipline and some of the software include: ANSYS, OpenFOAM, Sim Scale, Autodesk CFD, ABAQUS, RoboLogix e.t.c.
+
+
+K.E Gray et al, 2007 performed a staged finite element analysis during the construction of a well during which he considered the stress states at the wellbore and at the end of the wellbore. The analysis was to track the behaviour of the cement slurry which is time dependent after it has been placed. ABAQUS FEA was used to investigate a casing with zero eccentricity. The different loading steps were: Drilling, Cementing, Hardening, Shrinkage, Completion, Hydraulic Fracturing and Production. They observed:
+
+
+There was no difference in the distribution of the radial stress during "cementing" and "hardening"There was no loss of bonds at the interface of the casing and cement during the shrinkageThere was debonding at the interface of the casing and cement during shrinkage of stiff material systems. Stiff material here refers to rocks that have increased values of cohesive strength and modulus of elasticity. On the other hand, to recompense for cement shrinkage in "compliant material systems" the rocks elastically and plastically deformed (i.e. low young's modulus and cohesive strength respectively.
+
+
+METHODOLOGY
+
+
+An enterprise version of CFD (Computational Fluid Dynamics) code-ANSYS 2019R3 was utilised to draw the geometry in 3D and to mesh the symmetric section of it. The meshed symmetric geometry was adopted as the computational domain. The unstructured Hexahedral/tetrahedral mesh was then introduced to the model, solution and post processing mode of Mechanical APDL (ANSYS Parametric Design Language) in ANSYS 2019R3 for stress analysis.
+
+
+The formation-cement-casing geometry was modelled as vertical cylindrical tubes with void hollows. It is made up of three sections/domains which are distinct but continuous at the interface. The cement domain was sandwiched as a frozen cylinder between two other frozen cylinders which represented a slice of the formation and the production casing. The cement domain was 36.3701 inches (0.9238 m) high while the formation and the casing were 39.3701 (1 m) and 42.3701 inches (1.0762 m) high respectively. Total diameter to include a slice of the formation was 11.5 inches (0.2921 m) while the wellbore was 9.5 inches (0.2413 m) in diameter. Production casing represented by the innermost hollow cylinder has an inner and outer diameter of 4.09 (0.1039 m) and 4.5 inches (0.1143 m) respectively. Reservoir pressure is assisted by water flooding while the API specified production capacity of casing is 1.63 bbl/100ft.
+
+
+ANSYS Design Modeler package was used to draw the 3D computational domain in accordance to model description. Afterwards, a symmetry was introduced to the geometry to cut it in half on the XY plane. This is because material properties and flow behaviour are homogenous across the diameter of a riser design but not across its height. Hence, modelling a half to represent the whole has negligible or no effect on result, reduces computational cost as well as give insight into internal and enclosed areas of the domain.
+
+
+Meshing
+
+
+ANSYS AUTODYN Pre-post was used to mesh the symmetric geometry dividing it into finite elements to uniformly support an applied load. Mesh settings include quadratic element order and disabling of adaptive strings for sizing. Also, for sizing, growth rate was 1.85 default while mesh defeaturing, curvature capture and proximity capture were enabled.
+
+
+Boundary Conditions: The geometry was subjected to pressure and temperature concurrently to simulate response in term of stress. These conditions are to accommodate seabed (40C) and severe downhole (1040C) conditions. Boundary conditions (BC) are set as shown Equations (1 to 3).
+
+
+BC for temperature over L = 1.0762 m, 0 ≤ r ≤ D: BC1: ∅i=+24 °C/load step,Ti=0°C,  (first ramp only)(1)∅f=+20 °C/load step,Tf=104°C,(2)
+
+
+BC for pressure over L = 1.0762 m, 0 ≤ r ≤ D: BC1: ∅i=∅f=10 bar/load step, Pi=250 bar, Pt=290 bar(3)
+
+
+The finite element analysis was done using Mechanical APDL. The transient formulation for time dependent solution was adopted hence, transient structural chosen over static structural mode.
+
+
+A time step of 1×10−5s chosen as the minimum while 0.5s was chosen as the maximum with the initial step set at 0.2s. The simulations were performed in a stepwise manner starting with the convergence of one load step before ramping to the other using tabular input. Solution monitors and probes were included to plot contours and charts.
+
+
+Simulations were carried out until the transient state has reached convergence at around 10s. The total computational time was around 7 days for a real-time of 10s with 1 second equivalent to 999999 steps and 793 iterations.
+
+
+RESULTS
+
+
+Equivalent Von-Mises Stress
+
+
+The Von Mises Criterion integrates the three principal stresses into an equivalent stress value and can be used to determine the failure condition on a ductile material such as Cement (Butcher et al., 2019). It is used to determine if a material would yield at any point. This is the point at which a material transforms from showing elastic behaviour to plastic behaviour. It is also known as the yield stress, a material would yield if its yield strength is less than the stresses acting on it.
+
+
+From the results on Chart A, it is observed that the Von Mises stress value increased from 4.8581×107 Pa at 6.84×10−5s to a peak of 2.2433 ×108 Pa at 1.3245×10−4s and afterwards tailed. The curve could not rebound to its previous peak value as it increased again in a sinusodial form to 1.0629×108 Pa at 2.4245×10−4s. It continued to rise constantly till it attained a new lower peak of 1.0994×108 Pa at 10s. This implied that the critical value of the elastic energy of distortion has been exceeded after the first peak, hence the loss of elasticity which is the ability to rebound. Also, the flattening of the curve up to 10s reassured that the cement is entered its plastic phase.
+
+
+Chart AView largeDownload slideEquivalent (Von-mises) stress (m) vs. time (s) before time = 10sChart AView largeDownload slideEquivalent (Von-mises) stress (m) vs. time (s) before time = 10s Close modal
+
+
+Total Deformation
+
+
+From the results on Chart B, it is observed that the total deformation value increased from 2.9760×10−4m at 6.84 ×10−5s to a peak of 6.8583×10−4m at 1.3245×10−4s and afterwards tailed. The curve could not rebound to its previous peak value as it increased again in a sinusodial form to 6.3893×10−4m at 2.4245×10−4s. It continued to rise constantly till it attained a new lower peak of 6.9376×10−4m at 10s. Plastic deformation is the process by which a material is subjected to a permanent deformation such that there is an irreversible change in its shape with respect to applied forces. The transition from the linear elastic behavior to plastic behavior is called yielding. A common deformation theory that can be referred to is the Hooke's law.
+
+
+Chart BView largeDownload slideTotal deformation (m) vs time (s) before time = 10sChart BView largeDownload slideTotal deformation (m) vs time (s) before time = 10s Close modal
+
+
+Although the maximum deformation is not helpful in predicting material failure, it gives a description of the extent to which the material deforms before failure.
+
+
+Principal Stresses
+
+
+The Principal stresses are derived from the normal stress resulting when the shear stress is zero, and is calculated at an angle Ɵ. The maximum principal stress is the maximum stress in tension while the minimum principal stress is the minimum stress in compression.
+
+
+Principal stresses can either have a negative or positive value and this depends on the load being applied. A positive value translates as the material in tension while a negative value translates as the material in compression.
+
+
+Maximum Principal Stress: From the results on Chart C, it is observed that the Maximum Principal stress value increased from 2.18×107Pa at 6.84×10−5s to a peak of 2.3299 ×108 Pa at 1.3245×10−4s and afterwards tailed. The curve could not rebound to its previous peak value as it increased again in a sinusodial form to 9.6441×107 Pa at 2.4245×10−4s. It increased constantly till it attained a new lower peak of 9.9419×107 Pa at 10s. The theory of maximum stress explains that a material would fail when the maximum value of principle stress generated in the material becomes greater than the limiting value of stress. This theory is also referred to as the Rankine's theory. Chart CView largeDownload slideMaximum principal stress (Pa) vs time (s) before time = 10sChart CView largeDownload slideMaximum principal stress (Pa) vs time (s) before time = 10sFrom the stress strain curve in Fig G, it is seen that at point B (Yield stress point), the material does not exhibit elastic behavior (linear section of curve) anymore, intead it has entered the phase of plasticity in which it cannot return back to its original shape even after the external load being applied to it has been removed. From Chart C, this peak/yield point occurred at a tensile stress of 2.3299 ×108 Pa. Also, the flattening of the curve up to 10s reassured that the cement is entered its plastic phase. Figure GView largeDownload slideTypical stress-strain curveFigure GView largeDownload slideTypical stress-strain curveMinimum Principal Stress: From the results on Chart D, it is observed that the Minimum Principal Stress value increased from a negative value of - 2.2758×107 Pa at 6.84×10−5s to a peak of 1.4376 ×107 Pa at 1.3245×10−4s and afterwards tailed. The curve rebounded and exceeded its previous peak value as it increased again in a sinusodial form to 2.2430×107 Pa at 2.4245×10−4s. It increased constantly till it attained a new higher peak of 2.6172×107 Pa at 10s. This higher value of stress as compared to the stress at the yield point, could indicate an Ultimate Stress Point, after which comes the break point. The negative values indicate that the material is under compression. Chart DView largeDownload slideMinimum principal stress (Pa) vs. Time (s) before time = 10sChart DView largeDownload slideMinimum principal stress (Pa) vs. Time (s) before time = 10sThis implied that the critical value of the elastic energy of distortion has been exceeded after the first peak, hence the loss of elasticity which is the ability to rebound. Also, the flattening of the curve up to 10s reassured that the cement is entered its plastic phase.
+
+
+Figure AView largeDownload slideModel descriptionFigure AView largeDownload slideModel description Close modal
+
+
+Figure (B)View largeDownload slideFull geometryFigure (B)View largeDownload slideFull geometry Close modal
+
+
+Figure (C)View largeDownload slideGeometry with symmetry on the XY planeFigure (C)View largeDownload slideGeometry with symmetry on the XY plane Close modal
+
+
+Figure DView largeDownload slideMeshing interface showing meshed symmetric geometryFigure DView largeDownload slideMeshing interface showing meshed symmetric geometry Close modal
+
+
+Figure EView largeDownload slideEquivalent elastic strain (m/m) -symmetric viewFigure EView largeDownload slideEquivalent elastic strain (m/m) -symmetric view Close modal
+
+
+Figure FView largeDownload slideEquivalent (Von-mises) stress (m/m)-symmetric viewFigure FView largeDownload slideEquivalent (Von-mises) stress (m/m)-symmetric view Close modal
+
+
+Shear Stress
+
+
+The shear strength of the cement is a depiction of its shear resisting capacity. Shear stress is a force that causes material deformation by slippage along a plane. From the results on Chart E, it is observed that the Shear Stress value increased from a value of 1.4732×107 Pa at 6.84×10−5s to a peak of 6.0448 ×107 Pa at 1.3245×10−4 s and afterwards tailed. The curve could not rebound to its previous peak value as it increased again in a sinusodial form to 4.004×107 Pa at 2.4245×10−4 s. It increased constantly till it attained a new lower peak of 3.6632×107 Pa at 10s.
+
+
+Chart EView largeDownload slideShear stress-XY plane (m) vs. Time (s) before time = 10sChart EView largeDownload slideShear stress-XY plane (m) vs. Time (s) before time = 10s Close modal
+
+
+CONCLUSION
+
+
+From the FEA simulation using ANSYS, taking a closer look at all properties measured, it was observed from the properties that the material exhibited a linear behaviour from time (t) = 0s until a peak of time (t) = 1.3245×10−4s. After which their curves showed a sinusoidal form. It can be interpreted that the yield point for the cement occurred at a time (t) = 1.3245×10−4s.
+
+
+APPLICATIONS IN THE OIL AND GAS INDUSTRY
+
+
+The cement slurry used for this thesis was designed after an unnamed well in OML 130 in the Niger Delta. This thesis has demonstrated the importance of Finite Element Analysis of cement sheath in order to ensure its integrity of the lifetime of the well. Based on this work, the following steps can be adopted in the industry to monitor and ensure the cement sheath's integrity during the various phases of the well:
+
+
+During drilling
+
+
+During the design of the cement slurry, it should be subjected to laboratory testing in order to obtain its mechanical and rheology properties. The results of the mechanical tests would enable the engineer to design the slurry such that it withstands tensile, compressive and cyclic loads generated during drilling operations. The results of the rheology tests would confirm the workability of the cement such that it is able to be pumped down-hole into the annulus between the casing and formation without losing its integrity, and after setting it develops sufficient strength to withstand any external stress it is subjected to.
+
+
+During production
+
+
+Down-hole gauges in the well can be used to periodically obtain the down-hole temperature and pressure during production operations. Along with results of the mechanical properties obtained from tests during the drilling phase, these parameters can be fed into a Finite Element Analysis tool of choice to determine the stress and deformation generated in the cement sheath. As seen in this thesis, the point of yield (Von Mises Stress), fracture cracking (stress intensity factor) and extent of deformation (Total deformation) can be obtained from FEA simulation. By doing this the engineer would have critical values of temperature and pressure that must not be exceeded during production activities such that the integrity of the cement sheath is not damaged. This process can be repeated annually so as to obtain a trend of stress and deformation in the cement sheath. This trend can serve as an offset data if a new well is to be drilled in the same or nearby field.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+BoisA., GarnierA., GaldioloG., LaudetJ. (2012). Use of a mechanistic model to forecast Cement-Sheath Integrity. Society of Petroleum Engineers. SPE-139668-MS.Google ScholarCrossrefSearch ADS  GrayK. E, PodnosE., BeckerE., (2007). Finite element Studies of the Near-Wellbore Region during Cementing Operations: Part 1. Society of Petroleum Engineers: 127–136, SPE-106998-PA, https://doi.org/10.2118/106998-PAGoogle ScholarCrossrefSearch ADS  McDanielJ, WattersL., ShadravanA. (2014). Cement Sheath Durability: Increasing Cement Sheath Integrity to Reduce Gas Migration in the Marcellus Shale Play. Society of Petroleum Engineers. SPE-168650-MS. https://doi.org/10.2118/168650-MS.Google ScholarCrossrefSearch ADS  Al-AshbabJ., AfzalM., EmenikeC.O. (2004). Well Integrity Management System. Society of Petroleum Engineers. Paper Number: SPE-88696-MS. https://doi.org/10.2118/88696-MSGoogle Scholar ShenZ., BeckF.E., LingK. (2014). The mechanism of wellbore weakening in worn casing-cement-formation system. Journal of Petroleum Engineering. DOI:10.1155/2014/126167Google Scholar ArjomandE., BennettT., NguyenG. (2018) Evaluation of cement sheath integrity subject to enhanced pressure. Journal of Petroleum Science and Engineering. DOI:10.1016/j.petrol.2018.06.013Google Scholar De AndradeJ., TorsæterM., TodorovicJ., OpedalN., StroiszA., VrålstadT. (2014) Influence of Casing Centralization on Cement Sheath Integrity during Thermal Cycling. SPE-168012-MS, https://doi.org/10.2118/168012-MSJ. OEtoh & A.Dosunmu (2021) A Comparative Study of the Rheological Properties of Class G Cement Sheath in Niger Delta. International Journal of Engineering Science Invention (IJESI), 15–22, Volume 10 Issue 10Series II. www.ijesi.orgGoogle Scholar J. OEtoh & A.Dosunmu (2021) An Experimental Study of Mechanical Properties of Class G Cement Sheath in the Niger Delta. International Journal of Engineering Science Invention (IJESI), 23–29, Volume 10 Issue 10Series II. www.ijesi.orgGoogle Scholar NORSOK Standard D-010 Rev.3, August2004De AndradeJ., SangeslandS., TodorovicJ., VralstadT.. (2015). Cement Sheath Integrity during Thermal Cycling: A Novel Approach for Experimental Tests of Cement Systems. Society of Petroleum Engineers. SPE-173871-MS, https://doi.org/10.2118/173871-MSGoogle ScholarCrossrefSearch ADS  KhalifehM. & SaasenA. (2020). General Principles of Well Barriers. Introduction to Permanent Plug and Abandonment of Wells. pp 11–69https://link.springer.com/book/10.1007/978-3-030-39970-2.Google Scholar ShadravanA., SchubertJ, AmaniM, TeodoriuC. (2015) Using Fatigue-Failure envelope for Cement-Sheath-Integrity Evaluation. Society of Petroleum Engineers (SPE), 68–75., SPE-168321-PA, https://doi.org/10.2118/168321-PAGoogle ScholarCrossrefSearch ADS  
+
+
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211946-MS
+
+
+

files/2022/A Method for Reducing Wellbore Instability Using the Managed Pressure Drilling MPD System.txt

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+----- METADATA START -----
+Title: A Method for Reducing Wellbore Instability Using the Managed Pressure Drilling (MPD) System
+Authors: Chinedu Ejike, Tian Shouceng
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211902-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Drilling for oil and gas wells is considered as a risk factor that is perceived as tolerable. As drilling companies expand into harsh environments and farther depth, the probability of a potential failure increases. An unexpected influx to or from the wellbore might be disastrous if not handled properly. Drilling-related issues such as jammed pipes, lost circulation, and high mud costs demonstrate the need for improved drilling technologies. The goal is to limit annular frictional pressure losses, especially in fields where the pore pressure and fracture pressure gradient are too close together. If these issues can be resolved, the economics of drilling wells would increase, allowing the industry to drill previously uneconomical wells. Managed Pressure Drilling (MPD) is a unique approach that allows the control of annular frictional pressure losses and can solve these types of drilling challenges. The industry is still mostly unaware of the entire range of advantages. Prompt detecting and handling of an influx of formation fluids can have the possibility to reduce the magnitude and extent of a kick by operating on a faster time scale with greater precision. Constant Bottomhole Pressure (CBHP), Pressurised Mudcap Drilling (PMCD), and Dual Gradient Drilling (DGD) are a few MPD variants. MPD reduces drilling issues and increases the economics of drilling wells. This research focuses on strategies employed in MPD, with the goal of uncovering some of the less well-known and thus underappreciated possibilities.
+
+
+
+
+Keywords:
+drilling,
+variation,
+mud cap drilling,
+upstream oil & gas,
+application,
+mpd application,
+choke,
+managed pressure drilling,
+mpd,
+dual gradient drilling
+
+
+Subjects: 
+Pressure Management,
+Managed pressure drilling
+
+
+
+
+Introduction
+
+
+With the prevalence of reoccurring wellbore instability problems such as lost circulation, stuck pipe, excessive mud loss, influx during onshore and offshore drilling and the need for deeper wells to meet energy demands as current reservoirs recedes, managed pressure drilling (MPD) system is an advanced drilling technology that allows a driller to monitor annular pressures in the wellbore more precisely to avoid drilling-related issues. Drilling operations in an open-vessel setting are often subjected to instabilities, which contribute significantly to Non-Productive Time (NPT), an expense that must be factored into the overall project budget. The period when a rig is not drilling is known as NPT. Pressures in an open vessel can't be accurately controlled unless the well is shut down. As a result, rather than wasting time pulling the inner bushings to search for flow, well-controlled events concentrate on increasing flow. The influx volume becomes greater and more difficult to handle in that short period of time. Bottomhole pressure (PBH) can be better regulated with the recently developed MPD process.
+
+
+The main benefits of MPD are the elimination of drilling-related non-productive time and the opportunity to drill prospective wells that are technically and/or economically un-drillable with traditional methods [1]. It is unavoidable to take advantage of the benefits that MPD offers in a variety of conditions and environments. MPD would improve the economics of every well being drilled by reducing the NPT. Since the cost of NPT has a much greater economic impact on offshore drilling than onshore drilling, and because offshore operators’ portfolios contain a higher percentage of otherwise unfeasible opportunities, offshore is the area where the technology has the most potential to support the industry as a whole [2]. Using MPD, many drilling issues in any well can be alleviated. MPD implements new methods that require comprehension, and it requires time to gain trust in the technology enough to use it on a daily basis. With the current uneconomical resources in offshore markets and the problems that can arise when drilling a well, it is crucial that the oil and gas industry looks to MPD to boost drilling rig drilling capacity. The main goal is to establish a pressure profile in the well that stays within tight tolerances and near to the pore pressure, hole stability envelope, and fracture pressure boundaries of the activity envelope [3]. The purpose of this study is to reveal some of the less well-known and hence underestimated possibilities in MPD techniques, applications, and variants.
+
+
+MPD versus Underbalanced Drilling
+
+
+MPD is derived from a few unique technologies developed by its forerunner—underbalanced drilling (UBD). It is quite similar and applies the same method as UBD. When the well is underbalanced, the hydrostatic pressure of the mud is lower than the pore pressure. Protecting, characterizing, and maintaining the reservoir while not jeopardizing the well's capacity are the key goals of UBD. In-fluxes are encouraged to do this. If the well is producing gas while drilling, it is flared, reinjected, or sold at a gathering station. Generated oil is held in stock tanks if the drilling is performed onshore. The key difference between the two approaches is that UBD is used to avoid reservoir damage, while MPD is used to solve drilling problems [4]. UBD enables formation fluid influx by drilling with the fluid in the wellbore at a lower pressure than the pore pressure. MPD regulates reservoir pressure to maintain a balance between pore and fracture pressures. It's designed to manage any fluid influx that might occur during drilling, but it doesn't promote it. UBD is concerned with reservoir problems, while MPD is concerned with drilling issues.
+
+
+Basic Principle of this Technology
+
+
+The ability to control the BHP and pressure profile as required is the basic technique in MPD. The BHP can be measured in conventional drilling by adding the mud weight hydrostatic head and the annular friction pressure (AFP). The AFP is the friction pressure generated by the mud circulation during drilling while the equivalent circulating density of the BHP is known as ECD. During a connection, the pumps are shut off and the fluid is halted, removing the annular friction pressure. Pump start-up and stop-up may have a significant impact on the pressure profile, causing pressure to fluctuate outside of the pressure-gradient window and causing drilling issues. The returns gravity flow away from the rig floor in a traditional drilling method since it is exposed to the atmosphere [2]. The pumping rate is the only way to change BHP while drilling. MPD employs a pressurized, closed mud system. The BHP can be easily changed by adjusting backpressure while drilling.
+
+
+The most typical MPD setups include a rotating control device (RCD) and a choke. The RCD diverts the pressured mud returns from the annulus to the choke manifold. The mud returns device will stay closed and pressurized with the help of a seal assembly and the RCD, allowing the rig to drill ahead. The driller will apply backpressure to the wellbore by using the choke with the pressurized mud return device. If the pressure rises above the formation's fracture pressure, the driller may open the choke to relieve backpressure and lower the pressure. If the driller needs to boost pressure in the well, closing the choke will increase backpressure. This method is most commonly employed when the pumps are turned on and off during connections. The choke is closed when the pumps are switched off to apply backpressure to replace the missing AFP. When the pumps are turned on and the AFP rises, the choke can be opened to reduce backpressure. This assists in keeping the pressure profile inside the pressure window during the well. The pressure profile in Fig. 1 indicates that when the pressure is steady, it falls below the pore pressure and that when the pressure is flowing, it exceeds the fracture pressure. A driller can keep the pressure inside the pressure window by changing the mud weight and using backpressure. While circulating, the driller may reduce the mud weight to keep the pressure below the fracture pressure. Back pressure applied without circulating could hold the pressure above the formation's pore pressure. A driller could successfully drill a well with tight pressure margins by changing the drilling plan.
+
+
+Figure 1View largeDownload slideTight margin pressure gradient window [5]Figure 1View largeDownload slideTight margin pressure gradient window [5] Close modal
+
+
+Variations and Methods
+
+
+MPD operations are divided into ‘Variations,’ with each variation achieving one of its ‘Methods.’ Over the last few years, a few MPD variants and methods have been reported and referred to in MPD literature. This subsection covers a variety of methods for achieving MPD.
+
+
+Constant Bottomhole Pressure (CBHP)
+
+
+The CBHP MPD variation is one of the most commonly used MPD variants, and it aids in keeping the BHP (or wellbore pressure at a given depth) within a given range under both static and dynamic mud circulation conditions. Wellbore pressure (WBP) consistency aids in preventing drilling issues such as regular mud weight shifts, drilling through narrow windows, and safely meeting goal while lowering NPT. So far, two methods for CBHP variation have been identified: These are back pressure (BP) application and continuous circulation system (or CCS). The BP is also known as application of backpressure (ABP). The ABP system employs devices such as a blood pressure pump and chokes to help hold some blood pressure when connecting, keeping the WBP above the pore pressure (Pp). A Continuous Circulation Coupler (CCC) is used in the CCS to keep drilling mud flowing even when making or breaking contacts which as a result, the wellbore is still in a circulating state.
+
+
+Pressurised Mudcap Drilling (PMCD)
+
+
+PMCD, also known as ‘light annular mud cap’ or ‘closed-hole circulation drilling,’ is the most commonly used variation of MPD for drilling into heavily fragmented formations and areas with extreme lost circulation issues [6]. PMCD is based on a method known as ‘mudcap drilling,’ which has been used in the drilling industry for decades to drill fields such as the Austin chalk. PMCD uses a mixture of two drilling fluids to drill into these formations: a low density low-cost sacrificial fluid and a high density pressured mud column. The drill string and drill bit are pumped with an inexpensive sacrificial fluid that is readily accessible at most drilling sites, such as seawater in offshore locations. As shown in Fig. 2, this fluid carries the rock chips and cuttings away into the broken region. The annulus above this trouble zone contains a higher density fluid known as the mudcap. This mudcap fluid's hydrostatic head aids in sustaining the necessary BHP and preventing the well from kicking.
+
+
+Figure 2View largeDownload slideThe pressurized mud cap in the annulus prevents a kick by removing cuttings into the broken formation.Figure 2View largeDownload slideThe pressurized mud cap in the annulus prevents a kick by removing cuttings into the broken formation. Close modal
+
+
+Throughout the PMCD process, the annular pressure is controlled, and if it rises, signaling hydrocarbon migration into the annulus, more mud is injected into the annulus to restore the original BP and prevent a kick. Some of the benefits of using PMCD MPD variance are that it helps drill the troubled zone that cannot be safely drilled otherwise, it helps cut costs by saving a significant amount of expensive drilling mud that would have been lost otherwise, it increases rate of penetration (ROP) by using a lower density mud, and it helps to cut costs by saving a significant amount of expensive drilling mud that would have been lost otherwise and It also eliminates a lot of NPT, which would otherwise be a major issue in zones with kick loss times, lost circulation, and other issues. Some sour formations (fields containing hydrogen sulphide) have been safely and successfully drilled for the first time using PMCD, such as the Tengiz field in Kazakhstan [7]. It's important to use a sacrificial fluid that's readily available in large quantities. A RCD, choke manifold, BOP, downhole deployment valve, and mud gas separator are among the equipment used or recommended for PMCD operations [8, 9].
+
+
+Dual Gradient Drilling (DGD)
+
+
+In extended reach wells, deepwater wells, and wells with similar drilling issues, DGD has two gradients in the WBP profile that help reach the target depth. The initial motivation for developing this technology was to solve issues with offshore conventional riser drilling operations [10, 11]. Drilling problems on onshore wells can also be solved with DGD. Using subsea annulus returns pump, riserless mud recovery, mud dilution, injecting light liquids and solids through concentric casing and/or parasite strings, and using tools like the ECD reduction method are only a few DGD techniques.
+
+
+Applications of MPD
+
+
+So far, many MPD wells have been drilled around the world, and the variety of applications for MPD has expanded dramatically in recent years. MPD has evolved rapidly in recent decades, from its conventional implementations, even before MPD was invented, and each variation was independently created and tested, to new modern applications that serve very complex objectives. Some applications of MPD have been reviewed [12, 13, 14, and 15]. When looking at the past of MPD applications, there are three distinct groups to be found. The ‘Traditional MPD Applications’ are the first category, which deals with the earliest MPD applications. MPD systems have progressed to ‘Advanced Applications’ due to technological advancements, and improved understanding and knowledge of WBP regimes.
+
+
+Traditional MPD Application
+
+
+The first MPD application was to solve problems involving pressure margins or pressure windows that were too narrow. PMCD has proven to be a viable option for drilling extremely fragmented or cavernous formations with complete or near-total mud losses, and where no other drilling technique could safely meet the mark.
+
+
+Advanced Application
+
+
+Point of constant pressure (PoCP) is a modification of CBHP that allows drilling through very small pressure windows that would otherwise be impossible to drill through with CBHP alone. The depth at which the static and dynamic WBPs are equated in PoCP is not the hole's rim. This helps drill through very small pressure windows while also reducing the operations window. Drilling through several depleted and overpressured zones in a single hole section with CBHP or PoCP is another advanced MPD application. Since there is very little space for error, such procedures necessitate better preparation, accurate equipment, and a very systematic execution. Another advanced MPD application is combining two variants for the same hole segment.
+
+
+Enhanced Applications
+
+
+MPD is now being used for advanced kick/loss detection, Pp validation, ROP enhancement, formation invasion mitigation, and a variety of other applications that don't have to deal with small windows or problems hitting the target. These were only byproducts of the earlier MPD/UBD ventures.
+
+
+MPD was used in one North Sea project to improve ROP and remain close to formation pressure. Many UBD programs are solely focused on improving ROP. However, achieving this advantage with MPD is favored because it is associated with less safety problems or concerns than UBD.
+
+
+Another benefit of lower overbalance is the reduction of formation invasion, which has been a major goal of UBD ventures. Higher overbalance raises the pressure difference between formation and wellbore fluids around the open hole, driving drilling mud or filtrate into the formation. In CBHP and DGD programs, a decrease in formation invasion is normally observed because MPD can help sustain a lower overall BHP and minimize the quantity of fluid invading into the formation.
+
+
+MPD necessitates the purchase of additional equipment in order to better manage the WBP profile and track and detect changes in fluid flow and volume. This also makes it possible to detect an influx of fluids from the formation or a leakage of fluids into the formation very early. Kicks and losses can be identified early on, reducing NPT and preventing undetected kicks and blowouts. Kick-loss periods have become a very challenging drilling hazard as the depth and complexity of offshore and onshore wells has increased across the world. In such critical wells, MPD has proved to be invaluable.
+
+
+The variants of MPD are described below, with sub-categories based on their implementation areas and different strengths:
+
+
+▪Constant Bottom Hole Pressure○Drill through the Limits (DTTL) Method○Continuous Circulation Method○Friction Management Method▪Pressurised Mudcap Drilling○Controlled Mud Cap Drilling (CMCD)○Floating Mud Cap Drilling (FMCD)▪Dual Gradient Drilling○Riserless Dual Gradient Method○Annulus Injection Method
+
+
+Equipment's for MPD
+
+
+Signal, 2006 [16] listed the following MPD equipment that is used in MPD operations:
+
+
+▪ECD reduction tools▪Nitrogen production units▪Surface and subsea RCD▪Real-time pressure and flow-rate monitoring equipment▪Continuous circulating systems▪Manual, semiautomatic, and process-controlled choke manifolds▪Wireline-retrievable drillstring floats▪Surface mud logging equipment▪Casing isolation valves▪Subsea mud-return pumps▪Pressure while drilling equipment or ‘PWD’
+
+
+Other key components includes but not limited to:
+
+
+▪Rotating control device▪Geobalance choke (choke skid)▪Back pressure pump
+
+
+Limitations of MPD Technique
+
+
+▪In MPD techniques, no samples or logs are obtained, which is crucial for geologists.▪In the PMCD procedure, a large amount of drilling fluid is used.▪Problems with removing drilling strings from holes with absolute losses after hitting total depth or making intermediate trips.▪The processes for operations are very complex.
+
+
+Potential Improvement of MPD Technique
+
+
+Since MPD is still developing and adapting its strengths to meet new challenges, the method necessitates extra effort to identify the concept's missing pieces. If the missing pieces of various variations in a variety of applications have been discovered, the next step is to minimize the impact of gaps by adapting existing technology to MPD or finding a new technology that will contribute to MPD use. One of the main technological gaps in MPD adaptation should be addressed in order to accelerate the process of MPD adaptation to deep water applications.
+
+
+Many variations in the application of MPD in the field are still being created. Using compressible fluids with MPD is an intriguing variation that would allow for controlled pressure drilling using air, mist, or foam [17]. This could lead to a higher ROP while drilling while maintaining the pressure inside the gradient window. Another option is to use solids in the mud to plug and support microfractures that may occur in weaker formations by using a higher density mud [17]. This difference would not change the wellbore's pressure gradient, but it would extend the drilling window, allowing the well to be drilled successfully. For future MPD operation, a backpressure pump will provide efficient controller output. Furthermore, including a time delay parameter in the prediction model could improve the controller's ability to handle abrupt flow changes.
+
+
+Conclusions
+
+
+MPD is a modern technique that will help wells to be drill more economically. It will help companies raise their reserves by allowing them to drill in previously uneconomical areas.MPD may assist in the resolution of many of the issues that arise as a result of pressure variations in the formations.MPD uses techniques that are similar to those used in underbalanced drilling, which may make the transition to MPD technology easier for businesses.CBHP, PMCD, DGD are some of the variants of MPD.MPD requires extra work to uncover the concept's missing elements because it is still developing and modifying its strengths to meet new difficulties.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Abbreviations
+
+
+AbbreviationsAbbreviationExpansion MPD– Managed Pressure Drilling PBH– Bottom Hole Pressure NPT– Non Productive Time UBD– Underbalanced Drilling AFP– Annular Friction Pressure ECD– Equivalent Circulating Density RCD– Rotating Control Device CBHP– Constant Bottomhole pressure WBP– Wellbore Pressure BP– Backpressure CCS– Continuous Circulation System ABP– Application of Backpressure Pp– Porepressure CCC– Continuous circulation Coupler PMCD– Pressurised Mud Cap Drilling ROP– Rate of Penetration DGD– Dual Gradient Drilling PoCP– Point of Constant Pressure DTTL– Drill through the Limit CMCD– Controlled Mud Cap Drilling FMCD– Floating Mud Cap Drilling PWD– Pressure while Drilling
+
+
+References
+
+
+ErdemTercan. 2010. Managed Pressure Drilling Techniques, Equipment And Applications. Msc Thesis. Middle East Technical University, AnkaraGoogle Scholar don.hannegan@weatherford.com, "Discussions on MPD Technology", email communication, 8th – 30th December 09.Fossil, B., and Sangesland, S.: .Managed Pressure Drilling for Subsea Applications; Well Control Challenges in Deepwater. paper SPE 91633 presented at the 2004 SPE/IADC Underbalanced Technology Conference and Exhibition, Houston, 11 – 12 October.Hannegan, D.: Managed Pressure Drilling in Marine Environment- Case Studies. paper SPE 92600 presented at the 2005 SPE/IADC Drilling Conference, Amsterdam, The Netherlands, 23- 25 February.Hannegan, D: Managed Pressure Drilling. SPE Advanced Drilling Technology & Well Construction Textbook, Chp. 9, Section 10.Moore, D.2008. Mud Cap Drilling. In Managed Pressure Drilling ed. J.Schubert, A.Haghshenas, A. S.Paknejad, and J.Hughes, 155–180. Houston: Gulf Publishing Company.Google ScholarCrossrefSearch ADS  Gault, A.1996. Riserless Drilling: Circumventing the Size/Cost Cycle in Deepwater. Offshore56 (5): 49–54.Google Scholar Juvkam-Wold, H. C.2007. "PETE 628: Offshore Drilling", Dual Gradient Drilling, Texas A&M University, College Station, Texas, USA (7March).Google Scholar LingYan, HuishengWu, YanYan, Application of fine managed pressure drilling technique in complex wells with both blowout and lost circulation risks, Natural Gas Industry B, Volume 2, Issues 2-3, 2015, Pages 192–197, ISSN 2352-8540, https://doi.org/10.1016/j.ngib.2015.07.010.Google Scholar LeiShi, PingChen, ZeHu, YingjunFu. The application of bottom-hole flowmeter in the MPD systemJ Southwest Petrol Univ Sci Technol Ed, 32 (6) (2010), pp. 89–93Google Scholar HaifangSun, JinghaiFeng, KuanliangZhu, LiangqingBai, BoYang, Zhilin Li Application of fine managed pressure drilling (MPD) technology developed by CCDC in well NP23-P2009Drill Prod Technol, 35 (3) (2012), pp. 1–4Google Scholar Sweep, M. N.Bailey, J. M. and Stone, C. R.2003. Closed Hole Circulation Drilling: Case Study of Drilling a High-Pressure Fractured Reservoir – Tengiz Field, Tengiz, Republic of Kazakhstan. Paper presented at the SPE/IADC Drilling Conference, Amsterdam, 19-21 February. SPE 79850.Google Scholar Colbert, J. W. and Medley, G.2002. Light Annular MudCap Drilling – A Well Control Technique for Naturally Fractured Formations. Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29 September-02 October. SPE 77352.Google Scholar Moore, D.2008. Mud Cap Drilling. In Managed Pressure Drilling ed. J.Schubert, A.Haghshenas, A. S.Paknejad, and J.Hughes, 155–180. Houston: Gulf Publishing Company.Google ScholarCrossrefSearch ADS  LinShi, XiongwenYang, YingcaoZhou, HuaizhongLi, YingWangApplication of China-made precise managed pressure drilling equipment in the Tarim BasinNat Gas Ind, 32 (8) (2012), pp. 6–10Google Scholar SIGNA. 2006. Managed Pressure Drilling Manual. SIGNA Engineering Corporation: Houstons.Matthew DanielMartin. 2016. Managed Pressure Drilling Techniques And Tools. Msc Thesis. Texas A&M University, HoustonGoogle Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211902-MS
+
+
+

files/2022/A Novel Approach and Application to Dual String Design in Smart Well Completion.txt

@@ -0,0 +1,437 @@
+----- METADATA START -----
+Title: A Novel Approach and Application to Dual String Design in Smart Well Completion
+Authors: Daniel Omolewa, Boniface Oriji
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211997-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Completion design is a very important phase in the development of a field, and it has evolved over the years with more sophisticated technology. One of the latest technology trends is the Intelligent Digital Oil fields, which although started decades ago has recently received rapid traction in past few years due to reduced costs and improvement in sensors and data storage. The starting point of this technology is the smart or intelligent well system (IWS). This system significantly improves reservoir management as it enables remote control and monitoring of downhole equipment’s. Consequently, this minimizes the need for any intervention and saves OPEX. However, the IWS has majorly being applied to a single string producer or injector. Previously, a single string completion is used across the multiple zones and hence must be commingled if the zones are to be produced simultaneously. The alternative to produce simultaneously without commingling is to use Dual string completions but they have a major drawback in that they always require some intervention to be done which is expensive. The aim of this project is therefore to test a novel idea of combining a Dual String completion with an Intelligent completion. A hypothetical field with three reservoir zones stacked was used for this study. The objective was to produce the lower zones through the long string as they had similar reservoir pressures and compatible fluids while the upper zone would produce through the short string as it is incompatible with the lower zones. Two cases were considered – with and without lower completions. The major difference in these cases was the position of the accessories. The adopted design uses a feed-through dual packer, two ICVs, and a dual gauge on the long string above the gravel pack packer and on the short string, a permanent gauge is placed above the feed-through dual packer and a total of 6 control lines is required. It is concluded that the design is feasible in both cases, and it solves the short comings of the dual string completions currently being used. The critical consideration is the well architecture. The knowledge from this paper serves as a foundation for what could become a new standard design for smart completions.
+
+
+
+
+Keywords:
+upstream oil & gas,
+casing design,
+well integrity,
+completion equipment,
+completion,
+completion monitoring systems/intelligent wells,
+flowrate,
+dual packer,
+zonal isolation,
+intervention
+
+
+Subjects: 
+Casing and Cementing,
+Information Management and Systems,
+Casing design,
+Completion Selection and Design,
+Completion Monitoring Systems/Intelligent Wells,
+Well Integrity,
+Completion equipment,
+Flow control equipment,
+Zonal isolation
+
+
+
+
+Introduction
+
+
+Completions contribute a major part of the cost of drilling a well, so there is a need to design the best possible completion to minimize the cost of investments. Dual String completions help to selectively produce zones in a multi layered reservoir, but the sliding sleeves used for the selectivity must be opened or closed using wireline services (Ding & Kenji, 2018). This inevitability of carrying out costly well intervention during the lifecycle of a well, especially in subsea wells has led to increased interests in smart well technology which replaces the need for routine well interventions.
+
+
+The Smart or Intelligent completions is a technology that reduces the need for interventions as it enables remote control and monitoring of the well. Several case studies have reported the benefits of this technology. For example, during the study of (Adebayo, et al., 2017), the smart well completion helped to make an uneconomic marginal field economic by enabling commingling from three reservoirs. Although the commingling of multiple reservoirs is strictly regulated in countries like Nigeria, Smart wells’ benefits have been proven in practical applications for commingled and non-commingled production (Al-Ghareeb, 2009).
+
+
+However, while the use of single string completions with smart wells is widely discussed by many researchers, the use of smart wells with dual string completions is largely under researched but has recently shown good faith. In a case study by (Silverwell, 2020), they worked with an operator that had problems with gas lifts in their dual completion wells arising from the low casing head pressures and lack of downhole controls that consequently led to costly interventions. The problem was solved by installing complex dual string intelligent gas lifts and this led to a forecasted 40% production increase.
+
+
+Smart Well Completion
+
+
+A smart well (similarly popular as an intelligent well) is a well equipped with sophisticated completion accessories which monitors well properties, collects, transmits, and analyzes downhole production data, and then allows actions to configure and control parts of the well. Smart well completion is a high-tech control system that automatically controls hydrocarbon production (Wan, 2011). It makes real-time monitoring and control of the wellbore possible. The true test of intelligence is Data (Bello, 2021), so for a well to be called smart or intelligent, it means the well inherently has data processing and decision-making capability. It does this by measuring and monitoring downhole production and transmitting it real-time to surface. Therefore, a well failure that necessitates a workover or abandonment would completely undermine the objective of an intelligent well. It is also popular for its capability for remote water shut off.
+
+
+Areas of Application of SWC
+
+
+Horizontal wellsSubsea satellite wells (Wan, 2011)Deep water accumulations requiring extended reach wells (ERW)Multi zones injection and production wellsHighly productive oil fields with high cost of well interventionAuto-gas lift injection
+
+
+Figure 1 below shows a typical use of intelligent well completion to exploit individual sand zones.
+
+
+Figure 1View largeDownload slideUse of intelligent well to produce several zones in a single well (Gustavo, Marko, & Stan, 2018)Figure 1View largeDownload slideUse of intelligent well to produce several zones in a single well (Gustavo, Marko, & Stan, 2018) Close modal
+
+
+Components of SWCs
+
+
+There are four major components that makeup the Smart well completions
+
+
+Inflow Control Devices (ICD)/Inflow Control Valves (ICV)Downhole SensorsFeed-through PackerControl Lines
+
+
+Dual String Completion
+
+
+Two tubing within a single well casing is referred to as a dual string completion (Chew, Mohamed, Kew, & Jang, 2019). Usually, the strings are designed to reach different depths. The longer tubing string is referred to as the "Long String", while the shorter one is referred to as the "Short String".
+
+
+The benefit of dual strings is seen when multiple zones are to be produced but if the zones are to be commingled, the variations in pressure and production rate between distinct stratum series should be examined to prevent backflow into the lower pressure zone. Another advantage is that incase flow from one of the zones stops or becomes irregular, the zone can be cleverly converted to a gas-lift producer immediately.
+
+
+The major problems of this type of completion are the amplified cost of work-over operations, artificial lift, and sand control (Tausch & Kenneday, 1956). For dual-string production, other artificial lifting mechanisms apart from gas lifts are considered impractical (Wan, 2011).
+
+
+Areas of Applications
+
+
+For zones with incompatible fluids, where commingling could lead to flow assurance issues e.g., scalesFor zones with different pressure regimes which could lead to serious crossflow if their fluids are commingledWhere reserves assurance is critical i.e., excessive water production from one zone may ‘kill’ another zone.Countries where Government regulatory requirements forbid commingling to be able allocate production to different zones.For multipurpose wells with production from one zone and injection into another zone. (Bellarby, 2009)
+
+
+Proposed Dual String Smart Completion
+
+
+The Data of Field X is a hypothetical field modified from previous papers and is used in this study to test the concept. The geology, reservoir and drilling data were used to make decisions on the choice of completion design while respecting the technical constraints.
+
+
+Field Data
+
+
+Block X is situated in Nigeria, and It accounts for some major discoveries in the country’s oil and gas industry. The water depth is about 2000 ft. In this block, there are three (3) stacked reservoirs. Throughout this project, they will be referred to as zone A, zone B and zone C respectively. The field is defined with an extent of about 31250 acres with an original oil in place of 40 billion barrels. The expected flowrate from each well is about 10,000 BOPD.
+
+
+Issue Description
+
+
+Producing from the three zones is quite challenging since the reservoirs are stacked but their fluids are incompatible and are at different pressures. The current industry used IWC configurations will enable good reservoir management to independently produce the zones but since this will be done through a single string, simultaneous production from the zones can’t be done which consequently leads to a longer time to reach plateau or potential of the well.
+
+
+Concept Proposed
+
+
+The dual string offers the possibility to produce the zones simultaneously. The lower zones could be produced through the long string and the upper zone through the short string. Fitting the strings with IWC accessories will address the current challenges of dual strings as the need for costly interventions will be minimized and it also reduces the delay to reach the maximum well potential.
+
+
+Objectives of the Completion Design
+
+
+Well X is to be completed as an oil producer with the following objectives;
+
+
+–To lower the OPEX and minimize the need for future intervention–To design a dual string completion that enables production from the three stacked reservoirs–To design a dual string completion that ensures all operations can be performed in a safe and timely manner–To minimize completion fluid losses–To ensure the tubing can handle the stresses it encounters–To be able to take downhole readings of pressure, temperature, and flowrate
+
+
+The philosophy is to make the subsea completion as simple as possible and ensure it is equipped with the most reliable and economical equipment’s. The goal is to ensure that there is almost no need for any future well interventions.
+
+
+Workflow
+
+
+The completion design steps mainly include designing the IWC with maximum flexibility for control, screening & eliminating unnecessary IWC options and finally evaluating the performance of the IWC. The well is to be completed as a Dual String producer and the casing & drilling program of well X was reviewed to understand the well trajectory and the loads the intermediate and production casings might be exposed to. The well is a vertical well and the summary of the casing program is shown in Table 1 and Figure 2 illustrates the completion design steps in more detail.
+
+
+Figure 2View largeDownload slideBlock flow diagram of completion design processFigure 2View largeDownload slideBlock flow diagram of completion design process Close modal
+
+
+Table 1Summary of casing program Casing
+            . Shoe Depth (ft)
+            . OD (in)
+            . Drift ID (in)
+            . Thread
+            . Grade
+            . Weight (lb/ft)
+            . Surface casing 3000 20 18.81 Buttress C90 106.5 Intermediate casing 5000 13 3/8 12.06 Buttress C90 80.70 Production casing 11350 9 5/8 8.525 VAM Top N80 47 Casing
+            . Shoe Depth (ft)
+            . OD (in)
+            . Drift ID (in)
+            . Thread
+            . Grade
+            . Weight (lb/ft)
+            . Surface casing 3000 20 18.81 Buttress C90 106.5 Intermediate casing 5000 13 3/8 12.06 Buttress C90 80.70 Production casing 11350 9 5/8 8.525 VAM Top N80 47 View Large
+
+
+The scenario for the application the IWC initially involved investigating that the reservoir properties are feasible for this project. This included majorly basic analysis of number of layers, pressure differences between intervals, size of reservoir, composition of fluid and recovery mechanisms. The second step was to study the field environment (offshore or onshore), the well trajectory (deviated or vertical), the type of well (Producer or injector), possible need for artificial lifts etc. The final step in scenario identification is to consider the economics i.e., the added investment costs compared to the added revenue. This step was not considered in detail as it was out of scope for this project. It is assumed to be economically viable.
+
+
+Conceptual Completion Design
+
+
+This stage involved working with Schlumberger Nigeria completions team in the preliminary selection of the required components to meet the completion objectives and then sketches of the possible configurations were made. The possible configurations were then screened to eliminate unnecessary options. As shown in Figure 3 below, the sketches were made on paper and then digitalized using the Adobe Illustrator® software.
+
+
+Figure 3View largeDownload slideScreenshot showing the Completion sketching process on IllustratorTMFigure 3View largeDownload slideScreenshot showing the Completion sketching process on IllustratorTM Close modal
+
+
+Two cases were considered, one with only upper completions and one that includes lower completions i.e., sand control.
+
+
+Completion Selection
+
+
+The components were selected considering the production rate predicted and the stated completion objectives. A preliminary list of components was generated using the experience of the Schlumberger completion team which were then tailored to the project needs.
+
+
+Tubing
+
+
+Table 2 and 3 shows a summary of the characteristics of the tubing selected. The tubing was selected based on three factors: Flowrate, experience and well performance. The 3 ½" tubing was preliminarily selected to meet the required flowrate of 10,000 Bopd. From literature, the required tubing size would have been 4" but from experience the biggest tubing size used in dual string completions is 3 ½" so this was selected for both strings initially. Consideration was then given to the drift ID of the production casing. The main question was "would both strings have sufficient clearance to be run in?". The largest accessory size expected when the two strings will be together is the safety valve. Using two 3.5" string with the safety valves will leave only 0.035" clearance which will make it almost impossible to run the control. Therefore, using the 2 7/8" tubing for the short string and the 3 ½" tubing for the long string was adopted. This gives a minimum clearance of about 0.535". So, the tubing strings with the accessories should be able to be run inside the 9 5/8" casing without getting stuck.
+
+
+Table 2Long String Tubing Design Summary Tubing Sizing
+            . OD (inch)
+            . ID (inch)
+            . Drift (inch)
+            . Connection
+            . Weight (lbs/ft)
+            . Weight (daN/m)
+            . Grade
+            . Collapse (bar)
+            . Burst (bar)
+            . Tension (kdaN)
+            . 3 1/2 3.5 2.992 2.867 VAM TOP 9.2 13.43 N80 726 701 92.2 Tubing Sizing
+            . OD (inch)
+            . ID (inch)
+            . Drift (inch)
+            . Connection
+            . Weight (lbs/ft)
+            . Weight (daN/m)
+            . Grade
+            . Collapse (bar)
+            . Burst (bar)
+            . Tension (kdaN)
+            . 3 1/2 3.5 2.992 2.867 VAM TOP 9.2 13.43 N80 726 701 92.2 View Large
+
+
+Table 3Short String Tubing design summary Tubing Sizing
+            . OD (inch)
+            . ID (inch)
+            . Drift (inch)
+            . Connection
+            . Weight (lbs/ft)
+            . Weight (daN/m)
+            . Grade
+            . Collapse (bar)
+            . Burst (bar)
+            . Tension (kdaN)
+            . 2 7/8 2.875 2.441 2.346 VAM TOP 6.5 9.49 N80 770 729 64.5 Tubing Sizing
+            . OD (inch)
+            . ID (inch)
+            . Drift (inch)
+            . Connection
+            . Weight (lbs/ft)
+            . Weight (daN/m)
+            . Grade
+            . Collapse (bar)
+            . Burst (bar)
+            . Tension (kdaN)
+            . 2 7/8 2.875 2.441 2.346 VAM TOP 6.5 9.49 N80 770 729 64.5 View Large
+
+
+Figure 4View largeDownload slideAuthor with a part of Schlumberger’s completion teamFigure 4View largeDownload slideAuthor with a part of Schlumberger’s completion team Close modal
+
+
+The grade of the tubing was selected based on the burst, collapse and tension pressures that were calculated. Using the developed Excel program for both strings, the burst pressure, the collapse pressure, and tension were calculated. Using the Drilling Data Handbook (DDH), the minimum grade selected for the long and short string is 9.2lb/ft N80 tubing and 6.5 lb/ft N80 tubing respectively.
+
+
+Regarding the connections and type of thread, the VAM TOP was selected due to the high pressures the tubing string is exposed to. It provides the required metal to metal sealing to ensure flow integrity.
+
+
+Dual Packer
+
+
+The dual packer would be used to provide isolation for Zone 1 and support the weight of the long and short strings. Schlumberger’s Ultra dual packer, which is a hydraulically set, pull-to-release packer was chosen so as ease any future workover operation The dual packer would set by the long string by putting a plug inside the bottom no-go nipple. When deciding on the choice of packer to use, the pressure and temperature of the fluids it will be exposed to was considered and the anticipated need for retrieval. It will be run with the upper completions in a single trip.
+
+
+Also, the possibility of passing the hydraulic and electrical lines through them was considered. The chosen dual packer is a feed-through type.
+
+
+Single packers
+
+
+The choice of the single packer depends on the type of completions. The packers to be used in the two cases were investigated
+
+
+Case 1
+
+
+This is shown in figure 5. The single packers would be used to isolate the lower zones in an upper completion only scenario i.e., no sand control intended. The selection of the type of packer is based on reservoir pressure and temperature, feed-through ability and need for ease of retrieval or need to reduce risk of premature release. Weatherford’s HellCat™ 2 Intelligent-Completion Packer was considered. The cut-to-release type was selected to reduce the risk of failure due to the higher pressures it is exposed to. The Schlumberger’s XMP Premium multiport production packer was also considered as it offered the same advantages as the alternatives and since majority of the components will be sourced from Schlumberger, this option was adopted.
+
+
+Figure 5View largeDownload slideCase 1 - Completion sketch (without accessories)Figure 5View largeDownload slideCase 1 - Completion sketch (without accessories) Close modal
+
+
+Case 2
+
+
+A second case was considered, that included lower completion (Figure 6) i.e., sand control included. Here zone 2 and 3 are gravel-packed using gravel-pack packers. The gravel-pack packer would include an extension which will provide the flow path for the slurry. The selection of the type of packer is based on reservoir pressure and temperature and reliability. The Schlumberger’s "QUANTUM MAX HPHT gravel- and frac-pack" packer was selected as its rating (10,000 psi) is sufficient and can be retrieved if necessary.
+
+
+Figure 6View largeDownload slideCase 2 – Completion sketch (without accessories)Figure 6View largeDownload slideCase 2 – Completion sketch (without accessories) Close modal
+
+
+Flow/Interval Control Valve (FCV/ICV)
+
+
+The ICV will enable remote downhole control, which makes it intelligent. The key considerations that were taken were;
+
+
+–The flowrates expected–The reservoir pressure and temperature–Cost–Level of control desired–Number of control lines required
+
+
+The Schlumberger’s TRFC-HN tubing retrievable flow control valve was selected. It has a 7,500-psi pressure rating and 325 °F temperature rating. The valve has a 10-year life span and can handle a flowrate of about 40,000 bbl/d which is reliable enough. It is equipped with 11 possible positions including fully open and closed. It also reduces the number of control lines required to be run as only a single hydraulic control line is used. Two types of ICV’s are used: The annular valve (AP) and the in-line (LP) valve. The AP valve would be used to control the annular flow from zone 2 while the LP valve will be used to control the flow within the tubing.
+
+
+For case 1, the ICVs can be positioned just across the zones but for case 2, they can’t be positioned this close because of the gravel-pack packer ID so they will be positioned above the top gravel-pack packer.
+
+
+Also, the feasibility of including an ICV to the short string was considered. This could be used to control water production from zone 1 during its late life. However, it wasn’t adopted as it was identified as not critical and to lower the total well cost.
+
+
+Permanent Downhole Gauge (PDHG)
+
+
+The PDGH would be used for reservoir monitoring and data gathering. For this design, a dual gauge was selected to take both tubing and annulus data of pressure and temperature simultaneously. The Schlumberger’s Metris EvolveTM system was chosen. It will be configured with the dual sensors. It is rated to 10,000 psi and will housed in the Solid gauge mandrel (SGM).
+
+
+Subsurface Safety Valve (SSV)
+
+
+The SSV was included to ensure the safety of the well. With this, an uncontrolled control flow of hydrocarbon can be prevented peradventure severe damage to the wellhead occurs. The safety valve would be a Surface-controlled subsurface safety valves (SCSSVs). Two types of SCSSVs were considered for the design: The Tubing retrievable type (TR-SCSSV) and the wireline retrievable type (WR-SCSSV). The WR-SCSSV has a smaller OD which reduces the risk of getting stuck considering the control lines and second string. However, it has a very small ID which will prevent any wireline operations. The TR-SCSSV offers a larger internal diameter which allows wireline tools to be run through them, but its disadvantage is its larger outer diameter of about 5". This was chosen because there is still sufficient clearance when considering the 8.525" drift ID of the 9 5/8" casing and the combined OD of the 3 ½" × 2 7/8" tubing strings.
+
+
+Hydraulic and Electric control lines
+
+
+A total of 8 control lines are required conventionally in this design. Two hydraulic lines per valve and one Electric line per gauge. The ICVs will be controlled from the surface using two hydraulic lines. One to open, the other to close. Each ICV will have this pair. However, the adopted option was to use the Schlumberger’s TRFC-HN where only one control line is required per valve. Also, the Schlumberger ManaraTM system was considered which allows only one control line for closing all the ICVs to be run but this option was not adopted as it was considered too expensive and not critical for the completion objectives. Table 4 shows the summary of each system considered.
+
+
+Table 4Summary of required control lines 
+            . Conventional
+            . Adopted
+            . ManaraTM
+            . No. of Electric Lines 2 2 2 No. of Hydraulic Lines 6 4 1 Total 8 6 3 Remark complex Feasible Expensive 
+            . Conventional
+            . Adopted
+            . ManaraTM
+            . No. of Electric Lines 2 2 2 No. of Hydraulic Lines 6 4 1 Total 8 6 3 Remark complex Feasible Expensive View Large
+
+
+Final Completion design
+
+
+Figure 7 shows the basic sketch of the final completion schematic developed using Adobe Illustrator and Figure 8 shows the final completion schematic with all the accessories used in the design.
+
+
+Figure 7View largeDownload slideSketch of final completion schematicFigure 7View largeDownload slideSketch of final completion schematic Close modal
+
+
+Figure 8View largeDownload slideFinal design of the lower completion (Left) and Upper completion (Middle) and full (Right)Figure 8View largeDownload slideFinal design of the lower completion (Left) and Upper completion (Middle) and full (Right) Close modal
+
+
+Well Performance
+
+
+Well X performance was studied using the IPR curves generated in the PIPESIM software. Nodal analysis was carried out with the node at bottomhole. This was done to determine the conditions at which the well will flow. Sensitivity analysis was done on various tubing sizes to select the optimum size. The reservoir data was used to generate the IPR curves.
+
+
+Long String
+
+
+Figure 9 shows the system performance of the long string varying three tubing sizes: 2-3/8", 2-7/8" and 3-1/2". Using a 2-3/8" tubing, the production rate is 4452 STB/d while that of the 2-7/8" and 3-½" tubing was 9176 STB/d and 13,839.01 STB/d respectively. As expected, the larger the tubing size, the higher the achievable rates and since the increase is quite significant, coupled with technical considerations discussed earlier, the 3 ½" tubing size was selected for the long string.
+
+
+Figure 9View largeDownload slide(Left) Long String sensitivity analysis showing flowrates at varying tubing sizes. (Right) Nodal analysis showing long string commingled flow and selectively flow exceeding erosional limitsFigure 9View largeDownload slide(Left) Long String sensitivity analysis showing flowrates at varying tubing sizes. (Right) Nodal analysis showing long string commingled flow and selectively flow exceeding erosional limits Close modal
+
+
+From Figure 9 (Right), it is observed that using the 3 ½" tubing, the total production rate from the two lower zones commingled is 13,839 STB/d while the production rate from Zone B and Zone C selectively is 10,351.99 STB/d & 10,846 STB/d respectively. This is even higher than the expected production rate of 10,000 STB/d. Whilst this is very encouraging, it is also observed that this operating point is outside of the recommended operating envelope. This is because the erosional velocity is exceeded. This means this rate is too high for the tubing size and this can be fixed by bean down. Sensitivity analysis was carried out into to determine the optimum tubing size and bean size.
+
+
+Figure 10 shows the result of the choke size sensitivity analysis on the long string. The flowrates were varying based in the choke sizes; 16/64, 20/64, 24/64, 28/64, 32/64, 36/64. From this analysis, the 3 ½" tubing with a 36/64 choke was selected as it falls within the operating envelope i.e., the erosional velocity was not exceeded. At these conditions, the two zones commingled will flow at 6,359 STB/d with the flowing bottom hole pressure of 5924 psi and wellhead pressure of 2765.65 psi.
+
+
+Figure 10View largeDownload slide(Left) Sensitivity analysis of effect on choke size for the long string (Right) Adopted Long string performanceFigure 10View largeDownload slide(Left) Sensitivity analysis of effect on choke size for the long string (Right) Adopted Long string performance Close modal
+
+
+Figure 10 (Right) shows the result of selective and commingled production using the adopted flowing conditions. Producing Zone 2 only, the well flows at 3731.4 STB/d while Zone 3 alone flows at 4620.59 STB/d. It is therefore recommended to commingle these zones to make it more economical.
+
+
+Short String
+
+
+Figure 11 shows the system performance of the short string varying three tubing sizes: 2 3/8", 2 7/8" and 3 1/2". Using a 2 3/8" tubing, the production rate is 4299.4 STB/d while that of the 2 7/8" and 3 ½" tubing was 7865.08 STB/d and 10508.6 STB/d respectively. Here the 2-7/8" tubing was selected, as the overriding consideration was the increased clearance in the casing. However, this rate is seen to exceed the erosional velocity limits therefore the effect of varying the choke sizes was studied.
+
+
+Figure 11View largeDownload slide(Left) Sensitivity analysis showing flowrates at varying tubing sizes for the short string. (Right) Effect of choke size for the short stringFigure 11View largeDownload slide(Left) Sensitivity analysis showing flowrates at varying tubing sizes for the short string. (Right) Effect of choke size for the short string Close modal
+
+
+Figure 11 shows the result of the choke size sensitivity analysis on the short string. The flowrates were varying based on the choke sizes; 16/64, 20/64, 24/64, 28/64, 32/64, 36/64. From this analysis, the 2-7/8" tubing with a 28/64 choke was selected as it falls within the operating envelope i.e., the erosional velocity was not exceeded. At these conditions, the well will flow at 3876.16 STB/d and flowing bottom hole pressure of 5424.8 psi with a wellhead pressure of 2545.97 psi.
+
+
+Summary of Findings
+
+
+In this project, the novel idea to integrate these aspects for a dual string intelligent well system was attempted. Previously, Intelligent single string completion is used across the multiple zones and hence must be commingled if the zones are to be produced simultaneously. Is this about to change?
+
+
+The well X was selected based on the possibility to achieve this design. The considerations that influenced this decision includes the field size, geology, the number of reservoir zones, fluid properties, the casing program. Two cases were investigated, the first case neglected the need for sand control and the second case included the lower completion. It was discovered that it was impossible to have the intelligent components below a gravel-pack packer due to the OD of these components. Therefore, the designs were slightly different with the difference being the position of the intelligent components. This implies that the annular capacity required to be filled up before reaching the ICV should be considered when lower completions are required.
+
+
+The annular clearance was found to be a challenge in this design. It was discovered that it is near impossible to have the two 3-1/2" strings, except the production casing is bigger than 9-5/8 or the lightest grade of this casing. This is due to the number of control lines required. Therefore, slightly lower production rates can be achieved from the zone producing through this string.
+
+
+In the design of the tubing, it was discovered that the calculations for the collapse ratings were slightly different than that from a single zone. Only the annulus above the dual packer will be full of brine during the producing life of the well, therefore the different densities of the fluids from the different zones were also used in the calculations. In this design, this led to lower values calculated. The calculation for the burst pressures didn’t change. The grades of the two strings were based on the string facing the worse conditions which was the long string. This is to prevent corrosion induced by using different materials.
+
+
+Different levels of intelligence were also discovered to be possible depending on the level of control desired. The more control desired, the more expensive the well becomes. In this design, it was decided that the short string didn’t need to be super intelligent as only remote pressure and temperature measurements were termed critical. Should water cut increase the subsea choke could be bean down to fix it.
+
+
+Also, the number of control lines required to be run is an important consideration. This affects the reliability of the completion and rig space required to handle them. and A maximum of 8 control lines would be required to control and or monitor the three zones. These can be reduced by utilizing more expensive technologies such as Schlumberger’s ManaraTM system. With this system only 3 lines will be required.
+
+
+Conclusions
+
+
+This project has taken the Dual string intelligent well completion strategy from idealization to conceptualization and has shown that this design is technically feasible. The well production and technical objectives were met. The proposed design solves some of the shortcomings of the dual string completion and that of intelligent completions. This design can enable simultaneous production of multiple zones where non-compatible zones exist. It will also minimize the need for any future well interventions. It concluded that the quality of the seals is crucial to ensure proper zonal isolation and more expensive technology are required to reduce the number of control lines required to be feed-through the packers to improve the reliability of the design. Also, it is very difficult to have two 3-1/2" tubing which are the maximum for a conventional dual string completion. This is due to clearance require to run the control lines. Therefore, proper casing design during the initial drilling design phase is required for this design to work. The tubing grade should be based on the highest reservoir pressures. Finally, the design can work for both completions requiring lower completions and those that don’t.
+
+
+Recommendations
+
+
+The success of this research was a result of the collaboration of Schlumberger Nigeria and several professionals with the Author. Industry-academia collaboration is crucial if novel ideas are to be tested. However, a lot more work is needed to be done to take this idea from conceptualization to implementation and here are some recommendations;
+
+
+Detailed design of ICVs/ICDs for dual string completion should be carried outStress and Load simulations of running the gravel pack assembly should be investigatedThe effect of deviation in the design of dual string intelligent completions should be investigatedEconomic analysis of this completion should be carried outDynamic reservoir simulation should be carried out to evaluate the ICV performance
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgments
+
+
+The authors wish to thank the Management and staffs of Schlumberger Nigeria, especially the completions team for the providing the facilities and support to carry out this research. In addition, he wishes to thank the professionals at TotalEnergies, Haliburton and Cyphercrescent that helped review several aspects of the project.
+
+
+References
+
+
+Adebayo, A., Kassim, H., Abiodun, L., Nwankwo, C., Ricky, I., & Magnus, N. (2017). Smart Well Completion Design using Feed-Through Swell Packers in a Gas Well: A Case Study. Society of Petroleum Engineers, SPE-189179-MS.Google Scholar Al-Ghareeb, Z. M. (2009). Monitoring And Control of Smart Wells. Stanford University.Google Scholar Bellarby, J. (2009). Well Completion Design. Aberdeen, UK: Elsevier.Google Scholar Bello, J. (2021, June1). Rethinking intelligent completions: the future is here. Retrieved from offshore-mag: https://www.offshore-mag.com/drilling-completion/article/14203049/weatherford-rethinking-intelligent-completions-the-future-is-hereGoogle Scholar Chew, C. L., Mohamed, Z. Z., Kew, H. C., & Jang, H. L. (2019). Hybrid Model for Determining Dual String Gas Lift Split Factor in Oil Producers. Energies, 12(12), 2284.Google Scholar Ding, Z. P., & Kenji, F. P. (2018). Modern Completion Technology for Oil and Gas Wells. New York, Chicago, San Francisco, Athens, London, Madrid, Mexico City, Milan, New Delhi, Singapore, Sydney, Toronto: McGraw-Hill Education.Google Scholar Silverwell. (2020). Case History: Dual String DIAL System Successfully Installed for Offshore South East Asia Operator. Retrieved from Silverwell Web site: https://www.silverwellenergy.com/case-histories/south-east-asia-offshore-dual-string-completionWan, R. (2011). Advanced Well Completion Engineering. USA: Gulf Professional Publishing.Google Scholar Gustavo, C., Marko, M., & Stan, C. (2018). Intelligent Digital Oil And Gas Fields Concepts, Collaboration, and Right-Time Decisions. Cambridge: Gulf Professional Publishing.Google Scholar Schlumberger. (2021, December24). Schlumberger Media. Retrieved from slb.com: https://www.slb.com/-/media/files/co/product-sheet/ultra-dual-packer-ps.ashxSchlumberger. (2021, December25). Metris. Retrieved from. slb.com: http://www.slb.com/metris
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211997-MS
+
+
+

files/2022/A Rapid Method to Predict Minimum Miscibility Pressure Through Interfacial Tension Test and Visual Observation.txt

@@ -0,0 +1,160 @@
+----- METADATA START -----
+Title: A Rapid Method to Predict Minimum Miscibility Pressure Through Interfacial Tension Test and Visual Observation
+Authors: Muslim Abdurrahman, Akhmal Sidek, Augustine Agi, Radzuan Junin, Syahrir Ridha, Nguyen Xuan Huy, Agus Arsad, Afeez Gbadamosi, Faruk Yakasai, Jeffrey Oseh, Jeffrey Gbonhinbor
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211919-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The minimum miscibility pressure (MMP) CO2 injection performs in enhanced oil recovery (EOR) method to increase sweeping efficiency and reduce oil/CO2 interfacial tension (IFT) also advantageous to the environmental in term of gas emissions for carbon capturing. There are several methods to achieve reliable value of MMP such as slim tube test, raising bubble apparatus, vanishing IFT, swelling test, and visual observation. However, these methods have certain limitations, which leads to the development of new techniques for a wide range of applications. In this paper, a rapid method that integrated IFT test with visual observation was investigated. Based on the test, the pressure is plotted against the IFT to predict the MMP for temperature 60 °C and 66 °C. In the meantime, visual observation during the test is also conducted to identify the occurrence of miscibility. The combination of both methods may provide much faster MMP prediction because the test consumes a small amount of hydrocarbon samples. The outcomes of this research clearly suggest that the MMP values resulted from IFT test and visual observation considerably agree with each other.
+
+
+
+
+Keywords:
+mmp,
+miscibility,
+predict minimum miscibility pressure,
+enhanced recovery,
+experiment,
+pet,
+upstream oil & gas,
+co 2,
+visual observation,
+injection
+
+
+Subjects: 
+Improved and Enhanced Recovery,
+Chemical flooding methods
+
+
+
+
+Introduction
+
+
+In the last several decades, CO2 injection has been proved to be a successful enhanced oil recovery (EOR) process. CO2-EOR has been applied in oil fields worldwide. Many of them are located in the USA [1], [2], [3], [4], and some others are located outside the USA [5], [6], [7]. The CO2-EOR method offers additional oil recovery between 5-20% through either miscible or immiscible process [5]. The miscible condition generally yields higher oil recovery compared to that of the immiscible method. Several mechanisms during CO2 injection that have been known to date for improving oil recovery include oil swelling, viscosity reduction, oil density reduction, and vaporizing and extracting some portions of crude oil [8].
+
+
+The minimum miscibility pressure (MMP) must be known prior to the CO2 injection to determine the miscibility of injection either in miscible or immiscible conditions. The MMP is the lowest pressure to achieve miscibility between the CO2 and the oil at a specific reservoir condition [9]. In this case, for a given reservoir temperature, determination of the miscibility condition of the injection process is subject to the MMP. Numerous methods for predicting the MMP had been developed by researchers. Experimental methods such as slim tube test, raising bubble apparatus, vanishing interfacial tension (IFT), swelling test, and visual observation are among the most common methods to apply. Other researchers have proposed numerical simulation method for predicting the MMP [10], [11]. However, each method has its own disadvantages in its methodology leading to the limitation of its application. For example, the slim tube test requires a lot of samples, long testing time, and consumes high cost. Raising bubble apparatus is subjective to interpretation, lacks quantitative evidence to satisfy the results and has some arbitrariness with miscibility interpretation.
+
+
+We apply a combined method for predicting the MMP through an IFT test in the present study. In such a test, the MMP is obtained when the IFT between the CO2 and the oil is zero [12]. Based on this concept, the plotting of IFT versus pressure can predict the MMP at a certain temperature. In the meantime, visual observation was conducted to record when the miscibility occured. This combined method has some advantages, such as predicting the MMP very rapidly, and it consumes less oil and gas samples. Also, as the IFT test result is combined with visual observation, the method provides more convincing results since the occurrence of the miscibility can be easily recognized during visual observation.
+
+
+Experimental Apparatus and procedures
+
+
+IFT Test
+
+
+The schematic for the IFT experimental test is shown in Fig. 1. Two syringe pumps from ISCO company for injecting water and CO2, a goniometer apparatus from Rame-Hart Instruments Co., combined with a visual cell have been used for the experiment. High pressure and high temperature are applied to the visual cell to measure the IFT at reservoir condition. The cell diameter is 30 mm, its height is 60 mm, and its thickness is 16 mm. The maximum operating pressure and temperature of the visual cell are 3,000 psi and 300 °C, respectively. The needle, which has 0.91 mm diameter (OD) and 50 mm length, is made from stainless steel. A pair of sapphire-glass, placed toward each other, has been equipped within the visual cell. The glass window has a thickness of 10 mm and a diameter of 30 mm. A certain volume of dead-oil is mounted into the stainless-steel piston chamber with a maximum operating pressure of 3,000 psi. Metering valve and check valve were added to ensure the constant oil flow rate and prevent the flow-back situation. The temperature was measured with a calibrated thermocouple inside the cell. Afterwards, the pressure of the system was measured with a pressure gauge. All apparatus is connected by using stainless steel tubing lines.
+
+
+Figure 1View largeDownload slideSchematic illustration of the IFT experiment [17]Figure 1View largeDownload slideSchematic illustration of the IFT experiment [17] Close modal
+
+
+Before initiating the measurement, all lines and the apparatus were cleaned using toluene, then dried using nitrogen and vacuumed. The pressure inside the cell was conditioned by injecting CO2 into the cell. Meanwhile, the temperature condition was maintained by installing heater and wrapping the view cell with heater tapes. A series of experiments were conducted with pressure conditions ranging from 700 to 2500 psi and temperature ranging from 60 °C to 66 °C. After the pressure and temperature inside the cell are constantly maintained according to the desired condition of 20-30 minutes, the water with the specific rate of 0.1 cc/min is pumped into the chamber and the piston pushed up the dead oil inside the chamber. The dead oil flows from the chamber through the tubing line until it reaches the needle's tip. When the oil drop reaches the needle's tip, the drop hang, and this condition was maintained in stabilized condition for a certain time by adjusting the metering valve. In this experiment, the stabilized condition of the drop was between 40 to 60 seconds. These times have been suggested by previous investigators [13], [14].
+
+
+The IFT measurement was done by measuring the drop image geometry captured by the camera. A good and representative drop image, which clearly separates the oil phase from the CO2 phase. The camera was connected to a monitor and a personal computer equipped with an image capture board and the image analysis software including the DROP image advance program was used to calculate the IFT. The measurement was repeated three times to ensure the certainty of the IFT values. After the measurement, all apparatus was cleaned using toluene, dried by nitrogen, and vacuumed. Finally, the determination of MMP was determined by extrapolating the linear trend line of the IFT curve as a function of pressure to the IFT value of zero.
+
+
+Visual Observation Procedures
+
+
+Observation on the occurrence of miscibility through videos or pictures of the IFT test process was conducted to predict the MMP visually. This was basically to identify the change in shape of the oil drop as the pressure increases. When the pressure in the view cell is increased, the CO2 will dissolve in the oil and accordingly the shape of the oil drop at the tip of the needle will gradually change. This phenomenon occurs until the oil drop disappears from the tip of the needle. Thus, the MMP is estimated when the oil and the CO2 become one phase, (when the oil drop disappears from the needle tip at a higher pressure). This can be distinguished visually by observation through the cell. This method needs to be developed and should be regarded only as an approximate method in estimating the MMP. However, this method is effective in recognizing when miscibility occurs.
+
+
+Results and discussion
+
+
+MMP Predictions Based on IFT Test
+
+
+Their experimental study [13] explained that the light and moderate components are rapidly extracted from the oil drop during the diffusion mechanism, causing the CO2 to be an oil-rich gas. This phenomenon leads to a decrease in the IFT between the oil and the CO2. However, when the pressure increases to the near-miscibility condition, the heavy component remains in the oil. At this condition, the oil drop starts to shrink, and the IFT reduces quite slowly. Based on the explanation provided by [14], we name the two regions created during the IFT test as follows. The first is region A, representing the diffusion stage, and the second region B, representing the shrinkage stage. In the present study, the MMP is determined by linear extrapolation of the diffusion line of the IFT versus pressure plot to the zero value of the interfacial tension. The linear regression for estimating the MMP at temperature 60 °C and 66 °C follows Equations 1 and 2.
+
+
+IFT=−0.0262×pressure+42.22(1)
+
+
+IFT=−0.0226×pressure+40.17(2)
+
+
+The Equation (1) was generated for estimating the MMP at 60 °C, and the Equation (2) was created for estimating the MMP at 66 °C. The resulted MMP by the method is acceptable with the correlation coefficient (R2) value of 0.999 for both equations. Nevertheless, this equation is applied only to the pressure range between 700 psi to 1,500 psi at 60 °C and 700 to 1,600 psi at 66 °C. If the pressure is higher than any of these pressure ranges, the equation may not be applicable due to possible different mechanisms.
+
+
+The MMP estimation under elevated pressure from the IFT test for the temperature of 60 °C and 66 °C are respectively shown in Fig. 2 and Fig. 3. Using Equations 1 and 2 at the IFT value equal to zero, the curves showed that the miscibility occur at 1,611 psi and 1,777 psi for both temperatures of 60 °C and 66 °C, respectively. Meanwhile, it also can be seen that the MMP increases as the temperature of the system increases. The incremental of MMP due to the increasing temperature from 60 °C to 66 °C is about 166 psi or 27.7 psi/°C. These results are reasonably consistent with the previous finding presented by [15]. They reported that the increment of MMP was about 22.6 psi/°C. At high temperature, the CO2 solubility in crude oil is lower, which results in higher MMP. Moreover, the slope of the IFT vs. pressure curve is slightly different. When temperature increases, the slope of the curve is larger promting a higher MMP. Fig. 3, which is for the temperature of 66 °C, shows a greater slope, (-0.0226), compared to Fig. 2, which is for the temperature of 60 °C, with the slope of -0.0262. It also suggests that the higher temperature results in higher MMP.
+
+
+Figure 2View largeDownload slideMMP from interfacial tension test at temperature of 60 °C [17]Figure 2View largeDownload slideMMP from interfacial tension test at temperature of 60 °C [17] Close modal
+
+
+Figure 3View largeDownload slideMMP from interfacial tension test temperature of 66 °C [17].Figure 3View largeDownload slideMMP from interfacial tension test temperature of 66 °C [17]. Close modal
+
+
+MMP Estimation by Visual Observation
+
+
+The value of MMP by visual observation during swelling test experiment is quite viable [16]. The method was later proved by [17] as they used it to predict the MMP in their swelling test experiment. Following the idea, in this work, we also conducted visual observation to predict the MMP. Fig. 4 to Fig.11 depict the phenomena during IFT test experiment. The oil drops indeed changes slightly as the pressure increases. In this experiment, the oil drop shape starts to change irregularly at pressures between 1650 psi to 1700 psi for 60 °C temperature. At 66 °C, the oil drop shape starts to change irregularly at pressures between 1700 psi to 1800 psi. When the oil drop changes to irregular shapes, the corresponding IFT cannot be calculated. In such state, the system is presumably near miscible condition. Furthermore, when the oil drop disappears from the tip of the needle, we assume that the miscibility state between the oil and CO2 has occurred.
+
+
+Figure 4View largeDownload slideOil drop shapes at 700 and 800 psi and 60 °C.Figure 4View largeDownload slideOil drop shapes at 700 and 800 psi and 60 °C. Close modal
+
+
+Figure 5View largeDownload slideOil drop shapes at 1000 and 1400 psi and 60 °CFigure 5View largeDownload slideOil drop shapes at 1000 and 1400 psi and 60 °C Close modal
+
+
+Figure 6View largeDownload slideOil drop shapes at of 1550 and 1650 psi and 60 °CFigure 6View largeDownload slideOil drop shapes at of 1550 and 1650 psi and 60 °C Close modal
+
+
+Figure 7View largeDownload slideOil drop shape at 1,700 psi and 60 °C.Figure 7View largeDownload slideOil drop shape at 1,700 psi and 60 °C. Close modal
+
+
+Figure 8View largeDownload slideOil drop shapes at 700 and 800 psi and 66 °CFigure 8View largeDownload slideOil drop shapes at 700 and 800 psi and 66 °C Close modal
+
+
+Figure 9View largeDownload slideOil drop shapes at 1000 and 1400 psi and 66 °C.Figure 9View largeDownload slideOil drop shapes at 1000 and 1400 psi and 66 °C. Close modal
+
+
+Figure 10View largeDownload slideOil drop shapes at 1550 and 1650 psi and 66 °CFigure 10View largeDownload slideOil drop shapes at 1550 and 1650 psi and 66 °C Close modal
+
+
+Figure 11View largeDownload slideOil drop shape at 1800 psi and 66 °CFigure 11View largeDownload slideOil drop shape at 1800 psi and 66 °C Close modal
+
+
+Conclusions
+
+
+The present study provides valuable information and draws the following conclusions:
+
+
+MMP can be determined from IFT tests and visual observation.The combine method has advantages such as less oil and gas consumption and produces rapid results within hours.The results from the IFT test and visual observation are considerably close to each other. The MMP from the visual observation method is slightly higher than that of IFT test.To obtain reasonable results, the MMP from the IFT test and visual observation should be validated by other methods such as a slim tube test or simulation.Visual observation may be considered a proper and quick method to identify the occurrence of the miscibility when CO2 is injected into oil. This method, however, should be used with caution as it is very subjective.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgment
+
+
+The authors would like to thank the Ministry of Education, Culture, Research, and Technology of Republic Indonesia, Petroleum Engineering Laboratory, Faculty of Engineering, Universitas Islam Riau and Reservoir Simulation Computer Modelling Group (CMG) Ltd, Calgary, Canada. for providing support in writing this paper.
+
+
+References
+
+
+T. E.Gill, "Ten Years of Handling Co//2 for Sacroc Unit.," Soc. Pet. Eng. AIME, SPE, pp. 1–5, 1982, 10.2118/11162-ms.Google Scholar P. W.Hansen, "A CO2 tertiary recovery pilot Little Creek field, Mississippi," Proc. – SPE Annu. Tech. Conf. Exhib., vol. 1977-Octob, pp. 1–5, 1977, 10.2118/6747-ms.Google Scholar J. F. R.Bellavance, "Dollarhide Devonian CO2 Flood: Project performance review 10 years later," Soc. Pet. Eng. – Permian Basin Oil Gas Recover. Conf. OGR 1996, no. 4, pp. 395–403, 1996, 10.2118/35190-ms.Google Scholar K. R.Pittaway and E. E.Runyan, "Ford Geraldine Unit Co//2 Flood: Operating History." Soc. Pet. Eng. AIME, SPE, no. August, pp. 19–26, 1988.Google Scholar K.Pyo, M.Powell, J.Van Nieuwkerk, and P.West, "CO2 Flooding in Joffre Viking Pool," Pet. Soc. Can. Int. Pet. Conf., pp. 1–4, 2003.Google Scholar S.Sahin, U.Kalfa, andD. Celebioglu, "Bati Raman field immiscible CO2 application: Status quo and future plans," Proc. SPE Lat. Am. Caribb. Pet. Eng. Conf., vol. 1, pp. 93–105, 2007, 10.2523/106575-ms.Google Scholar T.Ahmed, "Minimum miscibility pressure from EOS," Can. Int. Pet. Conf. 2000, CIPC 2000, 2000, pp. 1–12, 2000, 10.2118/2000-001.Google Scholar F.Stalkup and H.Yuan, "Effect of EOS characterization on predicted miscibility pressure," Proc. – SPE Annu. Tech. Conf. Exhib., pp. 377–386, 2005, 10.2523/95332-ms.Google Scholar D. N.Rao, "A new technique of vanishing interfacial tension for miscibility determination," Fluid Phase Equilib., vol. 139, no. 1-2, pp. 311–324, 1997, 10.1016/s0378-3812(97)00180-5.Google Scholar Y.Gu and D.Yang, "Interfacial tensions and visual interactions of crude oil-brine-CO2 systems under reservoir Conditions," Can. Int. Pet. Conf. 2004, CIPC 2004, pp. 1–10, 2004, 10.2118/2004-083.Google Scholar Z.Yanget al. ., "Interfacial tension of CO2 and crude oils under high pressure and temperature," Colloids Surfaces A Physicochem. Eng. Asp., vol. 482, pp. 611–616, 2015, 10.1016/j.colsurfa.2015.05.058.Google ScholarCrossrefSearch ADS  S.Gondiken, "Camurlu Field Immiscible CO2 Huff and Puff Pilot Project," Soc. Pet. Eng. AIME, SPE2007, 10.2523/15749-ms.Google ScholarCrossrefSearch ADS  L. W.Holm and V. A.Josendal, "Mechanisms of Oil Displacement By Carbon Dioxide.," JPT, J. Pet. Technol., vol. 26, pp. 1427–1438, 1974, 10.2118/4736-PA.Google ScholarCrossrefSearch ADS  N.Mungan, "Carbon Dioxide Flooding – Fundamentals.," J. Can. Pet. Technol., vol. 20, no. 1, pp. 87–92, 1981, 10.2118/81-01-03.Google ScholarCrossrefSearch ADS  A.Hemmati-Sarapardeh, S.Ayatollahi, M. H.Ghazanfari, and M.Masihi, "Experimental determination of interfacial tension and miscibility of the CO2-crude oil system; Temperature, pressure, and composition effects," J. Chem. Eng. Data, vol. 59, no. 1, pp. 61–69, 2014, 10.1021/je400811h.Google ScholarCrossrefSearch ADS  G. C.Wang, "A study of crude oil composition during CO2 extraction process," Soc. Pet. Eng. – SPE Calif. Reg. Meet. CRM 1986, pp. 423–432, 1986, 10.2523/15085-ms.Google Scholar M.Abdurrahman, A. K.Permadi, and W. S.Bae, "An improved method for estimating minimum miscibility pressure through condensation-extraction process under swelling tests," J. Pet. Sci. Eng., vol. 131, pp. 165–171, 2015, 10.1016/j.petrol.2015.04.033.Google ScholarCrossrefSearch ADS  
+
+
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+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211919-MS
+
+
+

files/2022/A Redesigned Approach for Production String Paraffin Deposit Removal Using Thermo-Mechanical Technology The Paraffin Melting Tool.txt

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+----- METADATA START -----
+Title: A Redesigned Approach for Production String Paraffin Deposit Removal Using Thermo-Mechanical Technology: The Paraffin Melting Tool
+Authors: Leonard Ogbonnia Okoronkwo, Stella Okene, Cory Kohut
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212046-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Paraffin precipitation and deposition on the internal walls of oil well production string continues to remain an age-long concern for operators especially in mature oilfield regions of the world. Paraffin or wax deposits constitute a major challenge to meeting reservoir production daily target output due to flow restriction along production string. Several options or techniques have been deployed to manage continuous wax deposition including use of hot oil, hot water, scratching and scraping, using of chemicals and even combination techniques. These techniques have yielded different results with some associated concerns ranging from safety, cost to formation damage (permeability impairment). The latest being use of thermochemical fluids to generate in-situ heat and pressure to dissolve wax and flush same from production tubing (Amjed, et al. 2019). However, this combination method presents some challenges including potential completion string corrosion and pipe integrity. This paper will discuss the performance of a field proven thermo-mechanical technique for wax removal. This technique is being currently deployed in Niger-Delta and Gulf-of-Mexico (GoM) regions with excellent results. The thermo-mechanical system utilizes battery packs to generate heat in a heating element, transfers the heat through a patented heat-transfer-fluid to a mechanical cutting head that delivers speedy melting and cutting of deposited wax. The result is a wax-free completion tubing walls.
+
+
+
+
+Keywords:
+paraffin deposit,
+completion installation and operations,
+wax inhibition,
+remediation of hydrates,
+scale inhibition,
+production chemistry,
+scale remediation,
+asphaltene inhibition,
+asphaltene remediation,
+oilfield chemistry
+
+
+Subjects: 
+Production Chemistry, Metallurgy and Biology,
+Flow Assurance,
+Inhibition and remediation of hydrates, scale, paraffin / wax and asphaltene,
+Precipitates (paraffin, asphaltenes, etc.),
+Completion Installation and Operations
+
+
+
+
+Introduction
+
+
+Crude oil majorly contains, among other components, saturated hydrocarbons or paraffins. Paraffins are high molecular weight alkanes, having twenty and above Carbon atoms. Once completion is installed and a well brought online, production continues to induce change in crude composition due to drop (change) in temperature and pressure. With increasing drop in temperature and pressure, dissolved gases, and more soluble components of crude oil separate from the crude. The result is reduction in crude solubility. As production progresses, the heavier crude components start to precipitate and get deposited either in the reservoir rock pore spaces, near-wellbore area, on the walls of production tubing, other completion accessories and along flowlines etc. Continuous precipitation and deposition of paraffins or a combination of paraffins and asphaltenes results in reduction of flow tubing diameter and overall blockage of entire flow if left unmanaged. This would mean of lost of production and associated revenue.
+
+
+Operators look out for the most suitable and cost-effective means of controlling paraffin wax deposition. Some of the techniques currently deployed in oilfields today includes use of hot oil, use of hot water, scratching/scraping (mechanical technique – running slickline scratcher followed by a gauge cutter run). Combination techniques such as use thermochemical fluids and nano chemistry options (Tukenov, 2014). Sadly, use of hot oiling and watering means continuously pumping hot oil or water at certain interval about 7-10 days. This technique also has a major concern that hinges on safely handling of heated oil/water at surface as well as well formation damage (Barker and Mansure, 1994; Khalil et.al, 1997). Similarly, scratching and cutting with gauge (paraffin) cutter, in some cases, means cutting wax every 4-7 days. Cumulatively, these methods result in high maintenance or intervention cost and merely reduce the volume of deposited wax but does not offer a complete wax-free tubing flowing diameter.
+
+
+Thermo-mechanical Paraffin Removal
+
+
+This technique is by itself a combination method. The tool generates heat, by designed, which is conducted through a heating tip (mechanical cutting head) to melt paraffin deposits found on the internal walls of completion string. It employs a combination of battery packs that can generate upto 400W of power, a heating element/control board, designed to sustain heat at the cutting head in a chambered paraffin melting tool (PMT). The power generated is converted to heat within a heating element that forms an integral part of the melting tool. Ultimately, the heat is conducted via a specially designed heat-transfer-fluid (Dynalene) contained in a micro annulus chamber screwed to a spiral/sharp cutting head. The heat conducted to the cutting head generates temperatures up to 455° F. This temperature being well above paraffin melting point (circa 150° F) can melt an entire deposit of wax allowing a vertical well to flow or produce wax-free for extended time. The effectiveness of this thermo-mechanical method validates the conclusion of Guerreiro et. al (2019) regarding the widespread adoption of mechanical and combination methods in the removal of paraffin deposits in flowing vertical wells as well as use of innovative heating devices (Al-Yaari and Fahd 2011). Below shows a solid design piece of the paraffin melting tool, a typical innovative heating device that offers effective thermo-mechanical removal of paraffin wax, a patented design of Sagerider Incorporated.
+
+
+Figure 1View largeDownload slideSagerider paraffin melting toolFigure 1View largeDownload slideSagerider paraffin melting tool Close modal
+
+
+SageRider Paraffin Melting Tool (PMT) Components and Design
+
+
+This section discuses the design and composition of the game-changing paraffin melting tool designed and patented by SageRider Incorporated (SRI). The innovative design of this tool makes it a specific solution targeted at melting all paraffin and asphaltene deposits along the internal walls of a production string.
+
+
+From end-to-end (top to bottom), the tool is a patented battery-powered tool designed to be conveyed on all forms of wireline (slickline, braided line & e-line). The system utilizes heat and a steel cutting head (thermo-mechanical) to remove the paraffin from the production tubing wall and bring tubing back to full drift.
+
+
+From top, the tool has a top sub (with a fishing neck) designed to adapt unto slickline, braided line or e-line tool string. In sequence, the top sub connects to a battery pack housing. The battery pack housing comes modular in design to allow a field technician the freedom to run one or more battery packs. The battery pack is operatively (internally) connected to a heating element, contained in a heating element housing. Physically, the heating element housing is connected to the battery pack housing with a pin-pin bridge connector equipped with elastomeric seals on both pin ends. To ensure controlled power delivery from the battery pack to the heating element, an electrical control board is designed, retained in a control board-housing, and positioned between the battery pack and the heating element. The control board makes it possible for the battery pack to maintain a predetermined temperature at the heating element during operation of the tool. To protect the control board electronic components from potential damage from the heat generated at the heating element, the tool is equipped with specially made insulating material placed between the board and the upper section of the heating element.
+
+
+The heating element housing is designed to have fluid chambers filled with a heat transfer fluid (Dynalene). The electrical energy generated by the battery packs, transferred to the heating element through the control board, is converted to heat in the heating element. The heat diffusing from the element heats up the heat transfer fluid, which further conducts (transfers) the heat to the heating element housing. The tool design terminates with a heating tip (cutting head or spear). The cutting head has a threaded pin that makes up directly to the heating element housing box connection. Incorporated in the design of the heating tip, is a fluid chamber filled with Dynalene heat transfer fluid. The heat transfer fluid transfers heat to the heating element housing and the heating (cutting head) tip. When the tool is deployed down a completion string, the sharp or speared cutting head (heating tip) is capable of dissolving and cutting through paraffin or any asphaltene deposit along its way.
+
+
+This innovatively designed tool uses wire leads to connect from the control board to the battery packs and ensure leak-proof connections by utilizing specially designed elastomeric seals in all connections from the top sub to the cutting head.
+
+
+Battery Pack Design
+
+
+The battery pack is a long cylindrical member designed and modularly connected with pin-pin bridge connectors as required. Commonly, it comprises rechargeable Lithium-ion batteries of type 18650. In a typical design of the tool, the batteries are connected in parallel and managed by a control board. In some configurations of the PMT, a total of thirty-six type 18650 batteries are contained in a battery pack resulting in 4ft pack capable of generating about 48 volts and 9 amperes of voltage and current respectively. Notably, different battery pack configuration is possible due to field conditions, heat, and temperature requirement. Typically, the battery pack configuration is designed and targeted to accommodate a reasonable operating time downhole (1.5 – 2 hours); and ability to maintain the heating tip at temperatures above 350-degree Fahrenheit to be able to melt any paraffin deposit (Paraffins typical melts at 150-degree Fahrenheit).
+
+
+Control Board and Heating Element
+
+
+Cost effective operation incentive rationalized the design of the control board. The control board is designed to fit in a relatively small housing about 1.25 inches. This design reduces the frequency of replacing the rechargeable battery pack; hence, making the operation of the tool cost effective. From its narrow resident space, the board is designed to operate large heating element of four-hundred watts (400 W) and above without damaging the battery pack. Overall, the control board delivers all the required controls needed to ensure efficient operation of the PMT. Its circuit design delivers pulse (while using between 30 mA at resting period and 9A full operation consumption respectively) to the battery pack allowing the battery pack to rest at predetermined interval for chemical stabilization and long-time performance while in operation.
+
+
+Heating Tip (Cutting Head)
+
+
+As noted earlier, the heating tip is a steel member that typically comes as either a spear- headed or spiral-cutting edged head. The design allows the heating tip or head to melt and cut through paraffin deposit while running in hole with the tool. In some designs, a modified gauge cutter with a heat transfer fluid chamber is fitted to the heating element housing to serve as a cutting head. In such design, the gauge cutter heats and cuts through paraffin deposit and provide a passage for the melted paraffin deposit to flow downstream from the cutting depth.
+
+
+Application and Benefits
+
+
+The PMT, a typical thermo-mechanical technology for paraffin or asphaltene deposit removal finds good applications both onshore and offshore where wax removal intervention is required to improve production flow performance. The tool can be deployed on slickline, braided line and e-line tool string, hence an operator is not limited to one deployment option. Its temperature capability makes it possible for an operator to melt, remove and restore full production drift bringing production back to 100%. The technology is cost effective and significantly reduces operating time. It melts and removes paraffin deposit within 60 minutes, an operation that hitherto will require 4-8 hours in a typical mechanical wax cutting operation. Logistics is minimized and easily managed; the tool is about eleven feet (11ft) for a two-48volt battery pack design but comes in a modular stack in a pelican case. The charging accessories are quite portable allowing ease of transfer within and between locations. The battery pack is fully charged within an hour, making back-up battery ready for use as required. The cutting head is field-changeable, field crew can easily change the cutting heads depending on production tubing internal diameter; from 2-3/8" to 4-1/2".
+
+
+Performance of Thermo-mechanical Paraffin Melting Tool (PMT) in the Niger-Delta
+
+
+The PMT was first deployed February 2022 for an operator in the Niger-Delta region by Stelog following more than six months engagement and expected technology performance review. The operator identified some candidate wells during a well intervention campaign. To date, a total of five wells have been de-waxed using this thermo-mechanical technology. The flow performance of all five wells has remained stable for more than ninety (90) days. True evidence that a full drift completion string was re-established following the deployment of PMT on these wells. The paraffin melting tool is a game-changer in paraffin melting and removal today due to its efficiency (safe, reduced logistics, low cost, no damage to formation) and significant timesaving.
+
+
+Among the five candidate wells, one was delivered using two packs of battery in the PMT battery pack housing. With each battery fully charged to 54V, a total of 108V battery pack was deployed. Additional battery pack would result in slight increase in time to total discharge downhole. However, with 108volts battery, about 1.5 hours downhole resident before discharge is achievable and 455°F cutting head temperature guaranteed.
+
+
+In the first well, paraffin deposit was encountered slightly deeper than 200ft. From this dept, a paraffin-free dept was established slightly above 3,780ft in forty-five (45) minutes. With subsequent wells, it was confirmed that with this thermo-mechanical technology, paraffin deposits are melted and produced to surface, with well fluids, in less than sixty (60) minutes. This performance level dwarfs, significantly, other available techniques deployed in the removal of paraffin deposits. Traditionally, most mechanical paraffin cutting options, on slickline, take between 4-8 hours or more to complete paraffin deposit removal depending on the total waxed interval in the completion string.
+
+
+One of the candidate wells transitioned from a flowing tubing head pressure (FTHP) of less than 50 psi to a stabilized FTHP of about 950 psi over a period of ninety (90) days. Ninety days continuous production with no form of flow restriction is significant following less than twenty-four hours slickline intervention using the PMT. The result from this intervention operation is in line with the benefits discussed above.
+
+
+Conclusion
+
+
+It is safe to conclude that thermo-mechanical techniques offer the best option for paraffin deposit removal in onshore and shelf (shallow water) rigless paraffin flow restriction/plugging interventions. Specifically, the Sagerider paraffin melting tool (PMT) delivers a state-of-the-art performance in paraffin or asphaltene deposit removal operation. The tool is available in sizes for up to 4-1/2" production strings. The technology’s ability to generate more than 450°F temperature guarantees full-drift production string in less than 60 minutes of deployment. The technology offers a specially designed handling bag for handling the heated cutting head on surface before running in hole and after it is pulled out of hole. The modular design of the PMT battery pack housing makes it possible to run more than one battery pack. Multiple wells can be dewaxed in a day with more chargeable battery packs on location. To be sure of the temperature generated at cutting head, the system package comes with a surface temperature readout as one of its accessories. This thermo-mechanical technology is safe, does not damage formation (when compared to hot oiling or watering or even thermo-chemical fluids), saves time, less expensive and offers full-drift production string for hundred percent production rates restoration.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Amjed, H., Olalekan, A., Mahomed, M. and Adbulaziz, A. (2019). A Novel Technique for Removing Wax Deposition in the Production System Using Thermochemical Fluids. Paper presented at the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, November 2019.Paper Number: SPE-197323-MShttps://doi.org/10.2118/197323-MSGoogle Scholar Al-Yaari, M., and Fahd, K. (2011). Paraffin wax deposition: mitigation and removal techniques. SPE Int 155412(March):14–16Google Scholar Barker, K.M., and Mansure, A.J. (1994). Practical hot oiling and hot watering for paraffin control. Available at: https://www.osti.gov/biblio/10127994. [Retrieved 15 March 2022]Google Scholar Guerreiro, L.P., MatosH.A., and SousaA.L. (2019). Preventing and removing wax deposition inside vertical wells: a review. Journal of Petroleum Exploration and Production Technology (2019) 9:2091–2107https://doi.org/10.1007/s13202-019-0609-xGoogle Scholar Khalil, C.N., Rocha, N.O., and Silva, E.B. (1997). Detection of Formation Damage Associated to Paraffin in Reservoirs of the Recôncavo Baiano. Presented at the International Symposium on Oilfield Chemistry, Houston, 18–21 February. SPE-37238-MS. http://dx.doi.org/10.2118/37238-MSGoogle Scholar TukenovD. (2014). Technology Update: Nanochemistry Drives New Method for Removal and Control of Wax. J Pet Technol66 (12): 30–33. Paper Number: SPE-1214-0030-JPThttps://doi.org/10.2118/1214-0030-JPTGoogle ScholarCrossrefSearch ADS  
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212046-MS
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files/2022/A Review of Natural Polysaccharides as Corrosion Inhibitors Recent Progress and Future Opportunities.txt

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+----- METADATA START -----
+Title: A Review of Natural Polysaccharides as Corrosion Inhibitors: Recent Progress and Future Opportunities
+Authors: Pearl Isabellah Murungi, Aliyu Adebayo Sulaimon, Oscar Ssembatya, Princess Nwankwo
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211964-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Preventing and mitigating corrosion problems can be very challenging due to technical considerations and prohibitive economic implications. It is thus imperative to arrest the escalating corrosion rates and impede the deterioration effects of corrosion with versatile remedies. In this review, previous research efforts on the application of plant-derived polysaccharides as potential inhibitors of metal corrosion in various aggressive media are studied. The deployment of corrosion inhibitors has proven to be an outstanding solution to prolonging the lifespan of metals. However, the most applied inhibitors such as the inorganic and some organic compounds are prohibitively expensive, hazardous, and toxic. These limiting factors have stimulated interest in more research into greener and less toxic natural alternatives. Considering the success of synthetic polymers for corrosion inhibition, a wide range of plants with high natural polysaccharide content have been evaluated to determine their effectiveness as biodegradable, renewable, and more economical corrosion inhibitors. Studies generally show that natural polysaccharides exhibit over 90% efficiency for corrosion inhibition with appreciable adsorption on the metal surface. Modification and grafting of the plant polysaccharides to enhance their inhibition efficiencies and to make them more desirable are currently being investigated. Such bio-inspired polymeric molecules thus have invaluable significance as potential alternatives for the problematic corrosion inhibitors.
+
+
+
+
+Keywords:
+h2s management,
+corrosion management,
+subsurface corrosion,
+water management,
+corrosion inhibition,
+steel,
+extract,
+quraishi,
+riser corrosion,
+materials and corrosion
+
+
+Subjects: 
+Production Chemistry, Metallurgy and Biology,
+Pipelines, Flowlines and Risers,
+Materials and corrosion,
+Well Integrity,
+Subsurface corrosion (tubing, casing, completion equipment, conductor),
+Corrosion inhibition and management (including H2S and CO2)
+
+
+
+
+Introduction
+
+
+The phenomenon of metal corrosion is still a prevalent hazard in a plethora of industries. Without exception, the oil and gas industry regularly deals with endless costly corrosion problems because it consumes a tremendous amount of tubings and pipes during production, transportation, and storage. Corrosion in simple terms is the deterioration of the metal exposed to an aggressive agent. Technically, it can howbeit be explained as a ruinous aftereffect of an electrochemical redox reaction between the material of the exposed metal and the aggressive agent that may cause its deterioration (Goni & Mazumder, 2019). In oil production, the material of the corrosion-prone downhole and surface metallic tubulars, valves, and tools, becomes even more susceptive to oxidation reactions during acidizing stimulation, pickling, and cleanup activities (Ali & Suleiman, 2018; Govindasamy & Ayappan, 2015). Iron, steels, aluminium, zinc, and copper are some of the commonly used metals/ alloys that are susceptible to corrosive debacles. Such metal loss may be explicitly classified as general, galvanic, localized, erosion, pitting, crevice, microbially-influenced, or stress cracking corrosion (Fontana & Greene, 2018).
+
+
+According to Javaherdashti (2000), over 96 tons of steel react every day to form rust yet approximately 50% of every ton of the globally produced steel is purposed to replace tainted metal. The impact of corrosion on equipment and its circumambient environment is apparent and deserves a massive amount of attention. Eradicating corrosion is not an uphill task. Similarly, arresting its escalating effects has never been a walk in the park. Corrosion mitigation using contemporary and pragmatic remedies should therefore be an indispensable aspect, deliberately and comprehensively planned for in the current undying industrial society (Ropital, 2009; Verma et al., 2021). The possible corrosion costs, by and large, incurred to deal with corrosion are summarized in Fig.1 below.
+
+
+Figure 1View largeDownload slideThe breakdown of corrosion costs (modified from (Goni & Mazumder, 2019))Figure 1View largeDownload slideThe breakdown of corrosion costs (modified from (Goni & Mazumder, 2019)) Close modal
+
+
+For the past couple of years, the approach to corrosion has involved using methods like corrosion-resistant alloys, coatings, dehydration or pigging, pH stabilization, O2/H2S scavengers, corrosion inhibitors, biocides, and drag reduction. However, corrosion inhibitors (CIs) have been deemed more pragmatic, efficient, and economical in industrial facilities when it comes to maintaining pipeline integrity (Kelland, 2014; Saji & Umoren, 2020).
+
+
+Corrosion inhibitors
+
+
+Corrosion inhibitors are substances that when added to an aggressive solution in relatively minute concentrations can decelerate or deter any corrosion activity by generating a protective film that insulates the metal (Alrefaee et al., 2020). Oodles of considerations are tabled when selecting an inhibitor. These factors may encompass price tag, availability, reliability, concentration, but most importantly safety (Ayoola et al., 2020; Heidarshenas et al., 2020). Ideally, an effective corrosion inhibitor, as represented in Fig. 2, must be extremely economical, highly compatible with the aggressive medium, yield a desirable outcome when applied in small concentrations and also concur immensely with the environmental standards.
+
+
+Figure 2View largeDownload slideQualities of an effective Corrosion InhibitorFigure 2View largeDownload slideQualities of an effective Corrosion Inhibitor Close modal
+
+
+The commonly applied inhibitors are either inorganic or organic. Most of them have demonstrated decent results regarding solving corrosion problems for different metals in oil pipelines. But be that as it may, many of them are relatively costly, inaccessible, or ecologically detrimental (Abdel-Gaber et al., 2020; Khaleda et al., 2020; Lebrini et al., 2020; Neriyana & Alva, 2020; Xiang et al., 2021). Erstwhile, it was not uncommon for past researchers to study every other inhibition alternative except the natural polymers. Contemporary studies however have garnered their efforts toward exploring alternate sources that are non-toxic neither to the man nor to the environment. Thus far, attention has been directed toward employing eco-safe, cost-efficient, and natural inhibitors in the oil and gas industry (Zakaria et al., 2022). This review article, therefore, analyses the progress and future opportunities available for metal surface corrosion inhibition using natural plant polysaccharides. The review also highlights their respective inhibition modes and mechanisms, the environments and testing techniques, optimum polysaccharide concentrations, adsorption types, and their measured inhibition performance.
+
+
+Polymeric Corrosion Inhibitors
+
+
+A myriad of studies has proved the great performance of polymers when it comes to metal deterioration. They bear functional groups that form complexes with ions of metals, thus blanketing the metal surface from aggressive solutions (Arthur et al., 2013; Rizvi, 2021). Polymers also possess several possible attachment sites, have a lot of flexibility with derivation, and have a great potential to surpass the conventional macromolecule inhibitors (Tiu & Advincula, 2015). Polymeric corrosion inhibitors can either be natural or synthetic. The known hazardous effects of most synthetic polymeric corrosion inhibitors have motivated researchers to explore naturally occurring polymers as inhibitors (Biswas et al., 2017).
+
+
+Biopolymers are a form of naturally occurring polymeric compounds generated by the cells of plants and animals. They are gaining apparent attention because they are environment-considerate and tolerable. Their sources are readily available in extensive amounts, and they bear several electron-rich sites that trigger their excellent anticorrosive activity (Verma & Quraishi, 2021). These adsorption sites enable them to bond with the metal surface and cover a wide area. The molecules are also large in size and have heteroatoms such as oxygen which donate electron lone pairs (Arthur et al., 2013; Palumbo et al., 2019; Shahini et al., 2021; Vaidya et al., 2021). Polysaccharides, natural rubber, nucleic acids, polypeptides, and lignin are some of the common biopolymers (Verma et al., 2021). Polysaccharides are further classified as starch, cellulose, chitin, dextrin, and chitosan as illustrated in Fig. 3 below. Some natural polymers have further been experimented with to produce graft copolymers, especially from plant gums such as gum Arabic, guar gum, xantham gum, etc (Vaidya et al., 2021).
+
+
+Figure 3View largeDownload slideCategories of Corrosion Inhibitors (modified from (Murungi & Sulaimon, 2022))Figure 3View largeDownload slideCategories of Corrosion Inhibitors (modified from (Murungi & Sulaimon, 2022)) Close modal
+
+
+Natural Plant Polysaccharides as Corrosion inhibitors
+
+
+Plant extracts contain a blend of components such as proteins, polysaccharides, polycarboxylic acids, tannins, alkaloids, flavonoids, glycosides, and terepinoids which have proven to contribute to the high efficiency observed during corrosion inhibition (Dahmani et al., 2010; Huang et al., 2022; Ugi et al., 2018; Verma et al., 2018). Table 1 provides a summary of the recent applications of biopolymers and their derivatives for the inhibition of corrosion. The respective inhibition modes, mechanisms, corrosive media, testing techniques, optimum inhibitor concentrations, adsorption types, and their measured inhibition performance are also highlighted. It is summarily observed from Table 1 that the plant polysaccharides displayed high inhibition efficiencies, the majority over 85%, with adsorption behavior mostly involving both chemical and physical processes. (Umoren & Eduok, 2016) attributed their growing demand to this characteristic of simple chemistry.
+
+
+Table 1Recent experimental studies on natural plant polysaccharides used as corrosion inhibitors for Steel, Aluminium, and Magnesium. Polysaccharide source
+            . Alloy/Inh. Conc
+            . Test Condition
+            . Absorption Isotherm & Method
+            . Adsorption Mechanism
+            . Mode of inhibition
+            . Inhibition efficiency (ȵ%)
+            . Ref.
+            . Aloe polysaccharide (APS) Mild steel 15% HCl 800 mg/L APS at 30–60 °C Langmuir Mixed type WL, EIS, SEM, EDX, AFM) Glucose, mannose, galactose and fructose mainly 96.12% (Zhang et al., 2020) Adlay seed hull polysaccharide (ASP) Mild steel 1 M HCl at 800 mg L−1 Langmuir Physio-chemisorption Adsorption of ASP on the steel surface. 94.70% (Chen et al., 2021) Pig cartilage (CS-PC) and sodium alginate (SA) Mild steel 1 M HCl  Weight loss test, EIS, SEM, SECM, and UV Chondroitin sulfate 95.18 % (Zhang, Nie, et al., 2021) Apostichopus japonicus (AJPS) Mild steel 625 mg/L 35 °C. Weight loss, electrochemical, SEM, AFM Physio-chemisorption - 96.03% (Zhang, Wu, et al., 2021) Guar gum and methylmethacrylate (GG-MMA) composite P110 steel 3.5% NaCl with CO2 at 50 °C Weight loss, EIS, PDP Physio-chemisorption Hydroxyl groups 96.8%. (Singh et al., 2020) Methyl Cellulose (MC) polysaccharide Mg metal 0.1 M HCl 40 °C Langmuir Freundlich FT-IR, SEM Physio-chemisorption OH bridges between the substrate and metal surface 90.06 % (Hassan & Ibrahim, 2021) Starch Solution Mg alloy 3 % NaCl Tafel, SEM, DX, Mixed type  85% (Babu et al., 2021) Xanthan gum with Potassium-sodium tartrate Carbon steel 3 % NaCl Langmuir EIS  Single or double complexes with iron cations by their molecules. 90%. (Korniy et al., 2022; Korniy et al., 2021) Herbal expired drug bearing glycosides and polysaccharides moieties Carbon steel 1.2 g/l 1.0 M HCl Langmuir (EIS), PDP Mixed type Inulins and saccharose 97.5% (Zakaria et al., 2022) Pectin from Ecuadorian Citrus (Tahiti lime & King mandarin) Peels Carbon steel 400 ppm 0.1 M NaCl Room temperature Linear polarization and weight loss methods Physio-chemisorption Methyl ester groups heteroatoms 78.21% (Núñez-Morales et al., 2022) Polysaccharide source
+            . Alloy/Inh. Conc
+            . Test Condition
+            . Absorption Isotherm & Method
+            . Adsorption Mechanism
+            . Mode of inhibition
+            . Inhibition efficiency (ȵ%)
+            . Ref.
+            . Aloe polysaccharide (APS) Mild steel 15% HCl 800 mg/L APS at 30–60 °C Langmuir Mixed type WL, EIS, SEM, EDX, AFM) Glucose, mannose, galactose and fructose mainly 96.12% (Zhang et al., 2020) Adlay seed hull polysaccharide (ASP) Mild steel 1 M HCl at 800 mg L−1 Langmuir Physio-chemisorption Adsorption of ASP on the steel surface. 94.70% (Chen et al., 2021) Pig cartilage (CS-PC) and sodium alginate (SA) Mild steel 1 M HCl  Weight loss test, EIS, SEM, SECM, and UV Chondroitin sulfate 95.18 % (Zhang, Nie, et al., 2021) Apostichopus japonicus (AJPS) Mild steel 625 mg/L 35 °C. Weight loss, electrochemical, SEM, AFM Physio-chemisorption - 96.03% (Zhang, Wu, et al., 2021) Guar gum and methylmethacrylate (GG-MMA) composite P110 steel 3.5% NaCl with CO2 at 50 °C Weight loss, EIS, PDP Physio-chemisorption Hydroxyl groups 96.8%. (Singh et al., 2020) Methyl Cellulose (MC) polysaccharide Mg metal 0.1 M HCl 40 °C Langmuir Freundlich FT-IR, SEM Physio-chemisorption OH bridges between the substrate and metal surface 90.06 % (Hassan & Ibrahim, 2021) Starch Solution Mg alloy 3 % NaCl Tafel, SEM, DX, Mixed type  85% (Babu et al., 2021) Xanthan gum with Potassium-sodium tartrate Carbon steel 3 % NaCl Langmuir EIS  Single or double complexes with iron cations by their molecules. 90%. (Korniy et al., 2022; Korniy et al., 2021) Herbal expired drug bearing glycosides and polysaccharides moieties Carbon steel 1.2 g/l 1.0 M HCl Langmuir (EIS), PDP Mixed type Inulins and saccharose 97.5% (Zakaria et al., 2022) Pectin from Ecuadorian Citrus (Tahiti lime & King mandarin) Peels Carbon steel 400 ppm 0.1 M NaCl Room temperature Linear polarization and weight loss methods Physio-chemisorption Methyl ester groups heteroatoms 78.21% (Núñez-Morales et al., 2022) View Large
+
+
+Biopolymeric molecules have invaluable significance as potential substitutes for problematic corrosion inhibitors. However, future researchers have several considerations to give thought to. Before a natural polysaccharide is deemed efficient and safe, it is imperative to first run it through certain tests for example toxicity and bioaccumulation checks. Bioaccumulation is estimated by the partition coefficient method while toxicity is pre-investigated by mortality tests (Biswas et al., 2017). Longevity is another aspect that needs to be factored in while developing such inhibitors which are highly biodegradable and difficult to store up for a good length of time. The choice of extraction method and solvent used for extraction are other important factors. Seasonal availability and competition for sources of food with livestock and humans also pose a great threat to plant polysaccharides used for inhibition. Not to mention, the uncontrollable hydration rate, microbial contamination, and pH-dependent solubility and thermal stability (Arthur et al., 2013; Vaidya et al., 2021). Chemical modification with other polymers may be utilized to attempt to improve their performance using methods such as grafting, crosslinking, and nanocomposites (Banerjee et al., 2012; Mobin et al., 2017). Other researchers may also need to venture more into plant residue-extracted sources of polysaccharide materials, synergism with halide ions, and the addition of surfactants. These may all be considered to augment anticorrosion efficiencies so that the use of biopolymers is even more desirable (Arthur et al., 2013; Núñez-Morales et al., 2022; Roy et al., 2014; Vaidya et al., 2021).
+
+
+Conclusion
+
+
+A range of plants with high natural polysaccharide content has been reviewed and assessed for corrosion inhibition, in the light of the prevalent success of synthetic polymers. Previous studies have revealed that they are effective, biodegradable, renewable, and economical corrosion inhibitors. The efficiencies exhibited by these natural plant polysaccharides are usually over 90% with excellent surface cover on the metal surface. In their composition are the OH groups, heteroatoms, multiple adsorption centers, good film-forming capabilities, and surfactant nature which immensely enhances the formation of a protective barrier thereby reducing the likelihood of the corrosion reaction. It is recommended that the subsequent future investigations by contemporary researchers should have their focal point beyond the lab parameters revealed herein and focus more on commercializing and scaling up of the tested plant polysaccharides for mass industrial production. This will attract investment and make such research worthwhile.
+
+
+Author declaration
+
+
+The author(s) declare no potential conflicts of interest concerning the research, authorship, and/or publication of this article.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgments
+
+
+The authors would like to acknowledge the Malaysia Ministry of Higher Education for funding this research through the Fundamental research grant scheme, FRGS (Cost Centre: 015MA0-082) through Universiti Teknologi PETRONAS.
+
+
+Nomencalture
+
+
+NomencaltureAbbreviationExpansion AASAtomic Absorption Spectroscopy AFMAtomic Force Microscopy CIsCorrosion Inhibitors EDAXEnergy-dispersive X-ray analysis EDSEnergy-Dispersive Spectroscopy EDXElectron Dispersive X-Ray Spectroscopy EISElectrochemical Dynamic Impedance Spectroscopy FE-SEMField Emission Scanning Electron Microscope PDPPotentiodynamic Polarization SEMScanning Electron Microscopy SKPScanning Kelvin Probe Test UHPLCUltra-High Pressure Liquid Chromatography UV–VisUltraviolet-Visible Spectrophotometry XRDX-Ray Diffraction Analysis
+
+
+References
+
+
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Herbal expired drug bearing glycosides and polysaccharides moieties as green and cost-effective oilfield corrosion inhibitor: Electrochemical and computational studies. Journal of Molecular Liquids, 352, 118689.Google ScholarCrossrefSearch ADS  Zhang, W., Ma, Y., Chen, L., Wang, L.-J., Wu, Y.-C., & Li, H.-J. (2020). Aloe polysaccharide as an eco-friendly corrosion inhibitor for mild steel in simulated acidic oilfield water: Experimental and theoretical approaches. Journal of Molecular Liquids, 307, 112950.Google ScholarCrossrefSearch ADS  Zhang, W., Nie, B., Li, H.-J., Li, Q., Li, C., & Wu, Y.-C. (2021). Inhibition of mild steel corrosion in 1 M HCl by chondroitin sulfate and its synergistic effect with sodium alginate. Carbohydrate Polymers, 260, 117842.Google ScholarCrossrefSearch ADS PubMed Zhang, W., Wu, Y.-C., & Li, H.-J. (2021). Apostichopus japonicus polysaccharide as efficient sustainable inhibitor for mild steel against hydrochloric acid corrosion. Journal of Molecular Liquids, 321, 114923.Google ScholarCrossrefSearch ADS  
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211964-MS
+
+
+

files/2022/Achieving Safety at Sea Discussing the Safety Programs Implemented by the Nigerian Upstream Petroleum Regulatory Commission.txt

@@ -0,0 +1,178 @@
+----- METADATA START -----
+Title: Achieving Safety at Sea – Discussing the Safety Programs Implemented by the Nigerian Upstream Petroleum Regulatory Commission
+Authors: Okechukwu Nwankwo, Michael Edem, Jennifer Muku, Chidi Ike, Ebipador Ogionwo
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211954-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Over 70% of Nigeria's oil and gas reserves are in swamp and offshore environments with over 40,000 workers registered to work there. Following the signing of the Petroleum Industry Bill into law, the Nigerian Upstream Petroleum Regulatory Commission (NUPRC) as the successor agency of the Department of Petroleum Resources (DPR) is the upstream industry regulator mandated to drive several safety programs to protect people, environment, and assets through enforcement of laws and regulations. The aim of this paper is to discuss the various safety programs adopted by Commission to reduce accidents in swamp and offshore areas, in which bulk of the oil and gas operations occur. A detailed review of the programs showed that in addition to protection of people, environment and asset, safety programs drive cost savings in the industry, improves collaboration among operators, creates jobs and other economic opportunities in Nigeria. This paper will discuss in detail, the background, methodology, successes, challenges, and opportunities of some flagship safety programs of the Commission. The programs to be discussed are - Administration of Offshore Safety Permit; Implementation of Safety Case; Annual Facility Inspection and Oil Spill Contingency Plan; Risk Based Inspection; Safety and Emergency Training Center and Medical Center Accreditation and Search Rescue and Surveillance Program. This paper only gives insight into the management of safety in the Nigerian oil and gas industry and does not attempt to review the performance or effectiveness of these safety programs vis-à-vis accident statistics in the industry. The various safety programs can be adopted by regulators around the world most especially in countries with a nascent oil and gas industry.
+
+
+
+
+Keywords:
+upstream oil & gas,
+nigeria,
+emergency training center,
+contingency planning,
+nigerian upstream petroleum regulatory commission,
+centre,
+safety risk management,
+operation,
+personnel,
+nigerian oil
+
+
+Subjects: 
+HSSE & Social Responsibility Management,
+Safety,
+HSSE management systems,
+Contingency planning and emergency response,
+Safety risk management
+
+
+
+
+Introduction
+
+
+The Petroleum Industry Act (PIA) which came into effect in August 2021 provides legal, governance, regulatory and fiscal framework for the Nigerian petroleum industry, the development of host communities, and for related matters. The PIA put in place two industry regulators, the Nigeria Upstream Petroleum Regulatory Commission (NUPRC) and Nigerian Midstream and Downstream Petroleum Regulatory Authority (NMDPRA) for the Nigerian oil and gas sector. The NUPRC which was formed from the relevant SBUs of the defunct Department of Petroleum Resources (DPR) regulates the activities of the upstream sector. In the same vein, the NMDPRA which was formed from the relevant SBUs of the defunct Department of Petroleum Resources (DPR), Petroleum Product Pricing Regulatory Agency (PPPRA) and Petroleum Equalization fund (PEF) regulates the activities of the midstream and downstream of the industry. Figure 1 below depicts the regulatory institutional transition that took place as enacted in the PIA 2021.
+
+
+Figure 1View largeDownload slideRegulatory institutional transition as per PIA 2021Figure 1View largeDownload slideRegulatory institutional transition as per PIA 2021 Close modal
+
+
+According to the PIA, the Commission is the technical and commercial regulator of the upstream sector and oversees the management of petroleum reserves, exploration, development, and production activities within the onshore frontier basins, shallow water and deep offshore. In addition, administration, enforcement, and implementation of laws, policies and regulations as it relates to upstream petroleum operations in Nigeria is vested in the Commission. With respect to oil production, Nigeria is the largest producer in Africa, 7th largest producer in the world and produces sweet crude blend such as Bonny Light, Forcados, Escravos Crude etc. Figure 2 shows the reserves and crude oil production profiles in Nigeria.
+
+
+Figure 2View largeDownload slideNigeria's crude oil reserves and production data [1]Figure 2View largeDownload slideNigeria's crude oil reserves and production data [1] Close modal
+
+
+The management of this industry in Nigeria are not without its attendants HSE issues. Consequently, based on best practices, industry experience, lessons learnt from incidents, the Commission regularly develops safety programs to reduce and eliminate HSE incidents. The aim of this paper is to discuss the various safety programs adopted by Commission to reduce accidents in swamp and offshore areas, in which bulk of the oil and gas operations occur. A detailed review of the programs showed that in addition to protecting people, environment and asset, safety programs drive cost savings in the industry, collaboration among operators, job creation and economic opportunities for Nigeria. This paper discusses in detail, the background, methodology, successes, challenges, and opportunities of some flagship safety programs of the Commission. The programs to be discussed are - Administration of Offshore Safety Permit; Implementation of Safety Case; Annual Facility Inspection and Oil Spill Contingency Plan; Risk Based Inspection; Safety and Emergency Training Center and Medical Center Accreditation and Search Rescue and Surveillance Program. This paper only gives insight into the management of safety in the Nigerian oil and gas industry and does not attempt to review the performance or effectiveness of these safety programs vis-à-vis accident statistics in the industry. The various safety programs can be adopted by regulators around the world most especially in countries with a nascent oil and gas industry.
+
+
+Overview of the Nigerian Upstream Sector
+
+
+Nigeria's proven crude oil and gas reserves are estimated to be 37.046Bbbls and 208.62Tcf respectively placing the country 2nd in oil reserves and 1st in gas reserves in Africa [2]. Offshore and swamp areas have about 70% of oil and gas reserves which is the largest share of reserves as shown in Figure 3. The rig count is an index used to measure the frequency of exploration and development activities of oil and gas fields and is shown in Figure 4. Between 2010 – 2019, the rig count was on the increase until 2014 when it dipped by 37% from the preceding year and continued declining for two years. This was caused by fall in crude oil prices which forced oil and gas producers to cut their capital budgets. The rig count rebounded in 2017 and recorded its highest growth in 2019. Between 2010-2019, seismic acquisition in Nigeria has been most prominent in the deep offshore region as shown in Figure 5. Insecurity in the land/shallow water assets and marginal economic value compared to deep offshore prospects have seen IOCs invest heavily in exploration and development of deep offshore assets while gradually divesting their land/shallow water assets to indigenous companies. Oil and gas operations constitute high risk activities; incidents from these activities can result to loss of containment, fire, or other top events, which can potentially lead to painful consequences that includes serious injury, loss of lives, damage to the environment and assets. Over 40,000 workers are registered to work in offshore and swamp areas creating a high concentration of personnel in those areas [3]. The massive number of oil and gas workers in swamp/offshore areas, presence of Major Accident Hazards (MAH) which is characteristic of oil and gas operations, and the significance of revenues derived from the industry, emphasizes the need for the regulator to implement industry safety programs that will protect lives, environment, and assets, thereby ensuring sustainability of operations. Extracts from the 2019 Nigerian Oil and Gas Annual Industry Report shows that the upstream sector recorded 213 fatal accidents and 285 fatalities averaging 28 fatalities in a year, as seen in Figure 6. Figure 7 displays oil spill incidents that have occurred within the same period.
+
+
+Figure 3View largeDownload slideOil & Condensate Reserves Distribution on Terrain [1]Figure 3View largeDownload slideOil & Condensate Reserves Distribution on Terrain [1] Close modal
+
+
+Figure 4View largeDownload slideActive rigs by terrain between 2010-2019 [1]Figure 4View largeDownload slideActive rigs by terrain between 2010-2019 [1] Close modal
+
+
+Figure 5View largeDownload slide3D - Seismic Data Acquisition between 2010-2019 [1]Figure 5View largeDownload slide3D - Seismic Data Acquisition between 2010-2019 [1] Close modal
+
+
+Figure 6View largeDownload slideAccidents in the upstream sector between 2010-2019 [1]Figure 6View largeDownload slideAccidents in the upstream sector between 2010-2019 [1] Close modal
+
+
+Figure 7View largeDownload slideNumber and quantity of oil spills between 2010-2019 [1]Figure 7View largeDownload slideNumber and quantity of oil spills between 2010-2019 [1] Close modal
+
+
+Safety at Sea Safety Programs
+
+
+Administration of Offshore Safety Permit
+
+
+Offshore Safety Permit (OSP) is a personnel accountability system used for tracking the personnel movement to and fro offshore and swamp locations in Nigeria. The system was instituted in 2012 to standardize the requirement for travel to offshore and swamp locations and to eliminate issues such as non-compliance with mandatory competency and safety training, non-compliance with medical fitness to work requirement, unauthorized extended stay on facilities at offshore/remote location, inaccurate documentation of personnel movement to-and-fro facilities at offshore/remote location leading to delayed/wrong incident reporting that existed prior to the establishment of the program. The OSP system uses a barcode strip E-card as displayed below in Figure 8, card readers which are placed at embarkation points, network of robust IT infrastructure made up of database, software & storage, and onsite personnel that aid in the administration and enforcement.
+
+
+Figure 8View largeDownload slideSample of OSP CardFigure 8View largeDownload slideSample of OSP Card Close modal
+
+
+Personnel travelling to or returning from offshore/swamp locations in Nigeria are required to swipe their barcode strip E-cards at designated OSP desks for admittance as part of check in/out process at embarkation points [3]. When the OSP cards are swiped at embarkation points, the card readers update the database appropriately after validating the card to check for compliance with mandatory requirements which includes a valid offshore medical certificate of fitness, an internationally recognized HSE training for offshore travel by boat or helicopter in training centres approved by the Commission and a valid work permit visa for non-Nigerian citizens. OSP ensure real-time updates of the itinery of all offshore/swamp workers in Nigeria.
+
+
+Implementation of Safety Case
+
+
+A safety case is a structured argument, supported by evidence, put forward by the proponents (operators or facility owners of oil and gas facilities) to demonstrate and convince the regulator and other relevant stakeholders, that its facility throughout its life cycle is acceptably safe for a specific application in a specific operating environment [4]. In the Nigerian oil and gas industry, it is mandatory for any facility that completes a design, commences operation, continues operating, undergoes a major modification or decommissioning to submit a safety case to the Commission and obtain a safety case approval before commencing that activity. The procedure for obtaining a safety case approval at each stage of the life cycle of a facility includes preparation of safety case by Operator with the participation of the Commission, following which the safety case will be submitted for review and validation visits by the Commission to ascertain the extent of compliance in demonstrating that the facility is safe to operate or continue to operate or safe to decommission. Lastly, the safety case is intended to be kept up to date and revised as necessary during the facility life cycle in line with the conditions that trigger revision of safety case as contained in the Commission's Safety Case Guidelines for oil and gas facilities in Nigeria. Operators are required to submit and obtain approval of a revised safety case for each of their facility(ies) every five (5) years. The five (5) yearly periodic submissions made to the Commission are expected to capture all necessary updates on the facility description, safety management system and revalidation of relevant formal safety assessments conducted on the facility within the five years under reference. The safety case approval process is depicted in Figure 9.
+
+
+Figure 9View largeDownload slideSafety case approval processFigure 9View largeDownload slideSafety case approval process Close modal
+
+
+The safety case approval process by the Commission ensures that the hazards and risks associated with major incidents carried out for the facility are fully understood and that control measures are put in place to manage the risks are adequate, monitored and maintained. Additionally, it ensures that adequate and documented systems are in place to prevent major incidents and near misses at the facility, and to minimize the effects of major incidents that might occur at the facility. Furthermore, it serves as a tool for the Commission to validate the risk minimization strategy, hold proponents accountable to the objectives and standards declared in the safety case in ensuring that all major risks identified in the facility are eliminated or minimised to as low as reasonably practicable (ALARP).
+
+
+Lastly, as a step in the process for approving a safety case, the Commission may embark on a safety case validation visit to ensure that the facilities described by the Operator/facility owner are fit for purpose or remain fit for purpose and consistent with the safety management system and formal safety assessments. The validation visit measures the extent of workforce involvement and awareness of the safety case, confirms performance standards of safety critical elements, reviews remedial actions and ascertains all other claims made by the proponent in the safety case dossier.
+
+
+Annual Facility Inspection and Oil Spill Contingency Plan
+
+
+The annual Facility Inspection and Oil Spill Contingency Plan (FI and OSCP) activation exercise is carried out based on annual inspection schedule drawn up by the Commission for facilities in the Nigerian oil and gas industry. The objective of the exercise is to assess progress of completion of action items raised from past audit exercise carried out on the facility, determine the company's level of HSE compliance with statutory guidelines and procedures in its operations, raise action items and recommendations which is necessary for continuous improvement and assesses the company's readiness to respond to emergency situations. The exercise commences with a review of the preceding year's audit action items​, pre-audit activities, review of documentation related to HSE, engineering and management system of the facility, facility inspections including interviews and functional test of equipment, activation of emergency response system and feed back to the company. The process of the FI and OSCP activation is depicted below Figure 10. The actions accessed in the pre-audit activities for the facilities include timely submission, review and approval of Oil Spill Contingency Plan (OSCP) document by the company, adequacy of the safety brief provided upon visit by the Commission and the percentage close out of issues identified in the previous audit exercise. The facility inspection exercise commences with the review of the company's statutory documentation. The reviewed documentation includes but not limited to a personnel accounting system, HSE policy statement, display of Mineral Oils Safety Regulation (MOSR), review of the safety and environmental management system - HSE training program, waste management plan, radiation monitoring plan, emergency preparedness/response amongst others. Additionally, other documents which are reviewed include relevant environmental studies, biological monitoring reports, Environmental Evaluation Report (EER), safety case approval, in-house audit records, asset integrity database, maintenance records, certification documents for various equipment, records of drill exercises, incident reports, waste consignment notes, permit to work (PTW) administration/records system and valid relevant permits for third party contractors providing service(s) to the company.
+
+
+Figure 10View largeDownload slideProcess flow of FI and OSCPFigure 10View largeDownload slideProcess flow of FI and OSCP Close modal
+
+
+Following review of relevant documentation, facility walkthrough, specific inspection and functional testing of equipment are carried out. The checklist for the facility inspection exercise guides the Commission to check the adequacy, accessibility, condition and use of PPEs, fire prevention/protection/fighting equipment, Emergency Shut Down (ESD) System, safety/warning signs and instructions available at relevant areas and containment equipment as detailed in OSCP document. In the process area, physical conditions as well as functionality of various equipment such as pressure vessels e.g. scrubbers, separators, piping etc., safety critical equipment e.g. PSVs, LACT unit, pumps, manifold etc., tanks (storage tanks, chemical tanks) and lifting equipment are confirmed. In the laboratory, the accreditation status, equipment calibration records, availability of relevant personnel, adequate storage of chemical and inventory are inspected. At the control room the condition of the integrated control & safety system (ICSS), supervisory control and data acquisition (SCADA), navigational aids, other monitoring gadgets (specify) are also confirmed. The check list covers the inspections of various supporting facilities including the chemical storage area, helicopter/boat landing, where applicable, stairs/handrails/grating, chairs/swing ropes, life raft(s), boats, vests etc. These facilities are expected to be free from obstruction, strategically located and possess third party certification. Furthermore, content and placement of medical emergency facilities such as eye wash stations, showers, sick bay and first aid boxes are checked. The facility clinic is expected to have good housekeeping, drug inventory, implementation of good health protection and promotion programs - medical checkup, annual audiometric tests etc. The availability of MEDEVAC facilities/procedures, oxygen for life support and waste/expired drugs disposal methods is also examined. Lastly, accommodation area, galley and mess area are inspected for good housekeeping, safety signages, clearly marked emergency routes, first aid facilities, heating/cooling system storage of facilities for food, sanitary conditions etc.
+
+
+Following the completion of the facility inspection, appraisal of emergency response capability is carried out. The company is expected respond to the emergency drill scenario presented by the Commission. The Commission monitors the drill scenario to assess the alert and notification process, communication gadgets/system used, speed of shutdown, isolation and mustering, mobilization of personnel and materials for response, deployment of surveillance and security, logistics, public affairs, medicals, finance and historian, demonstration of ability to contain and recover the spill, stop gas leaks, and spill combat strategy/ ESI Map interpretation used. When the drill is call off, standing down, demobilization and postmortem analysis of the drill scenario presented by the company is evaluated. Finally, the exercise is concluded with detailed feedback to the company. The feedback from the Commission communicates identified and novel good practices of the companies, observed lapses, areas of improvement and expected close out dates for those improvement areas.
+
+
+Risk Based Inspection
+
+
+Inspections is a key component of asset integrity management system that provides information of current condition of facility, validates reports on conditions of equipment through the physical examination of components [5]. The responsibility of the Commission towards inspection includes reviewing guidelines governing inspection activities in the industry, ensuring operators have adequate asset integrity management plan compliant with the relevant regulations and guidelines, coordinating condition monitoring and inspection activities, developing and reviewing standard work procedures (SWPs) for condition monitoring and inspection and reviewing trends of inspection activities. Inspections are expected to comply with recognized international codes and standards and be performed by competent personnel and witnessed periodically by the Commission to ensure compliance with relevant legislation and procedures. Inspections are enshrined in legislations and regulations governing oil and gas operations such as the Petroleum Industry Act 2021, Petroleum Act 1969, Petroleum (Drilling & Production) Regulations 1969, Mineral Oils (Safety) Regulations 1997 and guidelines published pursuant to the above-named legislations and regulations such as the Procedure Guide for Design Construction and Maintenance of Fixed Offshore Platforms, Guidelines and Procedures for the Construction and Maintenance of Oil and Gas Pipeline and their Ancillary Facilities and Guidelines for Technical Safety Control and other guidelines are available on the website of the Commission.
+
+
+As opposed to statutory time-based maximum inspection interval which are contained in the statutory guidance documents, RBI approval is given to the company that based on identifying risks in focal areas to meet the acceptance criteria. RBI is a process which aims to assess the risks of operating an equipment/device in a facility prioritizes these risks and plans the inspection type and intervals based on these risks [4]. According to the Commission's guidelines for the Implementation of Risk Based Inspection, the Commission approves and validates RBI program for Operators in the oil and gas industry through a technical audit of the RBI cycle starting from the design through the development of RBI tools and strategy; gathering of baseline information for inspection decisions, participation in workshops for determining criticality and inspection frequency, witnessing equipment inspections, ensuring that inspection data are utilized for updating the RBI process cycle and validating an upgrade of the RBI tool.
+
+
+Safety and Emergency Training Center and Medical Center Accreditation
+
+
+Attention to safety and emergency training and medical fitness to work are key components of oil and gas operations because lessons learnt from various incidents show that lack of training and medical related incident account for significant number of incidents that occur in the oil and gas industry [6]. Safety and emergency centers are facilities built specifically or designated to offer safety and emergency trainings. Safety and Emergency Trainings (SET) such as BOSIET, HUET, TSbB, First Aid, Firefighting, are courses designed for personnel who work in hazardous areas and perform safety critical tasks in the oil and gas industry. Medical centers are facilities where personnel carry out medical test to determine the level of fitness to work. Consequently, while SET equips personnel with skills required to understand hazards/HSE risks and respond to different levels of emergencies, the medical fitness center conducts clinical examinations, laboratory tests, and specialized investigations to confirm medical fitness of personnel to carry out designated activities as specified in the Commission's Occupational Health Guidelines and Standards for Medical Assessment of Fitness to Work in the Oil & Gas Industry in Nigeria.
+
+
+In the guidelines for the Establishment and Operations of Safety and Emergency Training Centers in The Nigerian Oil and Gas Industry, fitness-to-work test must be carried out in medical facilities registered with the relevant government agency. For a Safety and Emergency Training Centre (SETC) to operate, accreditation and approval from the Commission is a mandatory requirement. An SETC must have requisite capabilities and competencies via a detailed presentation which shall cover all the sections of the assessment criteria listed in Guidelines for The Establishment and Operations of Safety and Emergency Training Centers in The Nigerian Oil and Gas Industry. Following the presentation, physical assessment of the SETC, which includes inspection, functional testing of equipment, verification and confirmation of the course module(s), qualifications, competencies, and abilities of the instructors to deliver the course modules to delegates are evaluated. Physical inspections cover administration and management of SETCs, training syllabus/modules, location of training facilities, maintenance of equipment, health practices, catering, security, safety, natatorium, etc. Figure 11 shows areas that will be assessed during the accreditation exercise.
+
+
+Figure 11View largeDownload slideSETC Accreditation RequirementsFigure 11View largeDownload slideSETC Accreditation Requirements Close modal
+
+
+Notwithstanding the annual approvals, the Commission will continuously monitor the activities of accredited SETC to ensure that training standards are always maintained. This is achieved through periodic announced or unannounced inspection(s) and periodic consultations with global certifying bodies to confirm the status of certification of the course modules in line with global industry best practices, trends, and innovations.
+
+
+Search Rescue and Surveillance Program
+
+
+Search, Rescue and Surveillance (SeRAS) was established as part of National Oil and Gas Excellence Centre (NOGEC). The NOGEC comprise of Search, Rescue and Surveillance (SeRAS) Command & Control Centre, National Improved Oil Recovery Centre (NIORC), Oil and Gas Alternative Dispute Resolution Centre (ADRC), Oil and Gas Competence Development Centre (CDC) and Integrated Data Mining and Analytics Centre (IDMAC). Each of the aforenamed centers is structured to drive the three-prong objectives of safety, value and cost efficiency which are critical for oil and gas industry stability, growth, and sustainability.
+
+
+The Search, Rescue and Surveillance (SeRAS) provides a suite of services namely Incident Response Services (IRS), Emergency Medical Services (EMS), Transportation for routine bed space management, Security Intervention Services (SIS) and HSE Monitoring and Surveillance & Support. The aforementioned services of the SeRAS center are expected to enhance safety management, emergency preparedness and response as well as bed space management and logistics services across the industry. Apart from reduction of severity of accidents, entrenchment of safe practices, cost reduction, and improved operational efficiency across the industry, the establishment of SeRAS is expected create hundreds of direct and indirect jobs for pilots, paramedics, ICT workers, engineers, maintenance personnel, training facilitators, Foreign Direct Investments (FDIs) and taxes to the government and build local capacity in Search and Rescue (SAR). The SeRAS Command and Control Centre (CCC) is located in Lagos and two (2) Rescue Coordination Centres (RCC) will be set up at Osubi in Delta State and Brass in Bayelsa State for effective coverage of areas of operations. Implementing a coordinated and exclusive SAR services will bring the Nigerian oil and gas industry at par with international best practices obtainable in the UK continental shelf, Gulf of Mexico, middle east, etc.
+
+
+Conclusion
+
+
+The Petroleum Industry Act puts enormous responsibility on the Commission. The Act provides for reforms in the governance framework for efficient and effective governing institutions that foster a business environment conducive for petroleum operations. The safety program discussed in this paper namely Administration of Offshore Safety Permit, Implementation of Safety Case, Annual Facility Inspection and Oil Spill Contingency Plan, Risk Based Inspection, Safety and Emergency Training Center and Medical Center Accreditation and Search Rescue and Surveillance Program. These programs are critical to reducing HSE risks needed for sustainable growth in the industry. Safety programs do not only protect people, the environment and assets, but drive cost savings in the industry, enhance collaboration among operators, creates jobs and increases economic opportunities in Nigeria. The Commission encourages feedback from stakeholders to improve these programs which will ultimately improve safety standards in the industry.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Department of Petroleum Resources, "Nigerian Oil & Gas Industry Annual Report," 2019.NUPRC, "NUPRC Chief Executive Engr. Gbenga Komolafe FNSE, Gives Account of his Stewardship in the Last Six (6) Months," May06, 2022. https://www.nuprc.gov.ng/nuprc-chief-executive-engr-gbenga-komolafe-fnse-gives-account-of-his-stewardship-in-the-last-six-6-months/ (accessed May 12, 2022).Okechukwu. K.Nwankwo, Jennifer. S.Muku, Mutiu. K.Amosa, Chidi. B.Ike, and E.Ogionwo, "Assessment of Safety Case Compliance in the Nigerian Oil and Gas Industry," Aug. 2020. doi: 10.2118/203604-MS.Google Scholar NUPRC, "Safety Case Guidelines For Oil and Gas Facilities in Nigeria," 2020. Accessed: Jun. 01, 2022. [Online]. Available: https://www.nuprc.gov.ng/wp-content/uploads/2020/10/Safety-Case-Guidelines-for-Oil-and-Gas-Facilities-in-Nigeria.pdfE.Terry, Introduction to Safety, Risk and Reliability Engineering Hazard Management in Design. 2017.Google Scholar M.Edem, O.Nwankwo, J.Muku, F.Usman, and C.Ike, "Reducing Accidents Through the Implementation of the Minimum Industry Safety Training for Downstream Operations Mistdo in the Nigerian Oil and Gas Industry," Aug.2021. doi: 10.2118/207085-MS.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211954-MS
+
+
+

files/2022/Achieving Zero Lost Time Injury in a Refinery A Practical Approach.txt

@@ -0,0 +1,196 @@
+----- METADATA START -----
+Title: Achieving Zero Lost Time Injury in a Refinery: A Practical Approach
+Authors: Eretoru Robert, Camilla Junaid, Kawu Idris-Idah, Adebimpe Oyeyele, Daniel Olomolaiye
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211994-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+This paper aims to highlight and share some insight on how a modular crude oil refinery operating at 5000bopd capacity delivered refined products with zero LTI. The intention is to pass knowledge to upcoming modular refineries as projections from the Ministry of Petroleum Resources indicate that many more will spring up within the next few years. The refinery came live in November 2020 and was tasked with ensuring optimum production in a safe working environment. Since then, it has gone on to safely refine over one million barrels of crude oilIn the Oil and Gas industry, safety is a crucial determinant of the overall success of a facility’s operations. Refineries have even greater responsibilities, given that there is the presence of highly volatile and flammable hydrocarbons within the facility, and personnel having consistent interactions with these liquids pose a huge danger. Achieving zero LTI implies that in the period under review, no incidents occurred that prevented the continuity of the core activities of the workplace, no productive work time was lost due to injuries to an employee and as such, is a key indicator in measuring safety performance, hence its adoptionThe steps taken to minimize and eliminate risks peculiar to the refinery were discussed, with these taking the form of engineering controls, policies and procedures. It is interesting to note that in the operation of a refinery, the areas that are most vulnerable to accidents are outside the core process areas. The array of process control systems, alarms, and fail-safes keep the system within operational limits. The systems involved in the storage and offloading of hydrocarbon products, however, handle more volumes of hydrocarbon, with higher frequency and is prone to human error. This work is a summarised document that contains proactive safety-inclined solutions and lessons learned along the way, hopefully translating into increased productivity and safety within the downstream space.
+
+
+
+
+Keywords:
+downstream oil & gas,
+refinery,
+time injury,
+upstream oil & gas,
+personnel,
+procedure,
+petroleum engineer,
+hsse reporting,
+human factors,
+loading bay
+
+
+Subjects: 
+HSSE & Social Responsibility Management,
+Safety,
+HSSE reporting,
+Human factors (engineering and behavioral aspects),
+Operational safety
+
+
+
+
+Introduction
+
+
+Modular refineries are mini-refineries with the capacity to process between 1000 to 30000 bopd of crude oil. (Ogbon, et al., 2018) carried out a financial analysis on a 10kbpd modular refinery using a five-year projection on earnings and found its average annual net income is $45Million, indicating that they are economically viable ventures. With its construction flexibility, smaller capital requirement, and higher return on investment, little wonder the steep rise in the development of modular refineries in Nigeria to meet local energy demand.
+
+
+The operations and maintenance of such process facilities to continually meet business needs are contingent upon a pivotal element, SAFETY. Production time is an important consideration in refinery management, where the focus is usually on crude oil to ensure maximum productivity in lesser time. However, the people who control/interact with the system can cause a greater loss in productive time if not properly managed. With that, we can say that safety is an integral part of achieving maximum productivity.
+
+
+Literature Review
+
+
+Oftentimes, Safety can broadly be classified into; process safety, behavioural safety, and managerial safety.
+
+
+Figure 1View largeDownload slideSafety ClassificationFigure 1View largeDownload slideSafety Classification Close modal
+
+
+Process safety is usually catered for in the design phase with Hazard Identification (HAZID) and Hazard and Operability Analysis (HAZOP) to ensure the integrity of process systems. Managerial safety accounts for policies and implementation techniques the business have put in place to reduce the occurrence of accidents. Behavioral safety encompasses the employees’ attitude towards already laid out guidelines from both process and managerial safety. This study will discuss the practical approach these components utilized in contributing to the success of refining over one million barrels of crude oil without any lost time due to injury.
+
+
+There are set guidelines and procedures that when followed, ensure smooth and safe operations which in turn ensured zero LTI during the period under review. These procedures were adopted and modified to suit the peculiar environment (refinery).
+
+
+Process Safety
+
+
+Understanding hazards and risks, management of risk by providing appropriate layers of protection to reduce the frequency and severity of incidents and learning from incidents when they happen is a summary of process safety. In the refinery under review, measures were incorporated to ensure the safety of the process. This includes a HAZID and HAZOP study.
+
+
+HAZID Study broke the refinery down into parts for detailed analysis. During this analysis, hazards capable of causing injury to personnel, asset damage or loss, environmental damage, loss of production, or LTI. The HAZID process was based on the company’s hazard control hierarchy. Hazards require some form of control to mitigate risks. By deploying this tool during the design phase, the refinery was determined to be a feasible project from a safety point of view.
+
+
+A comprehensive HAZOP study helps to identify and evaluate problems that may represent risks to personnel, equipment, or project efficiency (Siddiqui, et al., 2014). Process parameters and guidewords are examined methodically to ensure that the process is explored in every possible way.
+
+
+A concise HAZID and HAZOP study engender engineering controls that mitigate/eliminate identified possible hazards. In the case of this refinery, some of the practical engineering controls which were implemented include:
+
+
+Installation of flame arrestors in the flare systems.Deployment of Emergency Shutdown (ESD) buttons across various systems of the facility - Heater, Pump Systems, Control Screens, etc.Use of audible alarms across systems. The Boiler System, for instance, has an alarm that signifies both low and high levels of water and pressure.Equipment of all vessels (boiler, crude distillation unit, air compressors, etc.) with pressure relief systems.Construction of spill containment drains at the Loading Bay area and the Inside Battery Limits (ISBL).Construction of bund walls for spill containment and a state-of-the-art fire-water system, which are forms of reactive engineering controls
+
+
+Still, under engineering controls, it is worthy to note that a refinery is different from other process plants in the oil and gas sector in the sense that there is an area where there is continuous exposure to hydrocarbon fluids during normal operations. The area in question is the Loading Bay, and because of the high risk it poses, specific engineering controls were put in place during the HAZID and HAZOP study of the refinery. These controls include but are not limited to:
+
+
+Construction and installation of a hood at the Naphtha offloading line that traps vapor from this product during offloading operations. This trapped vapor is channeled through a dedicated line to the flare system.Installation of an earthing clamp that guarantees a secure discharge of static charges during truck-loading operations.Provision of pressure relief vents and drains along product lines which aids depressurization thus, keeping overpressure at bay.Installation of an additional shut-off system in the form of a ball valve at the loading arms. This guarantees containment of products outside of operating hours.The provision of an ESD button on the HMI guarantees the stoppage of all loading processes in the case of a failure.
+
+
+Managerial Safety
+
+
+As already stated, managerial safety encompasses all policies put in place by the business to eliminate hazards and prevent the occurrence of accidents. Examples of these measures include:
+
+
+Enforcement of the use of job-appropriate personal protective equipment (PPEs).Enforcement of zero loading bay operations at nighttime.Creation of job checklists for both routine and non-routine jobs and operations.Provision of safety observation report cards (SOR cards). These are used to report observed unsafe acts and conditions. Etc.
+
+
+Behavioural Safety
+
+
+The behaviour of employees and contractors to the laid-down safety measures and policies put in place by management played a vital role in ensuring the safe running of the refinery. Some of the behaviours of workers include:
+
+
+Strict adherence to the use of the appropriate PPEs when carrying out jobs.Use of intrinsically safe devices in process areas.Good hazard identification, reporting, and appropriate response to possible hazards.Dutiful attendance to daily toolbox meetings which serve as reminders of safety-related issues and practices.The ‘safety is a personal responsibility’ mindset of workers.
+
+
+Discussion
+
+
+An overall effective combination of the three aspects of safety that have been outlined has resulted in Zero LTI in this refinery; no aspect can be left out. Some major examples, using different sections of the refinery, of how process, managerial and behavioural safety are combined to yield Zero LTI are outlined.
+
+
+Loading Bay
+
+
+As the Loading Bay has the most exposure both to flammability and to non-staff, the loading bay operators are fully empowered to give direct commands to ensure safety. They use a thorough checklist to certify a truck is fully suitable for loading. They can refuse to attend to trucks if safety would be compromised even in the tiniest way. Examples of common ways that safety could be compromised are:
+
+
+signs of leakage on the truckincomplete PPE on the truck operators/driversinaccurate or incomplete documentation on the truck capacities, even if it physically looks otherwise
+
+
+Before truck drivers bring in their trucks to the Refinery area, a series of basic checks are done by auxiliary staff to rule the truck eligible for loading in the first place. These checks go a long way in averting accidents that may otherwise have occurred from letting an unsafe truck near the process area. Auxiliary staff is present to coordinate the movement, arrangement, and alignment of trucks heading into the Loading Bay area. This helps to prevent driving accidents.
+
+
+The administrative sales section plays its part in ensuring that the customers are fully informed of the safety procedures on the Plant, even before they step foot into the refinery. The consequences of not complying with these safety procedures are also relayed to them. Reports of any likely incidents or lessons learned from the loading bay operations are compiled by the loading bay operators and shared among all staff of the plant. This helps other workers to be more alert and sensitive to potential dangers on the job that may otherwise have gone unnoticed. Lessons from the Loading Bay can be easily applied to other parts of the plant.
+
+
+It is very important that no loading operation, no matter how little, is handled by just one personnel at a time. At least two personnel must be present and available for such activity. As loading bay operations continue for hours on end, forced personnel breaks are taken very seriously. During these breaks, all operations at the loading bay are paused and personnel takes the needed number of minutes for rejuvenation before continuing work for the day.
+
+
+ISBL and OSBL
+
+
+The ISBL area is the heart of process operations, operations in this area require that hydrocarbon fluids are maintained with very specific values of temperature and pressure. The hydrocarbon fluids in this area are at relatively high temperatures and pressures above atmospheric pressures. It thus follows that any condition under which fluid is exposed to air is potentially dangerous as all elements of the fire triangle are present. To efficiently run the process operations while achieving zero LTI, it is inadequate to only monitor the process conditions and see that they are within acceptable values. Physical inspection of systems is essential. A few of these checks are enumerated below:
+
+
+Periodic verification that field instrument readings correspond to values transmitted to the process control center.Inspection of vessels, pumps, process equipment, and pipelines for spills or signs of leakage.Checks that spill containment systems are not obstructed, clogged, or closed.Monitoring concentration of hydrocarbon vapors using the pertinent equipment.Corrosion monitoring and checking that prevention systems are fully functional.
+
+
+The above checks are essential and are done with a sense of awareness of potential risk factors.
+
+
+The OSBL area of the refinery is particularly unique in its function and consequential risks. The area contains large volumes of hydrocarbons in storage along with a variety of equipment. Special care is taken to isolate the two sections, as their combination is a risk factor. The section of the OSBL where equipment is located is the Utility Area. It consists of the boiler and its associated systems, air compressors, pumps, and Inline heaters. The Tank Farm, where the hydrocarbon storage tanks are located, on the other hand, is isolated from the Utility Area. The storage tanks are surrounded by a bund wall to contain spills with drainage valves that are kept closed. The storage tanks also have an independent fire system to cater for hydrocarbon fires. A good portion of the inherent risks in the OSBL area are catered for in the design, but just like in the ISBL area, operational practices are needed to eliminate risks (Refinery X 2020). These practices are highlighted below:
+
+
+Monitoring of liquid levels in storage tanks to avoid loss of containmentInspection of boilers, heaters, pumps, pipelines, and tanks for leaks or spillsPeriodic servicing and operation of fire equipmentFunction test on boiler pressure control systemsCorrosion monitoring
+
+
+Laboratory
+
+
+The laboratory contains different equipment used to perform quality control (and quality assurance) on products and raw materials. Handling of this equipment is permitted to be done only by trained laboratory staff. The laboratory staff has been fully equipped with PPE to ensure that all concerned parts of the body are protected. While product analyses are going on, entry into the laboratory and laboratory area is highly limited, to prevent inhalation of toxic fumes which would result from not wearing a protective nose mask or injury to the skin from hot fluids. The proper arrangement and organization of equipment in the laboratory minimizes the probability of accidents that may otherwise occur from trips and falls from cables and equipment parts (Refinery X 2020).
+
+
+Like the Loading Bay, more than one personnel are at work in the laboratory at a time. This would ensure that an eye is kept on all working equipment at every point in time. Any sign of malfunction or job completion can be attended to. Checks are also done at the end of every analysis and at the end of the day to ensure that equipment is returned to its off or dormant position when not in use.
+
+
+All laboratory personnel are completely familiar with all equipment manuals and understands their operations and maintenance. The needed validations and calibrations are done timely, according to schedules. Constant clean-up is done by lab and cleaning personnel to avoid slips that could result from spills and to also ensure that surfaces are free of loose hydrocarbons.
+
+
+General Safety Actions
+
+
+The members of the appointed safety team play their part in ensuring that they are doing checks on safety equipment on the plant. A major example of this safety equipment is Fire Extinguishers.
+
+
+Practical and theoretical safety training and meetings are held every month (and with more frequency, if necessary), ensuring that every department is well represented. These training and meetings help to mentally equip staff with an update on safety practices needed for work on the Plant.
+
+
+Toolbox meetings are held every day and a timetable is used such that each staff can come up with and share lessons on a topic that everyone would benefit from. This improves knowledge sharing amongst everyone and puts them on the same page about work and personal safety.
+
+
+Quality of food and quality of living cannot be overlooked when considering proper physical and mental working practices. Personnel's mind and body must be well taken care of before they can do work safely, after all, safety starts from good thinking before physical application. Necessary provisions are made to ensure that feeding and living conditions are top-notch.
+
+
+Conclusion
+
+
+To achieve Zero Lost Time Injury in the refinery, which is a constantly operating and dynamic process plant, all parts of safety – process, managerial and behavioural need to be fully and constantly applied. Every personnel involved, even in the tiniest way possible, must fully play their part in ensuring this.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Siddiqui, N. A., Nandan, A. & Sharma, M., 2014. Risk Management Techniques HAZOP & HAZID Study. International Journal on Occupational Health & Safety, Fire & Environment - Allied Science, July, 1(1), pp. 005-008.Google Scholar Ogbon, N., Otanocha, O. & A., R.-R., 2018. An Assessment of the Economic Viability and Competitiveness of Modular Refinery in Nigeria. Nigerian Journal of Technology (NIJOTECH), 37(4), pp. 1015 - 1025.Google ScholarCrossrefSearch ADS  Refinery X. 2020. "Refinery X Operations and Maintenance Manual."Refinery X. 2020. "Refinery X Laboratory Manual."
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211994-MS
+
+
+

files/2022/Agbami Stuck Frac Pack Service Tool Prevention Measures.txt

@@ -0,0 +1,232 @@
+----- METADATA START -----
+Title: Agbami Stuck Frac Pack Service Tool Prevention Measures
+Authors: Chidi Elendu, Steve Njoku, Ihechi Ojukwu
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211903-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+It is usually an unnerving moment when a service tool is picked up, after a frac pack pumping operation with the goal to reach the "reverse position". The failure to establish this position could have significant undesirable consequences on the overall well objective, which could range from extensive fishing of the resulting stuck pipe to eventual loss of the well. It, therefore, becomes imperative for the completions planning team in collaboration with other relevant stakeholders to establish an "execution-friendly" reverse-out decision and communication protocol that will prevent a stuck situation.Agbami completions are mostly stacked frac pack with Intelligent Well Completion (IWC) capability to adequately control and monitor production. The first phase completion of the three-phased development was installed in 2007, while production commenced in 2008 [1]. A major consideration for the phased development campaign was to ensure lessons learned from one phase can be applied to the next. The 8-well Infill Drilling campaign was executed from 2017 to 2019 to capture un-swept oil and optimize production from the field. The Agbami frac service tools were successfully upgraded after the initial development phases and deployed on the infill campaign to mitigate the challenges encountered during the third phase frac pack installations. This upgrade, coupled with standardized processes, equipment, and procedures contributed to the improved frac pack installation performance recorded on the infill campaign.Despite the frac pack improvements, a near-miss on one of the completions could have resulted in a stuck service tool where an overpull of up to 160kips was required to move the service tool to reverse out excess proppant. It also took seven attempts and ∼105 kips overpull to move the shifter and close the FS2 fluid loss isolation valve. An investigation into this near-miss identified amongst other opportunities, a gap in the current communication protocol, and the need to improve the operations team's situational awareness of downhole conditions during pumping, at screen-out and at reverse-out.The team leveraged global initiatives on stuck service tool prevention and collaborated with service partners and the rig contractor to develop a fit-for-purpose reverse out and communication protocol. This protocol was successfully implemented in subsequent well completions. A "Frac Pack on Paper" meeting held with all relevant stakeholders: the rig crew; pumping and completion service companies; Chevron's Frac support group, and Chevron's completions and operations teams, to methodically go through the reverse-out and communication protocol which contributed immensely to the huge success achieved on the frac pack operations. The team's effective collaboration with service partners contributed to the ability to respond quickly to these challenges leading to continuous improvement in Agbami frac pack executions.This paper aims to discuss the Agbami stuck service tool challenges, causative factors, and mitigation steps successfully implemented.
+
+
+
+
+
+
+Introduction
+
+
+Agbami field is one of the largest deepwater oil producing fields in Nigeria, it was discovered in the year 1998 [2]. The field is located approximately 70 miles offshore from the nearest Nigerian coastline in water depths between 4,200 ft and 5,410 ft (Figure 1)
+
+
+Figure 1View largeDownload slideAgbami field mapFigure 1View largeDownload slideAgbami field map Close modal
+
+
+Agbami is produced through a floating production, storage, and offloading (FPSO) vessel and a multiwell subsea well program consisting of producers, water injectors and gas injectors (Figure 2). The field development strategy was to optimize the location, placement, and drilling of deepwater production and injection wells in three major stacked reservoir units (A, B, and C), to optimize oil recovery and maximize the project NPV. A phased drilling program was adopted for the field so that lessons learned from one stage could be applied to the next. Production commenced in 2008.
+
+
+Figure 2View largeDownload slideAgbami subsea developmentFigure 2View largeDownload slideAgbami subsea development Close modal
+
+
+Table 1, shows the phased development of the field starting 2008 up to to the completion of the infill campaign in 2019, bringing the total well count to 45. More details on the completion summary are in Table 2.
+
+
+Table 1Agbami wells development by phases   View Large
+
+
+Table 2Agbami Completion Overview   View Large
+
+
+The Agbami formation is comprised of poor to medium consolidated Miocene sandstone reservoirs. They are made up of successfully stacked formation sands of net sand thickness ranging from 25 to 300 fttrue stratigraphic thickness. The initial completions were comprised of multiple stack frac packs with intelligent well control (IWC) upper completions (interval control valves and multiple pressure and temperature gauges for independent metering and well surveillance) to ensure that production intervals from different reservoirs are independently controlled and monitored (see Figure 3). The management of Agbami field is based on sound and effective reservoir management practices involving reservoir voidage balancing, water and gas injection optimization to stabilize reservoir pressure and well performance optimization within various operating constraints.
+
+
+Figure 3View largeDownload slideConventional vs Agbami completion designFigure 3View largeDownload slideConventional vs Agbami completion design Close modal
+
+
+Agbami Frac Pack Design Philosophy
+
+
+The Agbami frac pack design strategy was based on the rock mechanics analysis (RMA) and sanding studies which consider the safe drawdown pressure (SDP) [3]. SDP is the lowest value of the drawdown pressure across the rock face at which formation sand is produced, which is determined by in-situ stresses, pore pressure, formation rock properties, drawdown and wellbore and perforation orientation. The rock properties used in the analysis were estimated using RMA correlations and calibrated with laboratory test data (unconfined compressive strength (UCS), hollow cylinder strength (HCS) and triaxial compression). The decision to install frac pack was based on lower skin, subsea nature of the field and the expected high production capability of the individual wells [4] [5] [6].
+
+
+These are the considerations for the Agbami frac pack design based on the modelling/simulation, field experience and formation evaluation assessments [7] [8] [9] [10]:
+
+
+Target intervals to a maximum completion length of 150ft MDUse alternate path screen (APS) with 2 shunt tubes to ensure annular proppant coverageFrac fluid is YF13x ReFlex, range of gel loading is 30 – 35 lbs/galComplete annular pack across the interval to ensure completion integrityTarget interval proppant coverage designed for 600-1200 Ibs/TvD-ft, with focus on fracture length and width at TSO.Minimum pump rate to continue fracturing should target 18 bpm (validated through simulation)Maximum proppant concentration of 8 – 10 ppaMaximum pump rate of 30 bpm (validated through simulation).Placement of the frac pack should be at ±48 degree inclination.Production casing is 9-5/8" size and screen is 5-1/2" 250 micronsThe perforation is 18 SPF with big hole charges.
+
+
+The fluid design and fluid loss strategy for the Agbami recent wells are shown in Figure 4. This is based on the use of 8.8 ppg NaCl brine (temperature corrected to get the desired overbalance) and the minimum required overbalance of 250 psi for the perforated intervals [1] [7] [11].
+
+
+Figure 4View largeDownload slideFluid design and loss prevention strategy [12]Figure 4View largeDownload slideFluid design and loss prevention strategy [12] Close modal
+
+
+Historical Service Tool Events
+
+
+There are no specific literature available on the existence of the stuck service tool from the major service companies that could be accessed. One of the service companies in Nigeria confirmed that the only issue on record was an instance when collet locator was installed wrongly, "upside down" resulting in the change of the thread connection from "pin × pin" to "box × pin". Searches produced some information on historical high potential and actual stuck service tool as presented in Table 3 with the attendant causes and what the aftermath of the events was.
+
+
+Table 3Stuck service tool events   View Large
+
+
+Potential Stuck Event [13]
+
+
+After the screen-out of the frac pack at ∼5500 psi, the pressure in annulus was equalized and the FSV was opened. The service tool was picked up and tubing pressure bled to 2000 – 95 psi. Subsequently, the annulus was pressured to 2,500 psi while stripping to reverse circulating position. While attempting to move tool to reverse position, 130k overpull was observed. The workstring was reciprocated severally to free the service tool with 130k-160k overpull after which reverse out of the excess proppant commenced with the pumps staged up to 20 bpm, 2750 psi.
+
+
+The service tool was picked up to 16,450 ft, the Frac head assembly was disconnected and traversed to the auxiliary mouse hole. The rig continued to pick up (P/U) from 16,450 ft to 16,418 ft MD. As the seals were dumped, fluid losses at the rate of 158 bph was observed. The rig continued to POOH and shifted FS-2 closed but had to reciprocate the string up and down at 16,240 ft before the shifter located on the FS-2 on the 7th attempt with 105k over pull. The well was flow checked for 30 mins @ 16,180 ft MD but was static. This implied that the FS-2 was closed, and formation is isolated.
+
+
+Lessons Learnt from The Event
+
+
+The preliminary findings discovered the following [14]:
+
+
+Tubing Pressure was bled off prior to service tool reaching the reverse circulation position which would have caused U-tube of proppant into the frac port as shown in Figure 5.There were gaps in the current communication protocol.○Roles and responsibilities were not understood and communicated.○Instructions flow in a haphazard manner.○Manifold operator took instructions from the frac master instead of the tools man.There was gap in the situational awareness of the downhole conditions at screen-out.The pressure requirements post-screenout were calculated on individual basis.Various material limitation, e.g., tensile strength was known but not documented in the PoA.There was no malfunctioning of equipment like pump, manifold, etc.
+
+
+Figure 5View largeDownload slideSurface and Bottom hole events at screen-outFigure 5View largeDownload slideSurface and Bottom hole events at screen-out Close modal
+
+
+Prior to ROC tool activation (see Figure 6), the formation was exposed to annulus hydrostatic above VCH packer, the BHP was in communication to both annulus above the packer and workstring via MCS and frac port. Due to the exposure the surface pressures on both tubing and annulus must be adjusted to balance BHP and prevent U-tubing into either annulus or workstring. On ROC tool activation, pressure below tool was expected to decline to reservoir pressure
+
+
+Figure 6View largeDownload slideFlow path on ROC tool activationFigure 6View largeDownload slideFlow path on ROC tool activation Close modal
+
+
+The estimated downhole tool movement from WD Circulating to Reverse position was ∼ 8.4ft
+
+
+▪Surface workstring movement should account for stretch due to differences in fluid in workstring and annulus.▪Proppant in the workstring▪Pressures held on the workstring and annulus at surface
+
+
+The reverse-out roles, responsibilities and protocol should be clearly communicated and understood.
+
+
+In order to develop the full awareness and preparedness for post-screen-out responses after the frac pack, the reverse out drills should incorporate every aspect of the anticipated screen-out events. The flow path at reverse circulating position is shown in Figure 7, this is an indication of the position of the ROC tool.
+
+
+Figure 7View largeDownload slideFlow path at reverse positionFigure 7View largeDownload slideFlow path at reverse position Close modal
+
+
+Figure 8 shows the surface pump pressure response based on the near stuck event and the block positions, it is evident that human factor played a significant role as a causative factor (bleeding off DP pressure and moving the tool.
+
+
+Figure 8View largeDownload slideEvents prior to reaching reverse circulating positionFigure 8View largeDownload slideEvents prior to reaching reverse circulating position Close modal
+
+
+The strategy for the frac pack execution is always to have the ROC fully closed before getting to the reverse position. The BHP as shown in Figure 9 was in communication because the ROC wasn't closed.
+
+
+Figure 9View largeDownload slideROC position analysisFigure 9View largeDownload slideROC position analysis Close modal
+
+
+Implementation of the Lessons Learnt
+
+
+Several gaps were identified during the preliminary investigation of the near stuck event that may have contributed to the event. The major gap observed was closed by the checklist (Table 4) This was developed to help with the frac planning and execution process to minimize the risk of sticking the frac pack service tool.
+
+
+Table 4Frac Pack Planning and Execution Checklist  View Large
+
+
+The gap in the communication protocol and hierarchy was closed by the development of responsibility and accountability document with the appropriate channel of flow for all frac pack operations at screen out as shown in Figure 10.
+
+
+Figure 10View largeDownload slideCommunication protocol at screenoutFigure 10View largeDownload slideCommunication protocol at screenout Close modal
+
+
+Prior to moving the service tool to reverse circulating position at the screenout, drill pipe and annulus pressure calculations were performed. However, just as the tool started to move, the DP side was bled off too quickly, and at that moment there were both fluid movement as well as pipe movement. These movement led to mobilization of proppant into the packer bore and service tool interface.
+
+
+These were the calculations and considerations for the system pressures prior to screenout and performed on excel, see Figure 11:
+
+
+BHP (after screenout)=ANN_PRES (after SO)+ANN_HYDROSTATIC (on the annulus side)            DP_PRES + DP_HYDROSTATIC      (on the tubing side)
+
+
+Figure 11View largeDownload slideReverse out pressure spreadsheetFigure 11View largeDownload slideReverse out pressure spreadsheet Close modal
+
+
+The implication of the above equation is that if there is pressure in the annulus after screenout, then the BHP is not reservoir pressure.
+
+
+How to calculate pressure to be kept on the DP side when P/U to reverse: Pres on the DP = ANN_PRES (after SO) + ANN_HYDROSTATIC – DP_HYDROSTATIC + TBG_SF (300 psi)
+
+
+For Agbami 45:ANN_PRES ( after SO )=2,050 psi   ANN_HYDROSTATIC =6,470 psi   DP_HYDROSTATIC =6,415 psi (based on 347 bbl flush pumped)
+
+
+The pressures prior to moving the tool should be:
+
+
+Press on the DP = 2,050 + 6,470 – 6,415 + 300 (TBG_SF) = 2,405 psi
+
+
+Pres on the ANN = 2,050 + 500 (ANN to TBG_SF) + 300 (TBG_SF) = 2,850 psi
+
+
+The maximum overpull encountered during the tool movements depends on the service tool positions and material's tensile rating and is defined by the following:
+
+
+In weight down circulating / squeeze position:
+
+
+overpull is based on the lowest tensile rating from the top of the packer setting tool to the HPT seal mandrel.
+
+
+In reverse position
+
+
+Scenario 1: when the seal mandrel is still in the packer seal bore. Overpull is based on the lowest rated component from top of packer setting tool to bottom of HPT tool, see Figure 13 for tool positions.Scenario 2: after dumping seals. Overpull is based on the lowest rated component from bottom HPT to end of wash pipe. The recommendation going forward was to always include the tensile limits (see Figure 14) of the various component of the service tool in the well specific execution program and a printout posted at the driller's console for easy consultation.
+
+
+Figure 12View largeDownload slideService tool positionsFigure 12View largeDownload slideService tool positions Close modal
+
+
+Figure 13View largeDownload slideService tool component tensile strengthFigure 13View largeDownload slideService tool component tensile strength Close modal
+
+
+Conclusion
+
+
+The investigation primarily revealed that human performance factor was the major causative factor followed by communication protocol not well understood. Below is the summary of what the paper sets out to accomplish.
+
+
+The communication hierarchy protocol was updated to ensure clear understanding of individual responsibilities.Screenout pressure calculation sheet was developed and verified independently for accuracy.Updated well specific execution program to have the various responsible parties as well as the tools’ limitations listings.Several collaborative engagements with all the parties created awareness and confidence in the team.Performed screenout drills with pressure in the system to simulate actual conditions.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgements
+
+
+The author appreciates the support of the concessionaires and project participants throughout the project and for the permission to publish this paper.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion Annannular BHbottomhole BHAbottom hole assembly BHPbottom hole pressure BOPblowout preventer bphbarrel per hour bpmbarrel per minute C&Kchoke & kill CSGcasing CVXChevron DPdrill pipe DSMdrillsite manger FSVfailsafe valve gpmgallon per minute HEChydroxyethyl cellulose IWCintellegent well completion LAlower annular LASliquid additive system LLlesson(s) learnt MDmeasured depth MOPmaximum over pull OEMoriginal equipment manufacturer OIMoffshore installation manager pHpotential of hydrogen POAprogram of action PPpore pressure PPApound of proppant added ppbpound per barrel ppgpound for gallon ppgepound per gallon equivalent PRpipe ram PU or P/Upick up RDTremote data transmission ROCreverse out check RSPMrigsite project manager / company man SFsafety factor SMEsubject matter expert SOscreenout TBGtubing TDtotal depth TVDtrue vertical depth wtweight X/Ocrossover
+
+
+References
+
+
+K.Adegbulugbe, C.Elendu, I.Ugah and I.Ojukwu, "Agbami Next Generation Frac Pack Equipment Development & Deployment," in SPE Nigeria Annual International Conference and Exhibitionv, Lagos, 2020.Google Scholar Wikipedia, "Agbami Field," Wikipedia, April2009. [Online]. Available: https://en.wikipedia.org/wiki/Agbami_Field.T.Klimentos and S.Hassan, "A Case Study Of Rock-Mechanical Property Evaluation And Sanding Analysis In Very Porous Hydrocarbon-Bearing Sands," in SPWLA 42nd Annual Logging Symposium, Houston, 2001.Google Scholar V. J.Pandey, R. C.Burton and M.Nozaki, "Evolution of Frac-Pack Design and Completion Procedures for High Permeability Gas Wells in Subsea Service," in PE Hydraulic Fracturing Technology Conference, The Woodlands, 2014.Google Scholar F. C.Colbert, F. M.Garcia, A.Costa, R.Gachet, H.Mattos and AJunior., "Best Practices for Frac Pack on High Permeability/Unconsolidated Reservoirs: Experience from Offshore Brazil Operations," in SPE International Hydraulic Fracturing Technology Conference and Exhibition, Muscat, 2018.Google Scholar P.Baycroft, K.Webster and S.Mathis, "Optimized Frac-Pack Completion Requires an Appropriate Execution Pace," in SPE International Symposium and Exhibition on Formation Damage Control, Lafayette, 2006.Google Scholar D. W.Norman, Fracpack Technology, Hoston: Chevron Internal, 2010.Google Scholar L.Behrmann and K.Nolte, Perforating Requirements for Fracture Stimulations, Lafayette, Louisiana: Society of Petroleum Engineers, 1998.Google ScholarCrossrefSearch ADS  Agbami Fluid Loss Management Strategy, Chevron Internal.Post Job Report, Lagos: Chevron Internal, 2020.R.Tibbles, K.Govinathan, I.Mickelburgh, S.Jain and P.Wassouf, Understanding Sand Control Installation Failures, Kuala Lumpur: Offshore Technology Conference, 2020.Google Scholar Agbami Completion Basis of Design, Chevron Internal, 2005.D. W.Norman, Fracpacking General Introduction, Houston: Chevron Internal, 2010.Google Scholar D. W.Norman, Fracpacking D3essign Workflow, Houston: Chevron Internal, 2010.Google Scholar "Intelligent Completion Helps Agip and NPDC Make First-Oil Date," Schlumberger, [Online]. Available: https://www.slb.com/resource-library/case-study/co/ic-agip-npdc.
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211903-MS
+
+
+

files/2022/Algorithm to Compute the Minimum Miscibility Pressure MMP for Gases in Gas Flooding Process.txt

@@ -0,0 +1,506 @@
+----- METADATA START -----
+Title: Algorithm to Compute the Minimum Miscibility Pressure (MMP) for Gases in Gas Flooding Process
+Authors: Elohor Diamond Akpobi, Efeosa Praise Oboh
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211973-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Enhanced oil recovery (EOR) is important to the petroleum industry mostly because it is used to improve oil recovery. Miscible gas flooding, a type of EOR process that is proven and economically viable can significantly increase oil recovery from reservoirs. In this study, the minimum miscibility pressure (MMP) in gas floods for different gases were computed using empirical correlations (Glaso correlation for hydrocarbon gas injection, Emera, Yuan et al and Glaso correlation for pure carbon dioxide gas injection, Sebastin and Yuan correlation for impure carbon dioxide correlations and Glaso, Firoozabadi and Aziz correlations for nitrogen gas injection). An efficient computer program was developed using visual basic programing language. Employing its highly versatile features, friendly graphical user interface (GUI) forms were designed and robust codes were developed. Validation was done for the program and results showed that the software which was developed had acceptable level of accuracy, was fast and effective. The study provides a new and cost effective way of checking for MMP which will enhance the process of screening gas flooding processes for the reservoir.
+
+
+
+
+Keywords:
+clojure,
+enhanced recovery,
+upstream oil & gas,
+artificial intelligence,
+gas injection method,
+cobol,
+chemical flooding methods,
+programming language,
+correlation,
+mmp
+
+
+Subjects: 
+Improved and Enhanced Recovery,
+Gas-injection methods,
+Chemical flooding methods
+
+
+
+
+INTRODUCTION
+
+
+Enhanced oil recovery (EOR) process involves the injection of fluids into the reservoir which interacts with the system and encourages the displacement of oil to the producing well. (Lake et al., 2014; Jin, 2017; Green et al., 2018; Fanchi, 2018). There are different types of EOR processes employed in the industry and still being researched on (Delamaide et al., 2014; Rao, 2001; Sen, 2008; Taber et al., 1997; Thomas, 2006). Gas injection or gas flooding process is a method of EOR that uses gas (natural gas, nitrogen(N2) or carbon dioxide(CO2)) that expands in a reservoir to displace additional oil to the producing well (Nnaemeka, 2010).There are the miscible and immiscible gas flooding process. Miscible gas flooding is more effective in increasing recovery factor (RF) due to the presence of capillary forces in the immiscible case (Farajzadeh et al., 2010; El-hoshoudy and Desouky, 2018; Mashayekhi et al., 2018).
+
+
+Minimum miscibility pressure (MMP) is the lowest pressure for which a gas can develop miscibility through a multi-contact process within a given reservoir oil at reservoir temperature. Miscibility displacement can only be achieved at pressures greater than this minimum (Moudi et al., 2009; Barati-Harooni et al., 2019). The main factors affecting MMP are: reservoir temperature, oil composition and injected gas purity (Emera and Serma, 2007). Two processes commonly used to develop miscibility during gas injection are first contact miscibility (FCM) and multi-contact miscibility (MCM). The MMP can be determined either by laboratory experiments or numerical studies. (Mansour et al., 2016; Vahid, 2021; Feng et al, 2017). Empirical correlations used for the prediction of MMP in reservoir oils with various types of gas injection provide quick estimates useful during the screening (early stages) of various gas injection processes for the reservoir. Correlations can provide preliminary evaluation and can also be used to verify other method of evaluation, it is suitable for fields with limited amount of data (Leonid et al., 2010; Eakin and Mitchell, 1988).This can also be a limitation as they are unable to capture all the variations in the MMP with regards to the physical aspects. (Karkevandi-Talkhooncheh et al., 2017). As a result, there is room to work on more generalized and robust approaches to develop universally applicable models/correlations, hence is an open area of research. (Barati-Harooni et al., 2019; Kamari et al., 2015; Mollaiy-Berneti, 2016); Chen et al., 2014; Huang et al., 2003; Sayyad et al., 2014; Shokrollahi et al., 2013; Tatar et al., 2013; Zhong and Carr, 2016). Robert et al. (1988) pointed out that any correlation should:
+
+
+account for each parameter known to affect the MMP, i.e. temperature, composition of the displacing and displaced fluid;be based on thermodynamic or physical principles that affect the miscibility of fluids, and finally;be directly related to the multiple contact miscibility process.
+
+
+MMP computation using empirical correlations can be tedious, slow and prone to errors when handled manually. Hence a computerized process is needed just like the many petroleum engineering softwares including those used in EOR processes which has made computations, simulation, prediction and interpretation of data to be fast and efficient. These software written using programming languages like Java, C++, Python and Visual Basic are expensive and not readily available (licensed). Visual Basic is modern, general purpose and object oriented (Dietel et al., 2014). It's easy to learn and has a very simple structure, hence it has been employed in solving problems in reservoir engineering (Akpobi and Ebojoh, 2020).
+
+
+This study aims to develop an efficient algorithm to evaluate the MMP of gases used in miscible gas flooding process. Its objectives are to present relevant mathematical equations (correlations), develop a pseudocode and design a simplified flowchart. Also to design forms (GUI) for input and output and develop efficient codes in order to provide an easy, fast and cheap computerized process that has acceptable level of accuracy.
+
+
+METHODOLOGY
+
+
+Methods used in developing the software are outlined in the following sections
+
+
+Mathematical Equations
+
+
+The correlation used in developing the program were for hydrocarbon gas / oil system, nitrogen gas, pure carbon dioxide gas and impure carbon dioxide gas. The empirical correlations used were selected on the basis of accuracy and simplicity (Nnaemeka, 2010)
+
+
+Hydrocarbon Gas/Oil Systems)
+
+
+Glaso correlations (Glaso, 1985) for predicting MMP for hydrocarbon gas/oil systems are as follows
+
+
+MMPx=34=6329.0−25.410y−z(46.745−0.185y)+T(1.127×10−12y5.258e319.8zy−1.703)(1)
+
+
+MMPx=44=5503.0−19.238y−z(80.913−0.273y)+T(1.700×10−9ye3.73013.567zy−1.058)(2)
+
+
+MMPx=54=7437.0−25.703y−z(73.515−0.214y)+T(4.920×10−14y5.520e21.706zy−1.109)(3)
+
+
+Where:
+
+
+MMP = Minimum miscibility pressure in psig;
+
+
+ x = Molecular weight of C2 through C6 in the injection gas in lbm/lb-mole;
+
+
+ y = Corrected molecular weight of C7+ in the stock-tank oil,
+
+
+ z = Methane in injection gas in mole percent;
+
+
+T = Reservoir temperature in °F.
+
+
+Nitrogen Gas Injection
+
+
+Nitrogen gas (N2) is available, cheap and can be combined with other gases in varying proportion for gas flooding process. Numerous correlations are available for computing its MMP. Glaso MMP correlations for nitrogen gas injection (Glaso, 1985) is given as:
+
+
+For molecular weight of C7+>160, mole percent of intermediates >28, the correlation to use is
+
+
+MMP=6364.0−12.090MC7++T(1.127×10−12MC7+5.258e23,025.0MC7+−1.703−20.80)(4)
+
+
+For molecular weight of C7+>160 and mole percent of intermediates >28, the correlation to use is:
+
+
+MMP=7695.1−12.090MC7++T(1.127×10−12MC7+5.258e23,025.0MC7+−1.703−39.77)(5)
+
+
+If the mole percent of intermediates <28, the correlation to use is:
+
+
+MMP=9364.0−12.090MC7++T(1.127×10−12MC7+5.258e23,025.0MC7+−1.703−20.80)−99.3C2−6(6)
+
+
+Where:
+
+
+MC7+ = molecular weight of the C7+ in the stock-tank oil in lbm/lb-mole;
+
+
+C2-6 = mole percent of the intermediates (C2 through C6) in the reservoir oil.
+
+
+Firoozabadi and Aziz Correlation
+
+
+The Firoozabadi and Aziz correlation (Firoozabadi and Aziz, 1986) for prediction of MMP for nitrogen or lean gas injection is:
+
+
+MMP=9433−188×103(C2−5MC7+T0.25)+1430×103(C2−5MC7+T0.25)2(7)
+
+
+MMP = Minimum miscibility pressure in psia;
+
+
+MC7+ = Molecular weight of C7+ in lbm/lb-mole;
+
+
+C2-5 = Mole percent of C2 through C5 including CO2 and H2S in the reservoir fluid;
+
+
+CO2 Gas Injection
+
+
+Research showed that CO2 is a better candidate for gas flooding because it possesses the ability to develop multi-contact miscibility (MCM) with oils at lower pressures than Nitrogen, also an important source of greenhouse gas emissions which needs to be reduced from the environment (Gozalpour et al., 2005; Dindonuk et al, 2020 and 1997; Heidary et al 2016]. Robert et al. (1988), gave a list of many correlations that have been used in computing for MMP of CO2. Important parameters that could influence the MMP include temperature, oil composition and the contaminants present in the CO2.
+
+
+Pure Carbon Dioxide Gas Injection
+
+
+Correlations selected for the computation of MMP for impure CO2 were the Glaso correlation, Emera and Sarma correlation, and the Yuan Correlation.
+
+
+Glaso Correlation
+
+
+For mole composition of C2 through C6 greater >18%, the Glaso correlation (Glaso, 1985) is
+
+
+MMPpure=810.0−3.404MC7++T(1.700×10−9MC7+3.730e786.8MC7+−1.058)(8)
+
+
+For mole composition of C2 through C6 < than 18%, the Glasø correlation is
+
+
+MMPpure=2947.9−3.404MC7++T(1.700×10−9MC7+3.730e786.8MC7+−1.058)−121.2C2−6(9)
+
+
+Emera Correlation
+
+
+The Emera Correlation (Emera and Serma, 2005) for pure CO2 Injection is represented as:
+
+
+MMPpure=5.0093×10−5×(1.8T+32)1.164×MC5+1.2785×(CC1+N2CC2−4+H2S+CO2)0.1073(10)
+
+
+If the bubble pressure (Pb) < 50 psi, the Emera correlation for pure injection becomes:
+
+
+MMPpure=5.0093×10−5×(1.8T+32)1.164×MC5+1.2785(11)
+
+
+Where:
+
+
+MMPpure= Minimum miscibility pressure for pure CO2 in MPa;
+
+
+MC5+ = Molecular weight of the C5+ in the stock-tank oil in lbm/lb-mole;
+
+
+CC1+N2= mole fraction of the volatiles (C1 and N2) in the reservoir oil;
+
+
+CC2−4+H2S+CO2= Mole fraction of the intermediates (C2, C3, C4, H2S, and CO2) in the oil.
+
+
+Yuan Correlation for Pure CO2 Injection Gas
+
+
+Yuan correlation (Yuan et al., 2005) is given as
+
+
+MMPpure=a1+a2MC7++a3pc2−6+(a4+a5Mc7++a6Pc2−6M2cc7+)T+(a7+a8Mc7++a9M2c7++a10Pc2−6)T2(12)
+
+
+Where a1= −1.4364E+03; a2=0.6612E+01; a3= -4.4979+01; a4= 0.2139 E+ 01; a5= 1.1667 E-01; a6= 8.1661 E+03; a7=−1.2258 E-01; a8=1.2883 E-03; a9= −4.0152 E-06; a10= −9.2577 E- 04
+
+
+PC2-6 = mole percent of C2 to C6,
+
+
+Impure Carbon Dioxide Gas Injection
+
+
+Correlation considered were Sebastin and Yuan Correlation (Sebastin et al, 1985, Yuan et al, 2005)
+
+
+Sebastin Correlation for Impure CO2
+
+
+T−CM=∑ixiTci(13)
+
+
+MMPimpureMMPpure=1.0−2.13×10−2(T−CM−304.2)+2.51×10−4(T−CM−304.2)2(14)
+
+
+Yuan Correlation
+
+
+MMPimpureMMPpure=1+m(PCO2 −100)(15)
+
+
+Where
+
+
+m=a1+a2MC7++a3pc2−6+(a4+a5Mc7++a6Pc2−6M2cc7+)T+(a7+a8Mc7++a9M2c7++a10Pc2−6)T2(16)
+
+
+Where a1= −6.5996E-02; a2=−1.5246-04; a3= 1.3807-03; a4= 6.2384 E- 04; a5= −6.7725 E-07; a6= −2.7344 E-02; a7=−2.695 E-06; a8=1.7279 E-08; a9= −3.1436 E-11; a10= −1.9566 E- 08
+
+
+Software Design and Development
+
+
+The mathematical expressions for the empirical correlations in equations (1) - (16) were used to write most of the codes for the software. Employing the unique syntax that Visual Basic offers, efficient codes were developed as observed from the steps outlined in the pseudocode. The program has several forms each designed to achieve a specific objective. Figure 1 depicts a simplified flow chart for the program.
+
+
+Figure 1View largeDownload slideProgram's Flow ChartFigure 1View largeDownload slideProgram's Flow Chart Close modal
+
+
+The Design of Graphical User Interface (GUI Forms)
+
+
+The objective of the design of the form was for ease and simplicity. Several forms were designed for computation of MMP for hydrocarbon gas, nitrogen gas, pure carbon dioxide gas and impure carbon dioxide gas, using different correlation methods. The design was customized for each form via the property window and toolbox, it involves selection and placement of input and output labels, textboxes and control buttons. The programs opens with an introduction form page designed to give the user a summary of what the software can do as shown in Figure 2. Option of selecting the type of gas is also available. The other forms were designed each for a particular type of gas with different methods of computing for its MMP.
+
+
+Figure 2View largeDownload slideScreenshot of Welcome Page for the ProgramsFigure 2View largeDownload slideScreenshot of Welcome Page for the Programs Close modal
+
+
+The Output Window
+
+
+When the user inputs the correct parameters and clicks the calculate control button, the program computes and outputs the answer in the output section on the GUI at run time
+
+
+Program's Pseudocode
+
+
+The computer program were written following the step outlined in the pseudocode
+
+
+Start
+
+
+Select type of gas
+
+
+Hydrocarbon gasNitrogen gasImpure CO2 gasPure CO2
+
+
+Select method of computing MMP
+
+
+Hydrocarbon Gas
+
+
+Select Glaso correlation
+
+
+If Textbox1 to n .text is ( ) Then
+
+
+Input Parameters: X, z, Y, T
+
+
+Compute Mol at 1st 2nd and 3rd Point
+
+
+Compute MMP @ points
+
+
+If input Mol C2-C6 ≤ mol @1st point
+
+
+Call Function INTP.
+
+
+ElseIf input Mol C2-C6 > Mol @1st point
+
+
+Call Function EXPL,
+
+
+Compute MMP @ given Mol C2 - C6
+
+
+Else Msgbox.show ( )
+
+
+Output
+
+
+EndifEndif
+
+
+NITROGEN GAS
+
+
+Select correlation (Glaso / Firoozabadi)
+
+
+Input Mol % of C2-C5, T, MW C7+ in oil, PBP of oilCompute MMPOutput
+
+
+PURE CO2 GAS
+
+
+Select correlation (Emera / Glaso / Yuan.)
+
+
+If Textbox1 to n .text is ( ) Then
+
+
+Input Mol % of C1 in oil, T, MW of C5+_
+
+
+Compute MMP
+
+
+Else Msgbox.show ( )Endif
+
+
+Output
+
+
+IMPURE CO2 GAS
+
+
+Select Correlation (Sebastin / Yuan)
+
+
+If Textbox1 to n. text is ( ) Then
+
+
+Input parameters
+
+
+Initialize Correlation constants.
+
+
+Compute MMP
+
+
+Else Msgbox.show ( )Endif
+
+
+Output
+
+
+END
+
+
+RESULTS AND DISCUSSION
+
+
+Using data from Table 1, a demonstration of the program's efficiency was tested, for hydrocarbon gases using the Glaso correlation and for nitrogen gases using Glaso, Firoozabadi and Aziz Correlations, screenshot of the output form at runtime showed good results were obtained as depicted in Figure 3 and Figure 4.
+
+
+Table 1Examples for computation of MMP (Nnaemeka, 2011) Parameters
+            . Hydrocarbon Gas
+            . 
+            . Nitrogen gas
+            . Pure Co2
+            . Impure Co2
+            . Molecular Weight of C2–C6 lbm / lb-mole 32  27.17 24 23.62 Mole % of CH4 in injection gas Mixture 65.2  - 4.4 10 Molecular Weight of C7+, lbm / lb-mole 224  193.3 234 240 Molecular Weight C5+, lbm / lb-mole -  - 185 - Mole % of CO2 in Injection Gas -  - - 90 Temperature, °F 160  164 180 106 Bubble Point Pressure, Psi -  4000 - - Parameters
+            . Hydrocarbon Gas
+            . 
+            . Nitrogen gas
+            . Pure Co2
+            . Impure Co2
+            . Molecular Weight of C2–C6 lbm / lb-mole 32  27.17 24 23.62 Mole % of CH4 in injection gas Mixture 65.2  - 4.4 10 Molecular Weight of C7+, lbm / lb-mole 224  193.3 234 240 Molecular Weight C5+, lbm / lb-mole -  - 185 - Mole % of CO2 in Injection Gas -  - - 90 Temperature, °F 160  164 180 106 Bubble Point Pressure, Psi -  4000 - - View Large
+
+
+Figure 3View largeDownload slideScreenshot of Graphical User Interface for computing MMP for hydrocarbon gasesFigure 3View largeDownload slideScreenshot of Graphical User Interface for computing MMP for hydrocarbon gases Close modal
+
+
+Figure 4View largeDownload slideScreenshot of Graphical User Interface for computing MMP for Nitrogen gasFigure 4View largeDownload slideScreenshot of Graphical User Interface for computing MMP for Nitrogen gas Close modal
+
+
+Figure 5View largeDownload slideScreenshot of Graphical User Interface for computing MMP for pure CO2 gasesFigure 5View largeDownload slideScreenshot of Graphical User Interface for computing MMP for pure CO2 gases Close modal
+
+
+Computations of MMP for pure CO2 gases was done using Emera, Glaso and Yuan et al. correlations, while the MMP for impure CO2 was estimated using Sebastin and Yuan et al. correlation screenshot of output forms from Figure 6 Results gave an acceptable level of accuracy.
+
+
+Figure 6View largeDownload slideScreenshot of Graphical User Interface for computing MMP for impure CO2 gasesFigure 6View largeDownload slideScreenshot of Graphical User Interface for computing MMP for impure CO2 gases Close modal
+
+
+The programs has features that will prompt the user to input all parameters correctly before computing the MMP as shown in Figure 7.
+
+
+Figure 7View largeDownload slideScreenshot of program's featuresFigure 7View largeDownload slideScreenshot of program's features Close modal
+
+
+Validation
+
+
+Validation of this work was done by comparing results with literature (Vahid and Siavash, (2021) a study done using Mathlab) and manual computation. Results were obtained quickly and comparison could be made using other correlations. Acceptable level of accuracy was obtained as shown in Table 2 and Table 3 
+
+
+Table 2Validation of the Software Program with Manual Computation CORRELATION FOR CALCULATING MMP
+            . Manual Computation
+            . Software (This Study)
+            . % Error
+            . Glaso correlation for hydrocarbon gas injection 3536.70 3535.992 .0292 Emera correlation for pure carbon dioxide gas injection 12.900 12.717 1.439 Firoozabadi and Aziz correlation for nitrogen gas injection 4422. 30 4421.603 .0158 CORRELATION FOR CALCULATING MMP
+            . Manual Computation
+            . Software (This Study)
+            . % Error
+            . Glaso correlation for hydrocarbon gas injection 3536.70 3535.992 .0292 Emera correlation for pure carbon dioxide gas injection 12.900 12.717 1.439 Firoozabadi and Aziz correlation for nitrogen gas injection 4422. 30 4421.603 .0158 View Large
+
+
+Table 3Validation of the Software Program (Glaso Correlation for pure and impure CO2) T °F
+            . Mole % of CH4 in injection gas Mixture
+            . Molecular Weight of C2–C6 lbm / lb-mole
+            . Molecular Weight C5+, lbm / lb-mole
+            . Molecular Weight of C7+, lbm / lb-mole
+            . Software (This Study) MMP
+            . MATLAB (Vahid and Siavash, 2021)MMP
+            . % Error
+            . 103 28 30 200 223 1376.066 1376 0.00479 109 17 13 204 222 1450.403 1450 0.0278 T °F
+            . Mole % of CH4 in injection gas Mixture
+            . Molecular Weight of C2–C6 lbm / lb-mole
+            . Molecular Weight C5+, lbm / lb-mole
+            . Molecular Weight of C7+, lbm / lb-mole
+            . Software (This Study) MMP
+            . MATLAB (Vahid and Siavash, 2021)MMP
+            . % Error
+            . 103 28 30 200 223 1376.066 1376 0.00479 109 17 13 204 222 1450.403 1450 0.0278 View Large
+
+
+CONCLUSION
+
+
+In this study, an algorithm to compute MMP for gases used in gas flooding process was designed and developed using Visual Basic programming language. It can estimate MMP for hydrocarbon gas, nitrogen gas, pure and impure CO2 gas. Relevant mathematical equations (empirical correlations) were presented, pseudocode and flow chart were designed to outline the steps used for developing the program. Friendly graphical user interface forms were designed, which provided a virtual platform for inputs / output at runtime and efficient codes were developed. Numerous examples were used to demonstrate the efficiency of the program. Validation was done and the program's solutions were observed to have acceptable level of accuracy. The program developed in this study is flexible, can be updated and used as a learning tool for students. It has provided a new, cheap and fast means for checking for MMP in miscible gas flooding process which can aid the process of screening the reservoir for suitable gas flooding candidate.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+NOMENCLATURE
+
+
+NOMENCLATUREABBREVIATIONEXPANSION MMPminimum miscibility pressure C1methane C2ethane C4butane C5+pentane plus C6hexane C7+heptane plus EXPLextrapolation function INTPinterpolation function MWmolecular weight PBBubble point pressure H2Shydrogen sulphide gas
+
+
+ACKNOWLEDGMENTS
+
+
+Special thanks to all individuals and organizations that provided materials and facilities for this research.
+
+
+References
+
+
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211973-MS
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files/2022/An Integrated Approach To Production Optimization In Ageing Gas Lifted Fields- Ikanto Field Experience.txt

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+----- METADATA START -----
+Title: An Integrated Approach To Production Optimization In Ageing Gas Lifted Fields- Ikanto Field Experience
+Authors: Benjamin Obong, Pius Adegoke, Soba Osuji-Bells, David Ogbonna, Hassan Salisu, Onyinyechi Ekerenduh, Segun Adomokhai
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211961-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+In many ageing fields, there is a constant need to optimize production performance of the wells to ensure that they continue to deliver value. As a field matures, with high water and sand cut production from the wells, water breakthrough from water flooding projects and other artificial pressure maintenance programs, the produced fluid water cut and gas-oil ratio will be changing. For such fields, the optimal use of existing surface facilities is critical to sustaining and increasing well rates leading to a corresponding reduction in production costs.In the Ikanto field, Gas Lift is the preferred artificial lift method, and has been so for over twenty years. However, with increased water production from the wells, the field separating train is faced with handling produced water in the separator train. Other challenges in the gas lift system including obsolete field metering equipments, meter calibration and maintenance challenges, etc, have impacted optimization opportunities from the gas lifted wells. The resulting consequence is the inability to fully determine optimal lift gas injection rates if the lift gas injection into the well is over or under-injected in line with advised lift gas rates from well performance models. An important input for gas lift optimization is the volumetric flow rate of injection gas. This data can help experienced Production engineers and field technicians determine if the lift gas injection into the well is optimal, thus providing directional guidance on what change(s) should be made to improve the well performance.In order to ensure that the asset value is enhanced, an integrated approach to maximizing production from the field was deployed ranging from the upgrade and automation of the existing gas lift infrastructure in the field vis-à-vis carrying out gas lift system optimisation, carrying out de-bottlenecking of parts of the production system, and the installation of real time surface monitoring systems.In this paper, the results of the optimization efforts in the Ikanto field are discussed. The analysis of the results has resulted in an upscale of total daily production from the field by over thirty percent (30%).
+
+
+
+
+Keywords:
+upstream oil & gas,
+manifold,
+integrated approach,
+gas lift,
+artificial lift system,
+reservoir,
+gas injection,
+ikanto-1 cpf,
+operation,
+nigeria annual international conference
+
+
+Subjects: 
+Artificial Lift Systems,
+Improved and Enhanced Recovery,
+Gas lift,
+Well Operations and Optimization
+
+
+
+
+Introduction
+
+
+The Ikanto field is an onshore field in the Niger Delta. It was discovered in 1959 and came on stream in August 1961. The field attained peak production of 110 kbopd in 1970. Production decline set in shortly afterwards and by 1983, the average field production had dropped to about 27 kbopd. A drilling and workover campaign kicked-off in 1989 and brought the field production up to 50 kbopd. This however declined to an average production of 25 kbopd by 1998. A field review carried out in 1998 resulted in a horizontal drilling campaign in 2000 – 2002, which increased the field production back to 50 kbopd. The field is however on the decline again with increasing water cut and the current average production is 12.5 kbopd.
+
+
+Cumulative production of 723 MMstb of oil has been produced from 104 wells with 37 currently producing, out of which 20 wells are on gaslift. The field comprises multiple stacked reservoirs, and gravel pack is used as sand control mechanism for most of the wells. The oil is under saturated with 18-20 °API in shallow reservoirs and 22-30 °API in deeper reservoirs. The initial solution gas-oil ratio ranges from 113 to 2660 scf/bbl. The reservoirs are generally shoreface and channel deposits with average porosity of 18-33%, permeability of 500-3000mD, and oil viscosity of 0.14-17.6cp. The reservoir is supported by a strong water drive, with about 54% of the wells requiring artificial lift due to prevailing fluid properties and continuously increasing water cut.
+
+
+The Ikanto field with over sixty (60) years of production was first kicked-off on Gas Lift in 1992 to increase oil production through improved vertical lift performance of existing wells that have attained the limit of natural flow. The field comprises of a large comprehensive and distributed pipeline network, and the field production infrastructure consists of three production processing facilities viz Ikanto-1, Ikanto-2 and Ikanto-3 central processing facilities (CPFs). The Ikanto-1 CPF is a double-bank facility with sixty thousand barrels (60kbbls) capacity and operates a two-phase separation philosophy. The gas lift manifold is located centrally at the Ikanto-1 CPF, from where high pressure compressed gas at 1015psi is supplied from an associated gas gathering plant and sent through the gas lift manifold via 2" gas lift lines to the various gas-lifted wells. The wells consist of both single and dual string completions, with dual string completions accounting for eighty-five (85%) of the total gas-lifted well count. Continuous gas lift is presently deployed in the wells that contribute 51% of the total field production.
+
+
+Gas-lifted oil contribution to the overall field's net oil production has not been sustained due to a wide range of problems. The notable ones have been poor uptime of dehydration unit within the Associated Gas Gathering plant (AGG), limited lift gas metering and control capability, and ageing gas lift infrastructure. Others have ranged from sub-optimal well designs and legacy completion strategy, to limited collaboration and slow restoration of wells with deferment. Also, poor/reactive surveillance, dual string optimization challenge, gaslift competence of key personnel in production operations and production technology disciplines, changes in production rates and fluid properties, have made steady and predictable production a challenge. An efficient gas lift system management and optimization is therefore integral to maximizing asset value.
+
+
+To gain a better understanding of the issues contributing to the under-performance of the gas lift system in the Ikanto field, and secure management/ asset ownership of the identified issues, a Gaslift Health check and opportunity framing workshop was held within the Ikanto Asset team using the well and reservoir management (WRM) value loop tool methodology shown in Figure 1 below:
+
+
+Figure 1View largeDownload slideWell and reservoir management (WRM) value loop tool methodologyFigure 1View largeDownload slideWell and reservoir management (WRM) value loop tool methodology Close modal
+
+
+The results from the health checks/workshops revealed that a multi-disciplinary team cutting across subsurface, surface, operations, community liaison, well intervention and asset engineering will be required to fix the problems with equipment, facilities, wells, skills, operational practices, workflows and accountabilities. Consequently, a multi-disciplinary team was constituted to drive the improvement initiatives. Key issues identified during the health checks were translated into an improvement plan that formed the basis of the ongoing improvement activities. These ranged from carrying out an upgrade of the existing gaslift system, gaslift optimization, deployment of real time tools and process de-bottlenecking.
+
+
+Gas Lift System Status Assessment and Improvement Identification
+
+
+As part of the health check, a status report was generated, and improvement opportunities identified as seen in the populated WRM value loop shown in Figure 2 below
+
+
+Figure 2View largeDownload slideUpdated Well and reservoir management value loop toolFigure 2View largeDownload slideUpdated Well and reservoir management value loop tool Close modal
+
+
+The focus of the multi-disciplinary team was to understand the fundamental cause(s) of the underperformance of the gas lift system and proffer short, medium and long-term improvement solutions. The team was also to set up structures, tools and systems that will sustain and embed best practices for an efficient system going forward.
+
+
+Based on the reviews carried out by the Ikanto field multi-disciplinary team, it was decided that the improvement initiatives be divided into four (4) phases viz:
+
+
+Gas Lift system revamp (includes enhanced well testing capability for gas-lifted wells and installation of Ikanto-1 CPF water cut meter)Gas Lift Optimization (GLOP)Process De-bottleneckingValidation of gains from the GLOP activities/report-out.
+
+
+Revamping of Gas Lift System in the Ikanto Field
+
+
+There were a lot of challenges with the gas lift infrastructure in the Ikanto field, despite an upgrade from manual gaslift metering in 2011 to an automated manifold fully equipped with flow control valves (FCVs) (Figure 3). These challenges stemmed from the lack of a robust maintenance strategy for the automated gas lift manifold leading to slow response time to address instrumentation issues, non-responsive FCVs, non-calibration of FCVs that have drifted, and asset ownership of the installed hardware. Usually for un-responsive or non-functional FCVs, the by-pass valves of each ligament was usually opened to allow for manual operation with no handle on the amount of lift gas being injected into each gas lifted well. This has led to over-injection in most cases resulting in sub-optimal production.
+
+
+Figure 3View largeDownload slideIkanto 1 CPF central automated gas lift manifoldFigure 3View largeDownload slideIkanto 1 CPF central automated gas lift manifold Close modal
+
+
+Another challenge was also the lack of integration of the automated gas lift manifold with the existing well testing module installed in the Ikanto field in 2006. This impacted on the accuracy of lift gas injected volumes recorded during well testing of the gas lifted wells.
+
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+The material difference between a conventional gas lift system and an automated gas lift system is that the later helps to improve efficiency and smoothen gas injection to each well for maximised oil production. It is an acknowledged fact worldwide that wherever the automation in continuous gas lift system has been introduced, the oil production has increased from 5 to 35%. On introduction of gas lift automation, the benefits like automatic control of the quantity of lift gas injection in individual well as per the well requirement, optimum gas allocation to each well, easy and quick monitoring, troubleshooting of gas lift operation on individual well basis, and overall increase of oil production, can be derived (Kumar et al., 1996). The other additional benefit is the conservation of energy, as excess gas can be exported for sales.
+
+
+The focus of the revamp was to complete the integration of the Ikanto Gaslift DCS ((Distributed Control System) with the Ikanto-1 CPF DCS for well test volume measurement particularly the injected gas volume, and to also restore the functionality of the FCVs.
+
+
+Well testing procedures are carried out monthly for all oil producing wells at Ikanto-1 CPF to evaluate the performance of each oil well. The test data is used for production forecasts as well as other business-related decisions. The current data set provides the average gross oil, average net oil, average water cut (BS&W) and average gas flow rates for each oil well that is put on test. However, with the introduction of continuous gas lift in many depleted oil wells in the field, the current well test of the DCS application logic does not reflect the actual formation gas production performance. The gas production data for these gas-lifted wells is rather a combination of the injected lift gas and the formation gas, with the lift gas component usually estimated by an experienced operator, using the equations below:
+
+
+For gas lifted wells:
+
+
+Formation Gas Volume, GVfmt≈GVprd−GVinj
+
+
+Where GVinj is Injected Gas Volume i.e the total gas volume that is injected into a well.
+
+
+For non-gas lifted wells:
+
+
+Formation Gas Volume, GVfmt≈GVprd
+
+
+Where GVprd is Production Gas Volume (i.e the total gas volume produced from a well), and
+
+
+GVfmt is Formation Gas Volume i.e the total gas volume that is produced by a well excluding the total gas volume injected for gas-lifted operations.
+
+
+The intent of the well test optimization project was to correct this anomaly, with the aim being to put the injected gas volumes into consideration during well testing, which will help determine the actual formation gas production rates for any gas lifted well that is put on test.
+
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+Figures 4 and 5 below depicts the schematics of the outcome of the integration activities:
+
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+Figure 4View largeDownload slideIntegration ArchitectureFigure 4View largeDownload slideIntegration Architecture Close modal
+
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+Figure 5View largeDownload slideIkanto-1 CPF Test Separator-1 Well Test Status GraphicsFigure 5View largeDownload slideIkanto-1 CPF Test Separator-1 Well Test Status Graphics Close modal
+
+
+Gas Lift Optimization
+
+
+An important input for gas lift optimization is the volumetric flow rate of injection gas. This data can help experienced Well analysts and gas lift technicians determine if the lift gas injection into the well is optimal in line with advised lift gas rates from models, thus providing directional guidance on what change(s) should be made to improve a well's performance. The volumetric flow rate can be used to estimate the transit time of injected gas, which can then be combined with other tools to determine downhole injection points. An accurate and reliable flow measurement is vital in optimising the injection rates. Too little gas injection and the oil production will be reduced, while too much gas injection nevertheless also impedes production. At the same time, the excess gas can't be exported for sales (Obong et al., 2018).
+
+
+In the Ikanto field, the operational practice of managing gas-lifted wells is to flow them with chokes installed in the bean boxes as advised by the subsurface team thus having the flow rate of each well choke-controlled rather than injected lift gas rate-controlled, apparently because of the sub-optimal performance of the Gas Lift system and lack of confidence of the gas lifted well test data especially with respect to measured injected gas during testing. This operation was an abberation as it was like applying the ‘brakes’ of a car and ‘accelerating’ at the same time.
+
+
+With the completion of the gas lift system revamp, the focus of the optimization effort was to
+
+
+Reduce back pressure on the wells through bean-ups for gas lifted wells with chokes on them.Carry out multi rate testing (MRT) with injected lift gas as the driver, vis-à-vis sand and water cut monitoring. This is to get a baseline data for all the 20 gas lifted wells in the field for Well performance model update.Carry out BHP surveys for the gas-lifted wells and develop gas lift valve change-out (GLVCO) proposals where applicable.Restoration of wells on deferment (due to flowline cuts, gas lift line vandalization and wellhead issues) and kick-off wells on gas lift.Restoration of wells on deferment, using new tools and technologies (venturi orifice, foam-assisted gas lift, soap sticks, Echometer, etc), especially for dual completions on gas lift due to gas sharing issues.
+
+
+A staircase implementation plan tranching each of the above components was developed, and is seen in figure 6 below
+
+
+Figure 6View largeDownload slideGaslift Optimization Staircase Implementation PlanFigure 6View largeDownload slideGaslift Optimization Staircase Implementation Plan Close modal
+
+
+Process De-bottlenecking
+
+
+To sustain gains from the gas lift system upgrade and optimization (including engineering activities via flowline and gas lift line repairs), the Ikanto operations team in collaboration with the process engineering team carried-out a Do-it-Yourself (DIY) campaign to de-sludge the gas lift manifold and remove restrictions from the inlet manifold of Ikanto-2 and Ikanto-3 CPFs which had earlier being re-routed to Ikanto-1 CPF as part of ullage optimization strategy. With the re-routing of the flow from both Ikanto-2 and Ikanto-3 CPF, and mothballing of the two facilities, the orifice plates earlier installed in the inlet manifold of both facilities for real time monitoring became redundant.
+
+
+With a view to optimize production, the Ikanto field Integrated Production System Model (IPSM), which also incorporates the surface network model, was used to evaluate the pressure losses and back-pressure impact on the wells due to the presence of the orifice plates. It was observed that these orifice plates caused significant back-pressure impact on the declining gas-lifted wells, resulting in suboptimal production from the wells. As predicted by the IPSM, the removal of the orifice plates from the well flowlines at the inlet manifold of the CPF resulted in less back-pressure to the wells and increased production rates from the wells to the central Ikanto-1 CPF. Figure 7 below is a snapshot of the Ikanto field IPSM (showing the reservoirs, wells and surface network) post the process de-bottlenecking activities.
+
+
+Figure 7View largeDownload slideIkanto Field Integrated Production System ModelFigure 7View largeDownload slideIkanto Field Integrated Production System Model Close modal
+
+
+Field Execution Strategy- Gas Lift Optimization Gains
+
+
+The approach of the Ikanto asset team was to first carry out a baseline production data survey of the wells producing on gas lift, which served as input into the models to be developed for the gradual choke removal exercise vis-à-vis well testing and sand cut monitoring.
+
+
+The plan was to execute a stepwise bean-up approach, while monitoring water cut and sand cut. This was done in four phases (5 wells per phase), based on sand cut performance, up to the final proposed bean size of 64/64″. After each bean-up, the wells were allowed to produce on the new choke size for up to a week for proper well and reservoir management. Table 1 below shows the bean sequence of the first phase of wells, and the estimated gain (480 bopd). Table 2 shows the actual gains from the bean up sequence vis-à-vis using the optimized lift gas rates after completion of the multirate tests (MRT).
+
+
+Table 1Bean-up sequence of gas lifted wells   View Large
+
+
+Table 2Actual gains from the bean up sequence of gas lifted wells   View Large
+
+
+A Multi-rate test was also conducted for all the gas-lifted wells, and the results documented. Some gas-lifted wells whose flowlines and gas lift lines were vandalised, were restored ex-engineering activities (sectional replacement of flowlines and gas lift lines) and kicked-off on gas lift as well.
+
+
+The actual work execution of the opportunities deployed the Lean methodology (ANDON BOARD) where process lead times (PLT) were monitored from opportunity identification to execution, and this helped in pulling various sections of the multidisciplinary team to achieve a smart delivery of the project objectives. The table below (Table 3) shows the total gains from the Gas Lift optimisation activities in the Ikanto field. Key activities that contributed to these gains were bean-ups of the gas lifted wells, back pressure reduction in existing wells, kick-off of new wells on gaslift, liftgas optimization, gaslift line repair, flowline repairs and well head equipments repairs just to mention but a few.
+
+
+Table 3Gas Lift Optimisation Activities Gains in the Ikanto Field   View Large
+
+
+Figure 8 below is a graphical representation of the Andon board showing the integrated approach deployed by the multidisciplinary team (Asset development team, Asset engineering, Community/External relations, Production, Programming) to achieve a smart delivery of the project.
+
+
+Figure 8View largeDownload slideProcess Lead Times by Deploying Integrated Approach for the Gaslift Optimisation ProjectFigure 8View largeDownload slideProcess Lead Times by Deploying Integrated Approach for the Gaslift Optimisation Project Close modal
+
+
+An analysis of the gains earlier presented in Table 3 above, clearly paints a clear impact of the revamped gas lift system at the Ikanto field. Although the expected gains from some few wells were below target, many of the wells delivered above target. Figure 9 below shows the production gain staircase plot by activity, and the attendant impact on the bottom line.
+
+
+Figure 9View largeDownload slideIkanto Field Production Growth Staircase PlotFigure 9View largeDownload slideIkanto Field Production Growth Staircase Plot Close modal
+
+
+Following the full implementation of the Gas lift optimisation project, an added focus was to enhance the capability of field staff to carry out proactive monitoring of both well and process performance. As part of the digitilisation drive, a Water cut meter (WCM) was installed at the crude oil metering skid of the Ikanto-1 CPF. Other tools included the use of hand-held analytical tools for technicians and field engineers such as Smartconnect PI tools etc. This was to provide real time proactive monitoring of both the well performance and the process system to allow for quick response to situations where the process vessels set points are outside the operating envelope. This is particularly key during a process re-start after a trip or the trip of the gas gathering plant leading to the cut back of lift gas supply to the wells. The drive was to deploy a robust Water cut creaming strategy where high water cut gas-lifted and natural flow wells will be closed first, during process upsets and opened last ex-facility re-start. For a typical start-up of the facility, it takes 48 to 96hrs to stabilise the gaslifted wells post re-start of the gas lift manifold. The focus was to open-up low water cut natural flowing wells, open up gaslifted wells with natural flow potential with lower water cut, stabilise the wells for one (1) to three (3) days, before bringing in higher water cut natural flow wells. This will be closely followed by the kick-off of gas lift critical wells (these are wells that will not flow without lift gas), and eventually all other gas lifted wells.
+
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+With the use of smart tools in the hands of both operations and maintenance technicians in the field, it was easier to sustain the banked gains by following an integrated approach to facility start-up and well optimisation after open-up ex-trips or process upsets. With the real time proactive monitoring, peak net production was achieved faster between three (3) to five (5) days when compared with seven (7) to ten (10) days achievement of peak production without the use of smart tools. Wells’ sub-optimal performance, wells’ trip, facility equipment trips (e.g export pumps etc) can now be quickly detected and re-instated.
+
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+A seven (7) month trend data of open-ups after process shut- downs/trips highlighting the impact that digitilisation tools have helped in quick production restoration and optimisation is presented in Figure 10 below.
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+Figure 10View largeDownload slideIkanto-1 CPF Production Trend showing the Impact of Real time tools in Quick Production RestorationFigure 10View largeDownload slideIkanto-1 CPF Production Trend showing the Impact of Real time tools in Quick Production Restoration Close modal
+
+
+Learnings
+
+
+Gas lift systems reward regular attention because there are many reasons why a system may benefit from attention. They can be reservoir— issues may be related to the change in fluid composition or production characteristics over time; mechanical—valves can stop steadily injecting gas at the correct pressure or tubing leaks can release it; or be design-related—a standard downhole injection system might not be sized correctly for that well (Rassenfoss, 2014). However, the beauty of gas lift is that minimal intervention is required because it can handle a wider range of production conditions versus other artificial lift methods. When a gas lift system starts performing poorly, there is a good chance no one will notice. It is not an event that demands attention like a broken pump (Rassenfoss, 2021). A gaslift system may continue injecting gas and oil production even with suboptimal functioning of some of its component parts’. Optimization is therefore key if we are to get the most from the asset and ensure that ‘we do not leave anything on the table’. A further benefit seen from the revamp of the gas lift manifold was a reduction in lift gas injected volumes, which increases capacity within the production piping for additional produced liquids, and associated gas, as well as availability of the excess gas for sales.
+
+
+To help keep focus on the gas lift system in the Ikanto field, a maintenance plan was developed and loaded into the preventive/corrective (PM/CM) maintenance database to allow for work order callups to enable the maintenance and operations teams carry out 1-weekly, 1-monthly, 3-monthly, 6-monthly and 1-yearly PMs/CMs activities. Also, a visual log First line maintenance (FLM) book was developed for the Operations team to carry out daily FLMs during their walkrounds in the Ikanto-1 CPF.
+
+
+Conclusion
+
+
+The success of the Ikanto Field production optimization project is due to an integrated effort between subsurface, operations, maintenance, programming, surface engineering and community liaison teams. The external coordination and support from the Gaslift focused delivery team provided an external perspective and technical support for the delivery within the Asset teams. The timely revamp of the Gaslift manifold skid, flowline/gas lift line repairs, improvement in gas dehydration unit uptime, capability building, robust Integrated production system modelling and rollout of the integrated well test system for gas lifted wells were key enablers. The project, by work activity resulted in a total gain of 5100 bopd which translates to over thirty (30%) percent production increase.
+
+
+The success of the project has given Production engineers, well analysts and Plant operators increased confidence to optimise gas lifted wells in the field. The next phase of the project will see the integration of the visual display FLM as part of the hand-held Smart tools that have been deployed in the facility. Also, to effectively manage dual string gas lifted wells, venturi orifice valves will be deployed to help improve gas lift performance of the dual string wells (Nwadike et al., 2014), as well as other gas lift diagnostic tools like the implementation of well tracer technology for exception-based surveillance to help sustain the already achieved gains. With the new data collected from BHP surveys and during multi rate tests, the field operators are now able to balance the gas lift system and carry out a focused and precise process optimisation. Now, the right amount of lift gas for optimal gas lift can be injected, while excess gas can be exported for sales.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgements
+
+
+The authors wish to express their appreciation to the management of Shell Petroleum Development Company of Nigeria Ltd for the permission to present and publish this paper.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion WRMWell and Reservoir Management FCVFlow Control Valve GLOPGas Lift Optimisation WCMWater Cut Meter DCSDistributed Control System MRTMultirate Tests MSCFThousand Standard cubit feet MMSCF/DMillion Standard cubic feet per day BOPDBarrels of Oil Per Day PIPlant Information STBStock Tank Barrel CPFCentral Processing Facility IPSCIntegrated Production System Capacity GLGas Lift FLLFlowline leaks FLMFirst Line Maintenance
+
+
+References
+
+
+Kumar, A., Singh, R.S.K.De & MalhotraB.D. (1996) "Automation of Gas Lift Operation in Bombay Offshore Fields", Presented at the 7th Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, U.A.EOctober 13 –16.Google Scholar Obong, B., Ogoleh, F., Ekine, T., Azibapu, F., (2018), "The Use of Clamp-on Flowmeter In Gas Lift Operations-Badan Field Experience", Presented at the Society of Petroleum Engineers Nigeria Annual International Conference and Exhibition, SPE Paper #193413, August 6-8.Google Scholar Rassenfoss, S. (2014) "Paying Close Attention To A Gas Lift System Can Be Rewarding", Journal of Petroleum TechnologyNovember30th.Google Scholar Rassenfoss, S. (2021) "Now Is The Time For Gas Lift To Live Up To Its Potential", Journal of Petroleum TechnologyMay1st.Google Scholar Nwadike, N., Ofia, I., Essien, U., Obong, B., Amos, T., Oton, E., Sheri, A., (2014), "Enhancing The Production Performance Of Dual Completed Gas Lifted Wells Using The Nova Venturi Orifice Valves’’, Presented at the Society of Petroleum Engineers Nigeria Annual International Conference and Exhibition, SPE Paper #172451, August 5-7.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211961-MS
+
+
+

files/2022/Application of Gassmanns Model and the Modified Hashin-Shtrikman-Walpole Model in Land Subsidence Susceptibility Studies in the Jxt Field Niger Delta.txt

@@ -0,0 +1,413 @@
+----- METADATA START -----
+Title: Application of Gassmann's Model and the Modified Hashin-Shtrikman-Walpole Model in Land Subsidence Susceptibility Studies in the Jxt Field, Niger Delta
+Authors: Chukwudi Idowu, Bosede Taiwo Ojo
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211960-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+This paper investigates the susceptibility of the 'JXT' field, onshore Niger Delta to ground subsidence as an after effect of oil and gas production. Logs from two wells in the field were utilized for this study, Gassmann's model and the modified Hashin-Shtrikman-Walpole model for compressibility analysis were adopted. The results from Gassmann's model range from 0.06GPa-1 to 0.13GPa-1, while results from the modified Hashin-Shtrikman-Walpole bounds for compressibility range from 0.04GPa-1 to 3GPa-1. To further evaluate the susceptibility of the field to ground subsidence, some important elastic parameters were estimated. Results show young modulus (20.5-27.5GPa), bulk modulus (21.3-25.3GPa), shear modulus (8.01-11.2GPa), and Poisson ratio (0.23-0.28). Generally, these results indicate that the study area is less susceptible to ground subsidence and there is little risk of flooding and submergence which can be hazardous to oil and gas production
+
+
+
+
+Keywords:
+upstream oil & gas,
+well logging,
+reservoir geomechanics,
+structural geology,
+compressibility,
+jxt 04,
+thickness,
+equation,
+saturation,
+reservoir characterization
+
+
+Subjects: 
+Reservoir Characterization,
+Reservoir Fluid Dynamics,
+Formation Evaluation & Management,
+Exploration, development, structural geology,
+Seismic processing and interpretation,
+Reservoir geomechanics,
+Integration of geomechanics in models,
+Open hole/cased hole log analysis
+
+
+
+
+Introduction
+
+
+Land subsidence is caused mostly by anthropogenic activities. It is the result of human economic activities such as groundwater exploration and withdrawal from aquifers, hydrocarbon production from reservoir formations, mining activities. It is associated with the caving in of the earth's surface over a wide area due to either geologic or economic activities. The continuous production of oil and gas, or the withdrawal of hydrocarbon from reservoir formations is an economic activity that leads to reduced pore pressure, porosity, and permeability. Pore pressure reduction causes the effective stress on the reservoir formation to increase and therefore increases the overlying formation burden on the matrix of the reservoir formation. While the overburden load remains constrained, and pore pressure is reduced, effective vertical stress is increased, eventually resulting in reduced reservoir thickness and induced changes in in-situ stress (Zoback, 2007). The environmental effects of land subsidence are catastrophic and irreversible. In coastal low altitude areas, loss of ground elevation is particularly dangerous and can lead to severe events such as flooding and submergence. Other environmental and geologic implications include reservoir compaction, reduced groundwater flow, reduced aquifer storage, and aquifer collapse.
+
+
+Throughout the history of oil and gas exploration around the world, there have been several cases of land subsidence in oil fields (Abija and Abam, 2021). Fjaer et al. (2008), and Abija and Abam (2021), noted that in late 1910 and 1920, there were several recorded surface subsidence events across north and south America. The Goose creek field in Texas, and the Bolivar oil field in Venezuela, recorded many of these events. In Europe, Jones et al (1992) and Bertoni et al. (1995) noted such events that occurred in the Ekofisk and Valhall reservoirs in the Norwegian sector of the North Sea, the Groningen gas field in the Netherlands, as well as onshore and offshore of the Ravena area of Italy.
+
+
+Ebiwonjumi (2018), Abam, (2001), and Abija and Abam (2021) highlighted the role of the Niger Delta as one of the top oil-producing regions in the world, with an estimated production of 2 million barrels of oil and 3 million standard cubic feet of gas daily at a depth range of 2.7 – 8km. Studies have shown that the rate of ground subsidence in the Niger delta is increasing with production. According to Fubara (1986), the Niger delta basin sunk at the rate of 2.5cm/yr. Ibe (1988), reports that the rates increased with increased production to 12.5cm/yr. Uko et al. (2018) suggested that the rates ranged from 6.7cm/yr to 20cm/yr. These studies show a reduction in ground elevation around the Niger delta coastline, which is potentially dangerous and could lead to increased flooding and land submergence.
+
+
+Pore volume compressibility is a major component in reservoir compaction studies. To evaluate the deformation and compaction of a reservoir due to a decrease in fluid pressure, the pore volume compressibility is measured (Zhu, 2018). Traditionally, this is done in the laboratory with cores from reservoirs. This is achieved by integrating geomechanical models with the reservoir properties estimated from core analysis. In the absence of cores for static laboratory tests, dynamic Geomechanical models that rely on geophysical data can be used (Khatchikian, 1996; Abija and Abam, 2021). The application of Geomechanical models that rely on geophysical data have proven to be inconsistent over time. These models often predict very low to extremely high compressive strengths when applied to geophysical data and can only be relied on for the formations for which they were derived (Zoback, 2007). The traditional method of carrying out static laboratory analysis on core data are often in conditions that are different from reservoir conditions, and cannot be entirely relied on (Khatchikian, 1996; Zhu, 2018; Zoback, 2007).
+
+
+Rock physics models are unique because over time, they have several applications and have been used to solve problems ranging from lithology prediction, fluid type identification,. The limitations of applying rock physics models are very few. The advantage of rock physics models is that they can be applied to different basins and formation types around the world. The results of rock physics models are accurate and comparable to reservoir conditions.
+
+
+This study aims to investigate the possible occurrence of ground subsidence due to the continuous production of oil and gas. This is achieved by integrating rock physics models with reservoir elastic properties to obtain a suitable Geomechanical model for predicting the susceptibility of the field (JXT) to ground subsidence with increased production. Utilized for compressibility analysis were rock physics cement models, Gassmann's compressibility model, as well as the novel modified Hashin-Shtrikman-Walpole model.
+
+
+Location and Geology of the Study Area
+
+
+According to C. C. Okpoli (2020), the Niger Delta is located between latitudes 3°N and 6°N and longitudes 5°E and 8°E on the Gulf of Guinea's continental margin. It was developed in the late Jurassic and continued throughout the Cretaceous at the sight of the rift triple junction, which was linked to the opening of the south Atlantic. The geology of southern Nigeria and southwestern Cameroon defines the onshore portion of the Niger Delta province. The Benin flank, an east-nmarginortheast trending hinge line south of the West African basement massif, forms the northern limit. The Cretaceous outcrops on the Abakaliki high establish the north-eastern boundary, while the Calabar flank defines the east-southeast limit, forming a hinge line with the surrounding Precambrian. The Cameroon volcanic line to the east, the eastern boundary of the Dahomey basin (the eastern-most West African transform-fault passive margin) to the west, and the two-kilometer sediment thickness contour (or 4000-meter bathymetric contour in areas where sediment thickness is greater than two kilometers) to the south and southwest define the province's offshore boundary. The province covers 300,000 km2 and includes the geologic extent of the Tertiary Niger Delta (Akata-Agbada) Petroleum system.
+
+
+Figure 1View largeDownload slideBase map of the study area.Figure 1View largeDownload slideBase map of the study area. Close modal
+
+
+Methodology
+
+
+The study evaluated digitized wireline logs from two wells in the "JXT field," JXT 03 and JXT 04, with gamma ray logs, resistivity logs, acoustic logs, neutron logs, and bulk density logs. The research approach adopted is divided into two parts. These include:
+
+
+Petrophysical InterpretationRock physics analysisCement modelsGeomechanicsSubsidence studies
+
+
+Petrophysical Interpretation
+
+
+This section not only offers a framework for conducting the reservoir's compressibility analysis and characterization of elastic properties, but it also helps us understand the sensitivity of each elastic parameter used in this study. Additionally, efforts are undertaken to explore and comprehend the lateral fluctuations and vertical thickness of various reservoir units. The correlation of formation tops, lithology units, is carried out for this aim. For the three defined reservoir formations, six petrophysical characteristics were investigated. Reservoir thickness, shale volume, porosity (effective), permeability, water saturation, and hydrocarbon saturation are the variables to consider.
+
+
+Reservoir thickness
+
+
+The JXT field's reservoirs were identified using the gamma ray log, and reservoir thickness (h) was determined using the following relationship.
+
+
+h=Base​ of reservoir-Top of reservoir(1)
+
+
+Shale volume
+
+
+For the shale volume estimation, Larionov (1969) equation for tertiary formations was used.
+
+
+Vsh=0.083(23.7*Igr−1)(2)
+
+
+Igr=GRlog−GRminGRmax−GRmin(3)
+
+
+As defined by T. Gogoi and R. Chatterjee (2019), Igr is the gamma ray log index, GRlog is the gamma ray log reading at the depth of interest, GRmin is the gamma ray log reading in the clean zone, GRmax is the gamma ray log reading in the shale zone.
+
+
+Porosity
+
+
+Porosity is one of the most important characteristics for estimating compaction trends, cementation prediction, and hydrocarbon zone differentiation (Dvorkin and Nur, 2000). (Dvorkin et al., 2002). The porosity estimation in this study is based on two well logs (Neutron, and Density log).
+
+
+Φ=Φdensity 2+Φneutron 22(4)
+
+
+The effective porosity is given by Eq. 5 (Cluff and Cluff, 2004)
+
+
+Φeff=ΦTotal(1−Vsh)(5)
+
+
+Water saturation
+
+
+The Archie (1942) equation is used to calculate water saturation.
+
+
+Sw=α*Rwφm*Rtn(6)
+
+
+Hydrocarbon saturation
+
+
+The following equation, as defined by Shepherd (2009), is used to calculate hydrocarbon saturation (Sh).
+
+
+Sh=(1−Sw)(7)
+
+
+Rock Physics analysis
+
+
+The link between reservoir parameters (porosity, shale volume, and water saturation) and elastic properties is established via rock physics (velocity, impedance, and density). As a result, the primary goal of rock physics investigation is to quantify and improve amplitude interpretation for hydrocarbon detection, reservoir characterization, and reservoir monitoring, especially with recent advances in seismic data acquisition and processing (Avseth et al., 2005). The elastic characteristics of the reservoir rocks identified in this study are estimated using rock physics analysis. The estimation of these elastic parameters aided the characterization of the reservoirs in terms of their lithology, fluid content, and geomechanical properties.
+
+
+Vp estimation
+
+
+Vp, or compressional wave velocity, is a key rock physics quantity. Vp was calculated using the equation below based on the compressional wave sonic transit time log (DTc).
+
+
+Vp=(1000000/DTc)*0.3281(8)
+
+
+Vs estimation
+
+
+Vs, commonly known as shear wave velocity, is a fundamental parameter in rock physics, along with Vp. Castagna et al. (1993) provide the following empirical relationships for estimating Vs from Vp.
+
+
+Vs=0.804Vp−0.856(9)
+
+
+Geomechanics
+
+
+This is concerned with the behavior of rocks under the effect of various forces in terms of their physical qualities, as well as their interaction under various stress regimes. Reservoir geomechanics is a branch of rock mechanics that combines the study of earth stresses with understanding of rock mechanics concepts from many disciplines to solve problems that may occur during a reservoir's life cycle, from exploration to abandonment (Zoback, 2007).
+
+
+Bulk modulus (K)
+
+
+The bulk modulus explains the volumetric changes that occur when a material is subjected to normal stresses (W. Lowry, 2007). The dynamic bulk modulus of a reservoir formation defines how the formation's volume varies in relation to the fluid bulk modulus when normal stresses are applied. In this work, the dynamic bulk modulus (K) was calculated using the equation defined by (W. Lowry, 2007).
+
+
+K=ρ*(Vp2−43Vs2)(9)
+
+
+Shear modulus (µ)
+
+
+The resistance of a formation to shearing stress is described by its shear (Telford et al. 1990).
+
+
+μ=ρ*(Vs)2(10)
+
+
+Lambda (λ)
+
+
+Lambda (λ) is among the class of elastic parameters known as Lame's parameters. In this study, it was used in the characterization of other Geomechanical parameters.
+
+
+λ=ρ*(Vp2−2μρ)(11)
+
+
+Young modulus (E)
+
+
+When a material is subjected to a uniaxial normal stress, the longitudinal stresses are described by the young modulus (W. Lowry 2007). The relationship between Lambda (λ) and Mu (µ) was used to calculate E in this study.
+
+
+E=μ*(3λ+2μλ+μ)(12)
+
+
+Poisson ratio (σ)
+
+
+The Poisson ratio is a measurement of a rock formation's deformability and resistance to compressive forces. It ranges from 0.05 to 0.5, with the former indicating extremely hard materials and the latter indicating extremely soft materials.
+
+
+σ=λ2(λ+μ)(13)
+
+
+Gassmann model
+
+
+The Gassmann's (1951) fluid substitution model applied in this study is given in equation 15.
+
+
+KsatKmineral−Ksat=KdryKdry−Kdry+Kfluidϕ(Kmin−Kfluid)(14)
+
+
+μdry=μsat(15)
+
+
+Ksat is the saturated bulk density, Kdry is the dry bulk density, Kmin is the mineral bulk density, Kfluid is the fluid bulk density, µdry is the shear modulus of dry rock, µsat is the shear modulus of saturated rock, and ϕ is the effective porosity of the reservoir formation.
+
+
+Compressibility analysis
+
+
+The compressibility analysis expresses the physical properties of the reservoir in terms of density. A rock with limited porosity has a high density, which is equal to the rock's bulk strength. The fluid contained within a formation's pores influences its compressibility and is the inverse of its bulk strength (Avseth et al., 2005: Marvko et al., 2009). A weak formation with high porosity is expected to be highly compressible and prone to compaction when subjected to compressive stresses, whereas a stiff formation with low porosity is expected to be less compressible. In this study's compressibility analysis, which was done in two halves, the Gassmann equation, and the Berryman (1991) version of the Hashin-Shtrikman-Walpole (1966) equation were employed. To undertake subsidence susceptibility assessments, the Hashin-Shtrikman-Walpole (1966) equation was modified.
+
+
+Gassmann's equation for compressibility analysis
+
+
+The compressible form of equation 15 was adopted for the compressibility analysis carried out in this study (Avseth et al., 2005: Marvko et al., 2009).
+
+
+(Csat−Cmin)−1=(Cdry−Cmin)−1+[∅(Cfl−Cmin)]−1(16)
+
+
+Where:
+
+
+Csat=1ksat;Cdry=1kdry;Cfl=1kfl;Cmin=1Kmin.(17)
+
+
+Where 1Kdp is the dry pore compressibility, 1Ksp is the saturated pore compressibility, and 1Kϕ is the pore space compressibility.
+
+
+Hashin_Shtrikman-Walpole bounds for elastic moduli
+
+
+The effective elatic moduli of a combination of mineral grains and pores can be anticipated, according to Hashin-Shtrikman (1963) and Avseth et al. (2005). Without describing the geometrical intricacies of the phases' placement in relation to one another, this can be accomplished (Avseth et al., 2005: Marvko et al., 2009).
+
+
+KHS±=K1+f2(K2−K1)−1+f1(K1+43μm)−1(18)
+
+
+μHS±=μ1+f2(μ2−μ1)−1+f1[μ1+μm6(9km+8μmKm+2μm)]−1(19)
+
+
+Figure 2View largeDownload slidePlot of bulk modulus against volume fraction of mineral mixture in the Hashin-shtrikman bounds for elastic moduli (Avseth et al., 2005).Figure 2View largeDownload slidePlot of bulk modulus against volume fraction of mineral mixture in the Hashin-shtrikman bounds for elastic moduli (Avseth et al., 2005). Close modal
+
+
+The Berryman (1991) variation of the Hashin-Shtrikman-Walpole model which can be used to describe a mixture including more than two phases was adopted to predict the effective elasti strength of each reservoir identified in this study. The upper and lower limits of the compressibility of each reservoir formation was estimated using this model.
+
+
+KHS+=Λ(μmax)(20)
+
+
+KHS−=Λ(μmin)(21)
+
+
+μHS+=Γ(ζ(Kmax,μmax))(22)
+
+
+μHS−=Γ(ζ(Kmin,μmin))(23)
+
+
+The subscripts "1″ and "2″ correspond to the component's attributes. The upper bound is given by equations (22) and (23) when Km  and µµm are the individual constituent's maximum bulk and shear moduli, as well as the lower bound when Km and µµm are the minimum bulk and shear moduli of the constituents.
+
+
+Figure 3View largeDownload slideA plot of Bulk modulus against volume fraction of mineral and fluid mixture in the Hashin-shtrikman bounds for elastic moduli (Avseth et al., 2005).Figure 3View largeDownload slideA plot of Bulk modulus against volume fraction of mineral and fluid mixture in the Hashin-shtrikman bounds for elastic moduli (Avseth et al., 2005). Close modal
+
+
+μΛ(z)=〈1K(r)+43z〉−1−43zΓ(z)=〈1μ(r)+z〉−1−zζ(K,μ)=μ6(9K+8μK+2μ)
+
+
+The brackets denote a weighted average over the medium, which is the same as a weighted average over the elements.
+
+
+Modified Hashin-Shtrikman-Walpole bounds for compressibility analysis
+
+
+The Hashin-Shtrikman-Walpole limits model was used and modified in this study to obtain upper and lower bounds of the effective compressibility of a mixture of more than two phases. The upper bound of the effective compressibility is equal to the inverse of the lower bound on the mixture's effective elastic moduli, while the lower bound is equal to the upper bound of the mixture's effective elastic moduli.
+
+
+CHS+=1KHS−(24)
+
+
+CHS−=1KHS+(23)
+
+
+Results
+
+
+This section shows the results from the applied methodology.
+
+
+Reservoir characterization
+
+
+The stratigraphy of the two wells in the JXT field reveals shale and sandstone strata intercalated. Over the two wells, JXT 03 and JXT 04, three reservoirs were identified and correlated. SAND A, SAND B, and SAND C are the three reservoirs. The use of gamma ray logs and SP logs allowed for the definition and correlation of these lithologic units. Tables 1 and 2 present a summary of the results of the reservoir characterization performed.
+
+
+Table 1.0Summary of the petrophysical assessment of the reservoirs in JXT 03   View Large
+
+
+Table 2.0Summary of the petrophysical assessment of the reservoirs in JXT 04   View Large
+
+
+Figure 4View largeDownload slideReservoirs correlated across JXT 03 and 04.Figure 4View largeDownload slideReservoirs correlated across JXT 03 and 04. Close modal
+
+
+SAND A ranges from a depth of 3510m to 3531m in JXT 03. The depth range of this reservoir gives it a thickness of about 20m. In JXT 04, this reservoir has an estimated thickness of about 20m, and a depth range of 3485m to 3506m. The shale volume of SAND A has an estimated value of 15% in JXT 03, and 21% in JXT 04. Across the two wells, the effective porosity was estimated to be 20% and 17% respectively. The permeability of this reservoir ranges from 128mD in JXT 04 and 469.1mD in JXT 03. The reservoir is highly saturated with water in JXT 03 at 90%, while in JXT 04, the water saturation reduces to 30%.
+
+
+SAND B ranges from a depth of 3565m to 3618m in JXT 03. The depth range of this reservoir gives it a thickness of about 53m. In JXT 04, this reservoir has an estimated thickness of about 55m, and a depth range of 3545m to 3600m. The shale volume of SAND A has an estimated value of 14% in JXT 03, and 12% in JXT 04. Across the two wells, the effective porosity was estimated to be 20% respectively. The permeability of this reservoir ranges from 310mD in JXT 04 and 367mD in JXT 03. The reservoir is highly saturated with water in JXT 03 at 80%, while in JXT 04, the water saturation reduces to 30%. In this regard, the hydrocarbon saturation across both wells ranges from 20% in JXT 03 to 70% in JXT 04.
+
+
+SAND C ranges from a depth of 3750m to 3859m in JXT 03. The depth range of this reservoir gives it a thickness of about 108m. In JXT 04, this reservoir has an estimated thickness of about 82m, and a depth range of 3828m to 3910m. The shale volume of SAND A has an estimated value of 8% in JXT 03, and 12% in JXT 04. Across the two wells, the effective porosity was estimated to be 23% and 20%respectively. The permeability of this reservoir ranges from 310mD in JXT 04 and 1251mD in JXT 03. The reservoir is highly saturated with hydrocarbons in JXT 03 at 80%, while in JXT 04, the water saturation increases to 80%. In this regard, the hydrocarbon saturation across both wells ranges from 20% in JXT 04 to 80% in JXT 03.
+
+
+Rock physics
+
+
+The Vp across the SAND A interval in JXT 03 is 3489.1 m/s, while in JXT 04, it is 3402 m/s. In the SAND B interval, Vp increases with values ranging from 3473 m/s to 3686.1 m/s. The lower values for SAND A and B were estimated from JXT 04, while the higher values are from the JXT 03 well. The Vp values in the SAND C reservoir interval was estimated to have higher values in JXT 04, than in JXT 03. The increase and decrease in values of Vp across both wells can be attributed to changes in fluid saturation in the reservoirs. There is an observed increase when water saturation increases and a decrease when gas saturation increases. The Vs across the reservoirs is unaffected by fluid because shear sonic waves do not travel through fluids (Telford 1990; Avseth, 2005). The results from Vs estimation can be used to show the cementation properties in each of the reservoirs. The estimated Vs values from the SAND A interval across both wells range from 1887.5m/s to 1967m/s, with the higher values observed in JXT 03. The estimated values in SAND B show an increase with depth, and ranges from 2019.4m/s to 2170m/s, with the values increasing from JXT 03 to JXT 04. Vs values in SAND C range from 2214.4m/s to 2248m/s. The increase in Vs values across both wells give an indication of the direction of increasing cementation in the JXT field. For SAND A, cementation increase from SE to NW, while SAND B and C show an increase from the NW to SE direction. Cementation is important in understanding pore volume compressibility because it reduces the effect of compressive forces on the formation matrix. Studies have shown that formations with high cementation and low clay volume have higher effective porosities while undergoing diagenetic processes (Han, 1986; Avseth 2005).
+
+
+Estimation of elastic parameters
+
+
+The estimated bulk stregth of the reservoirs in the JXT field range from 21.3GPa to 25.3GPa, while the shear modulus ranges from 8GPa to 11.2GPa. Quartz being the major mineral in a clastic reservoir setting has a bulk modulus of 36.6GPa, there the estimated bulk modulus of the reservoirs in the JXT field give a good indication of its strength. The young modulus estimated also indicate the strength of the reservoirs. The values range from 20GPa to 28GPa, while the poisson ratio ranges from 0.23 to 0.28. A summary of the estimated parameters is shown in table 3 and 4.
+
+
+Table 3A summary of the estimated reservoir elastic parameters in JXT 03   View Large
+
+
+Table 4A summary of the estimated reservoir elastic parameters in JXT 04   View Large
+
+
+Gassmann's model
+
+
+Using Gassmann's model, the saturated bulk modulus, mineral modulus, undrained pore modulus, drained pore modulus, fluid modulus, and dry rock modulus were estimated. This aided the estimation of the corresponding saturated rock compressibility, mineral compressibility, undrained pore compressibility, fluid compressibility, drained pore compressibility C(dp), and dry rock compressibility. From table 6 and 8, the drained pore compressibility C(dp), which is the reservoir formations' pore volume compressibility range from 0.06GPa−1 to 0.13GPa−1. The values of the limits of compressibility derived from the Gassmann model was used to determine the compressibility of the pore volume relative to the mineral compressibility. Based on the obtained results, the least compressible pore volume in the JXT field is 2.2 times more compressible than quartz mineral, while the most compressible pore volume is approximately 5 times more compressible.
+
+
+Table 5showing results for Ksat, Kmin, Ksp, Kdp, Kf, Kdry, from the Gassmann compressibility analysis of JXT 03   View Large
+
+
+Table 6showing results for Csat, Cmin, Csp, Cdp, Cf, Cdry, from the Gassmann compressibility analysis of JXT 03   View Large
+
+
+Table 7showing results for Ksat, Kmin, Ksp, Kdp, Kf, Kdry, from the Gassmann compressibility analysis of JXT 04   View Large
+
+
+Table 8showing results for Csat, Cmin, Csp, Cdp, Cf, Cdry, from the Gassmann compressibility analysis of JXT 04   View Large
+
+
+Modified Hashin-Shtrikman-Walpole bounds for compressibility analysis
+
+
+The modified Hashin-shtrikman-Walpole model for compressibility is used in this research to predict the pore type within the allowable range (Avseth et al., 2005). The pore types fall within the range of soft pore shape and rigid pore shapes. Stiffer pore shapes cause the compressibility values to be lower within the permissible limits, while softer pore shapes cause the values to be higher. The results obtained from the application of this model are in the range of 0.04GPa−1 to 3GPa−1. Where the lower bound of compressibility (CHS−) measured in the JXT field is 0.04GPa−1, while the upper bound of compressibility (CHS+ ) is 3GPa−1. These values imply that the compressibility of a reservoir is highly influenced by lithology and mineral type, as well as fluid type. An overview of each well's findings is displayed in table 9 and 10 below.
+
+
+Table 9shows results obtained from the Hashin-Shtrikman-Walpole compressibility analysis of JXT 03. The upper and lower boundaries are highlighted   View Large
+
+
+Table 10shows results obtained from the Hashin-Shtrikman-Walpole compressibility analysis of JXT 04. The upper and lower boundaries are highlighted.   View Large
+
+
+Conclusions
+
+
+This paper studied the land subsidence susceptibility of the JXT field. This study was carried out by integrating the Gassmann model and the modified Hashin-Shtrikman-Walpole compressibility model. Data from two wells in the JXT field were utilized for the study. Three reservoirs were identified and correlated across the field, and petrophysical properties including thickness, porosity, permeability, volume of shale, and water saturation were measured. From the results, the reservoirs had good thickness, good porosity, and little shale volume. The drained and undrained pore volume compressibility measure using the Gassmann model showed similar values. The modified Hashin-Shtrikman-Walpole model was used to identify the pore shape types in the field. From the results, the dominant pore shape type of the delineated reservoirs in the field is the stiff pore shape. The implication of the results from the pore volume analysis is that the field is less likely to be affected by the continuous production of oil and gas in this field. This means that the risk of drastic seismic events such as ground subsidence is less likely.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Zoback, Mark D.Reservoir Geomechanics. Cambridge University Press, 2007.Google ScholarCrossrefSearch ADS  Abija, F. A., & Abam, Tamunoene K. S.. (2021). Predicting ground subsidence due to long term oil/gas production in a Niger Delta basin, Nigeria: implications for CO2 EOR and geosequestration. Research Square. https://doi.org/10.21203/rs.3.rs-357959/v1Google Scholar ErlingFjaer, et al. Petroleum Related Rock Mechanics. Amsterdam; Boston, Elsevier, 2008.Google Scholar Jones, M. E, Leddra, M. J., Goldsmith, A. S. and Edwards, D. (1992). The geomechanical characteristics of reservoirs and reservoir rocks, HSE - Offshore Technology Report, OTH 90, 333, pp. 1–202Google Scholar BertoniW., Brighenti, G., Gambolati, G., Ricceri, G. and Vuillermin, F. (1995) Land subsidence due to gas production in the on and offshore natural gas fields of the Ravena area, Italy Proceedings of the fifth international symposium on land subsidence. The Hague, IAHS Pub. No. 234. Pp. 13–20Google Scholar Ebiwonjumi, Funmi R.Perspectives about Monetizing the Deepwater Gas Reserves in Niger Delta, Nigeria, Capella University, 2018Google Scholar Zhu, S., Du, Z., Li, C., You, Z., Peng, X., & Deng, P. (2018). An analytical model for pore volume compressibility of reservoir rock. Fuel, 232, 543–549. https://doi.org/10.1016/j.fuel.2018.05.165Google ScholarCrossrefSearch ADS  Khatchikian, A. (1996). Deriving Reservoir Pore-Volume Compressibility from Well Logs. SPE Advanced Technology Series, 4(01), 14–20. https://doi.org/10.2118/26963-paGoogle ScholarCrossrefSearch ADS  Okpoli, C. C., and D.I Arogunyo. "Integration of Well Logs and Seismic Attribute Analysis in Reservoir Identification on PGS Field Onshore Niger Delta, Nigeria." Pakistan Journal of Geology, vol. 4, no. 1, 11May2020, pp. 12–22, 
+              https://doi.org/10.2478/pjg-2020-0002. Accessed 30 Aug. 2021.Google ScholarCrossrefSearch ADS  Gogoi, Triveni, and Rima Chatterjee. "Estimation of Petrophysical Parameters Using Seismic Inversion and Neural Network Modeling in Upper Assam Basin, India." Geoscience Frontiers, vol. 10, no. 3, May2019, pp. 1113–1124, 10.1016/j.gsf.2018.07.002. Accessed 11 Jan. 2022.CrossrefSearch ADS  Dvorkin Jack snd Nur Amos (2000) "Critical Porosity Models." Department of Geophysics, Stanford University, Stanford, CA94305–2215.FubaraD. M. J. (1986) Flood and erosion: Human contributions and remedial environmental policy. Mimeographed paper, Institute of Flood, Erosion, Reclamation and Transportation. Rivers State University of Science and Technology, port Harcourt, Nigeria.Google Scholar Ibe, A.C. (1988). Coastline erosion in Nigeria. The Nigerian Institute for Oceanography and Marine Research and Ibe A.C. Ibadan. University Press, Ibadan, Nigeria (ISBN 978-2345-041).Google Scholar Uko, E. D, Famuyibo, D. A. and Okiongbo, K. (2018). Estimation of Land Surface Subsidence Induced by Hydrocarbon Production in the Niger Delta, Nigeria, using Time-Lapse Orthometric Leveling Data. Mediterranean Journal of Basic and Applied Sciences (MJBAS), Volume 2, Issue 3, pp. 1–18Google Scholar DvorkinJack, Carr MatthewB., and Berge Tim (2002) "Rock Physics Diagnostic in Sand/Shale Sequence." EAGE 64th Conference and ExhibitionFlorence, Italy, 27.Google Scholar Cluff SuzanneG., Cluff RobertM. (2004) "Petrophysics of the Lance Sandstone Reservoirs in Jonah Field Sublette County, Wyoming." AAPG Studies in Geology 52 and Rocky Mountain Association of Geologists 2004 Guidebook.Google Scholar Archie, G.E. "The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics." Transactions of the AIME, vol. 146, no. 01, 1Dec.1942, pp. 54–62, 10.2118/942054-g. Accessed 27 Sept. 2020.Google ScholarCrossrefSearch ADS  ShepherdM. "Rock and Fluid Properties (2009) "Oil field production geology: AAPG Memoir, 91, 65–68.Google Scholar Han, D., 1986, Effects of porosity and clay content on acoustic properties of sandstones and unconsolidated sediments. UnpublishedPh.D. dissertation, Stanford University.Google Scholar PerAvseth, et al. Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge, Uk; New York, Cambridge University Press, 2005.Google ScholarCrossrefSearch ADS  Castagna, J. P., BatzleM. L. and KanT. K. (1993). Rock physics - The link between rock properties and AVO response, in offset-dependent reflectivity - Theory and practice of AVO analysis, ed. CastagnaJ. P. and M.Backus. Investigation in Geophysics, No. 8, SEG, Tulsa, Oklahoma, p. 135–171.Google Scholar Lowrie, William. Fundamentals of Geophysics. Cambridge, Cambridge University Press, Cop, 2007.Google Scholar Telford, W. M., Geldart, L. P., & Sheriff, R. E. (1990). Applied geophysics. Cambridge: Cambridge University Press.Google ScholarCrossrefSearch ADS  Gassmann, F. (1951). Elastic Waves Through A Packing Of Spheres. Geophysics, 16(4), 673–685. https://doi.org/10.1190/1.1437718Google ScholarCrossrefSearch ADS  Avseth, Per, et al.  "Rock-Physics Diagnostics of Depositional Texture, Diagenetic Alterations, and Reservoir Heterogeneity in High-Porosity Siliciclastic Sediments and Rocks — a Review of Selected Models and Suggested Workflows." GEOPHYSICS, vol. 75, no. 5, Sept.2010, pp. 75A31–75A47, 10.1190/1.3483770. Accessed 3 Sept. 2021.Google ScholarCrossrefSearch ADS  Mavko, G., TapanMukerji, & Dvorkin, J. (2009). The Rock Physics Handbook Tools for Seismic Analysis of Porous Media. Cambridge University Press.Google Scholar Berryman, J. G., and Milton, G. W., 1991, Exact results for generalized Gassmann's equation in composite porous media with two constituents. Geophysics, 56, 1950–1960.Google ScholarCrossrefSearch ADS  Hashin, Z., and Shtrikman, S., 1963, A variational approach to the elastic behavior of multiphase materials. Mech J. Phys. Solids, 11, 127–140.Google ScholarCrossrefSearch ADS  
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211960-MS
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+

files/2022/Application of Genetic Algorithm on Data Driven Models for Optimized ROP Prediction.txt

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+----- METADATA START -----
+Title: Application of Genetic Algorithm on Data Driven Models for Optimized ROP Prediction
+Authors: David Duru, Anthony Kerunwa, Jude Odo
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212016-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The demand for cost-effective drilling operations in oil and gas exploration is ever growing. One of the important aspects to tackling the aforementioned difficulty is determining the optimal rate of penetration (ROP) of the drill bit. The most important optimization objective is to achieve a high optimal rate of penetration in safe and stable drilling conditions. Several machine learning models have been developed to predict ROP, however, there have been few studies that consider the different optimization algorithms needed to optimize the conventional developed models other than the conventional grid search and random search techniques. Genetic algorithm (GA) has gained much attention as methods of optimizing the predictions of machine learning algorithms in different fields of study. In this study, GA optimization algorithm was implemented to optimize 5 machine learning algorithms: Linear Regression, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron algorithm while using torque, weight on bit, surface RPM, mud flow, pump pressure, downhole temperature and pressure, etc, as input parameters. Three scenarios were analyzed using a train-test split ratio of 70-30, 80-20 and 85-15 percent on all the developed models. The results from the comparative study of all models developed shows that the implementation of the GA optimization algorithms increased the individual ROP models, with the multilayer perceptron model having the highest coefficient of determination of 0.989% after GA optimization.
+
+
+
+
+Keywords:
+artificial intelligence,
+rop,
+upstream oil & gas,
+evolutionary algorithm,
+dataset,
+artificial neural network,
+genetic algorithm,
+neural network,
+machine learning,
+prediction
+
+
+Subjects: 
+Information Management and Systems,
+Artificial intelligence
+
+
+
+
+Introduction
+
+
+For decades, drilling optimization has focused on rate of penetration (ROP): the rate at which a well is drilled is a major measure of drilling efficiency. Higher ROP indicates faster drilling, which translates to improved rig performance and productivity. The instantaneous ROP is influenced by a number of factors. They include: formation properties, mud rheology, drill bits, and bit/rock interactions. String Vibrations, deformations, and bit fatigue can also affect the rate of bit penetration. Drilling well cost reduction has long been a goal of ROP optimization. Improving the ROP can result in substantial time savings in drilling operations (Kaiser, 2009). ROP can be improved by solving an optimization problem to maximize ROP (Lummus, 1970). The objective function (namely the ROP) should be maximized for economic gain in drilling because ROP is inversely proportional to the cost of the well (Hedge et al, 2018). Given the intrinsic interest in ROP modeling, deterministic models for ROP prediction have been created in recent years, however their performance on new datasets is not guaranteed. Over the last few years, advances in processing power and machine learning have resulted in the emergence of a slew of new data-driven ROP prediction models – models that are only based on data statistics (Hegde et al, 2018; Hegde et al, 2017; Hegde & Gray, 2017). These models predict ROP using machine learning algorithms and have been demonstrated to generalize well for different forms (Aljubram et al, 2022). Data-driven modeling and its applications in forecasting and optimizing extremely uncertain downhole conditions are expected to become more prevalent in the future of drilling operations (Noshi & Schubert, 2019). High-performance, repeatable, and scalable solutions are provided by data-driven modeling methodologies. They do, however, have an unknown functional form, making interpretation problematic. Drilling parameters are used as inputs; however functional form is not constrained. This helps them to more closely model the data, resulting in greater accuracy. The decrease in model inference goes hand in hand with the rise in accuracy. The functional form of a deterministic model, on the other hand, can be used to gain understanding (such as the most influential input drilling parameter) (Hegde et al, 2018).
+
+
+Data-driven models have long been thought to be inefficient for real-time applications because they can be nonlinear functions with uncertain functional forms, making real-time optimization problematic. Because of their inherent benefits over traditional algorithms, metaheuristic optimization algorithms have become increasingly appealing in recent years. Metaheuristics are used to identify high-quality solutions to an ever-growing variety of complicated real-world issues, such as combinatorial problems, because they can address multiple-objective, multiple-solution and nonlinear formulations. Because the equations (objective functions) are often convex or smooth and can be solved for optimal parameters, optimization of analytical or deterministic models in a real-time setting is not hampered by computational run-time restrictions (Cui et al. 2015; Hegde et al, 2018). Metaheuristic algorithms, such as particle swarm algorithms or genetic algorithms, can solve a more complex response equation.
+
+
+In this study, we developed a metaheuristic model (Genetic algorithm) and applied it on data driven models for the optimization of ROP. Real field drilling datasets, such as those regarding the bit type, bit drilling time, rotations per minute, weight on bit, torque, formation type, rock properties, hydraulics, and mud drilling properties, were collected from several wells. The raw data were first preprocessed and rows with missing values and outliers were removed to improve the performance of the model. Exploratory data analysis was performed on the data to discover how each feature contributes to the target. Some features which did not contribute much to the model's performance were dropped. The clean dataset was then fed to the metaheuristic model to find the optimal parameters of the model. After the optimal parameters of the model were obtained, we trained the model using machine learning algorithms (linear regression, decision tree, random forest and support vector machine) and multi layer perceptron neural network to achieve the highest accuracy. We demonstrated the accuracy of the model by comparing the prediction results to other data driven methods. The proposed model had a better performance. Also the model has a good computational efficiency (algorithm runtime on drilling data).
+
+
+Literature Review
+
+
+Hedge et al, (2018) conducted a study on Performance comparison of algorithms for real time rate of penetration optimization in drilling using data-driven models. In the study, they implemented two simple algorithms, the eyeball method and the random search method, to find the best drilling parameters in real time for the objective. Metaheuristic optimization algorithms (simplex, swarm methods, and differential evolution algorithms) were used to compare the result of the machine learning algorithms used in the study. The data-driven ROP model was built using four drilling parameters as inputs. ROP was modeled as a function of WOB, rev/min, flow rate and unconfined compressive strength (UCS) of rock. The analysis conducted showed that data-driven models can be used for real-time drilling despite the unknown functional form. All algorithms evaluated in the paper worked well, but the simplex algorithm performed with the best tradeoff. The performance metric utilized was the accuracy.
+
+
+Han et al, (2019) proposed a data driven approach of ROP prediction and drilling performance estimation. The data was obtained from wells from southern China. The drilling section covers 900 meters without changing the drill bit. The first 500 meters were utilized for training and validation, while the rest part was for prediction. The parameters used included depth, bit status, rotation speed, hook load, stand-pip pressure, and so on. The lithology data represented by gamma, conductivity, and resistivity were also included. The BP neural network model and the LSTM deep learning model were developed and used to predict the ROP. Both models were applied on the same well. The LSTM model showed a better estimation than the BP neural network model. Especially, for the drilling section around depth 2520 meters, the BP neural network model predicted half of the real field ROP value, while the LSTM had a good ROP estimation. From their research, they discovered that the main improvement from BP neural network to LSTM model was that LSTM deep learning model considers the time sequence effects. During drilling process, rock cutting is a continuous procession; the previous drill bit/rock interaction may have strong effects to the flowing drilling performance. So, when the time effects were taken into consideration, the model prediction accuracy was improved. The error metric used was the percentage error.
+
+
+Li and Cheng, (2020) carried out a research on Prediction and Optimization of Rate of Penetration using a Hybrid Artificial Intelligence Method based on an Improved Genetic Algorithm and Artificial Neural Network. The data collected included depth, weight on bit (WOB), pipe rotations per minute (RPM), torque, drilling fluid flow rate (inlet and outlet), pump speed, mud weight, mud viscosity, mud temperature (in and out), and standpipe pressure. Additionally, the petrophysical information collected by the LWD system includes the formation type, porosity, and formation fracture pressure. Firstly, they demonstrated the SG smooth filter's efficiency by comparing the accuracy of the developed model trained with raw data and SG filter smoothed data. Secondly, they applied the proposed IGA-ANN model with all the smoothed inputs to obtain the optimal input types and ANN structure and compared the variable selection results using the proposed IGA-ANN algorithm and those using the classic wrapper algorithm.Through these comparisons, they validated the efficiency of the proposed model for variable selection regarding the improvement in the accuracy of ROP prediction. Fourthly, they applied the optimized ANN trained by optimal variables to predict the ROP for offset wells to demonstrate the accuracy and usefulness of the present method. Various statistical indices were used to show the performance of the developed model. They used indices such as the mean square error (MSE), mean absolute error (MAE), and regression coefficient (R2).
+
+
+Singh et al, (2019) carried out a research on Cloud Based ROP Prediction and Optimization in Real-Time Using Supervised Machine Learning. Eight different types of machine learning models were trained using a population of 50 horizontal wells in the Permian Basin. Fifteen drilling parameters, or input features in terms of machine learning were proposed for the model development. They included surface torque, flow rate, hydraulic parameters, differential pressure, flow-rate, and rotary speed. The various machine learning models were compared using leave-one-out-cross-validation by training on 45 of the wells and blind testing on the remaining five. Mean Absolute Percentage Error (MAPE) was used to compare the models because it allows a percentage estimate of ROP prediction accuracy. Decision Trees and Random Forest models performed well on training data but poorly on blind data due to overfitting. Deep learning and artificial neural networks models gave acceptable accuracy but were rejected because their black box nature resulted in poor interpretability. Shrinkage methods such as LASSO and Ridge Regression gave clear interpretability but reduced accuracy because of the assumed linear relationship. The winning model was multivariate adaptive spline regression because it is computationally inexpensive and fast to keep up with real time drilling, provides the best accuracy, and offers clear interpretability.
+
+
+Noshi and Schubert, (2019) conducted a study on the application of Data Science and Machine Learning Algorithms for ROP Prediction. The study involved forty different wells. Training was done on 20% of the dataset while the remaining 80% was used for testing for all the different algorithms on the lateral section of the horizontal wells. The study was conducted to find an alternate solution to traditional models regarding ROP optimization by leveraging the power of Machine Learning and predictive analytics. Random Forest, Artificial Neural Networks, Support Vector Regression, K-Nearest Neighbor, and Gradient Boosting Machine were the machine learning models implemented in the study. Considerable modeling efforts were implemented to test the robustness of the best models developed. Several algorithms were developed and implemented on a single well then tested on the rest of the data set. An ensemble of methods: GBM and Random Forest helped achieve the best prediction with the least error metric for the dataset. Algorithms such as KNN and SVR also performed well and can be used if there is a constraint on computing capabilities. Regression metrics such as Mean absolute error (MAE), Root mean squared error (RMSE), Coefficient of Determination (R2) and Accuracy score were utilized in the study.
+
+
+METHODOLOGY
+
+
+Data collection
+
+
+Several log and geological parameters were acquired during drilling, and the data is available at the geothermal data repository. The data used to train the model was conducted on a depth-based-drilling-dataset and it included 3000 real-time drilling data points recorded from 2550ft to 5300ft. The data collected included depth, rate of penetration, weight on bit (WOB), pump pressure, torque, etc. The initial data of less than 2550ft was discarded in this study as the logging data were not recorded up to this height and the default values were reported for several of these parameters. The basic descriptive statistics of the data is shown in the table 1 and 2 below.
+
+
+Table 1Well log parameters 
+            . Mean
+            . Std
+            . Min
+            . Max
+            . Depth 3380.620549 516.053893 2552.000000 5301.500000 GR 39.671575 49.296097 0.837900 1567.590000 BS 8.5 0 8.5 8.5 DT 77.363302 14.635125 48.937100 136.253000 RT 44.846092 1299.030972 0.094000 62290.769500 RHOB 2.476438 0.141847 1.805100 3.149300 NPHI 0.165747 0.095474 0.002000 0.862600 PEF1 7.254799 1.357852 3.168200 32.537500 PEF2 6.967085 1.515469 2.033600 14.320300 
+            . Mean
+            . Std
+            . Min
+            . Max
+            . Depth 3380.620549 516.053893 2552.000000 5301.500000 GR 39.671575 49.296097 0.837900 1567.590000 BS 8.5 0 8.5 8.5 DT 77.363302 14.635125 48.937100 136.253000 RT 44.846092 1299.030972 0.094000 62290.769500 RHOB 2.476438 0.141847 1.805100 3.149300 NPHI 0.165747 0.095474 0.002000 0.862600 PEF1 7.254799 1.357852 3.168200 32.537500 PEF2 6.967085 1.515469 2.033600 14.320300 View Large
+
+
+Table 2Drilling parameters 
+            . Mean
+            . Std
+            . Min
+            . Max
+            . Depth 3380.620549 516.053893 2552.000000 5301.500000 DT 77.363302 14.635125 48.937100 136.253000 WOB 6.717661 2.953576 0.018500 29.631300 Torque 18.737392 4.013863 0.011000 40.169000 Surface RPM 164.532397 33.755331 0.000000 290.560000 Pump Pressure 189.038748 30.808893 1.645000 278.681000 Mud flow 2236.186669 162.400410 443.095200 2791.500000 ECD 1.409415 0.060620 1.275600 1.793500 BS 8.5 0.0 8.5 8.5 DownT 85.764245 11.508402 51.620500 113.000000 DownP 391.356865 40.998051 311.832300 528.775000 ROP 22.993391 8.463675 0.337400 65.861000 
+            . Mean
+            . Std
+            . Min
+            . Max
+            . Depth 3380.620549 516.053893 2552.000000 5301.500000 DT 77.363302 14.635125 48.937100 136.253000 WOB 6.717661 2.953576 0.018500 29.631300 Torque 18.737392 4.013863 0.011000 40.169000 Surface RPM 164.532397 33.755331 0.000000 290.560000 Pump Pressure 189.038748 30.808893 1.645000 278.681000 Mud flow 2236.186669 162.400410 443.095200 2791.500000 ECD 1.409415 0.060620 1.275600 1.793500 BS 8.5 0.0 8.5 8.5 DownT 85.764245 11.508402 51.620500 113.000000 DownP 391.356865 40.998051 311.832300 528.775000 ROP 22.993391 8.463675 0.337400 65.861000 View Large
+
+
+Data Exploration
+
+
+Missing values, unrealistic values and outliers impact the accuracy of the machine learning model. Data preprocessing was performed to handle the missing and unrealistic values, and outliers in the dataset. This is because; the outliers and unrealistic could be taking place as a result of measurement errors. Exploratory data analysis was then implemented on the data to discover relationships/correlations that exists between the different input features and the target feature.
+
+
+As stated earlier, missing values, unrealistic values and outliers affects the accuracy of the machine learning model. We handled the missing values by simply dropping them. We identified the outliers and unrealistic values by making a boxplots of all the parameters in the dataset and then removed them from the data so that it will not affect the model's decision.
+
+
+Similarly, the same procedure implemented to remove the outliers present in other features in the dataset. Detailed exploration of the data, revealed some unrealistic values. It was discovered that there were some negative values of weight on bit present, which is unrealistic, so they were dropped them.
+
+
+The accuracy of the machine learning model depends on the correlations between the various features used in training the model with the target feature. So there is need to determine the relative important predictive features for ROP from the other different input features in the data. Scatter plots of all the input features against the target feature (ROP) were plotted respectively to visualize the correlation these features have with ROP.
+
+
+The scatter plot of WOB vs ROP is shown in Figure 2. From Figure 2, positive correlation was noticed although the correlation is not linear. It observed that most values for ROP were scattered between WOB values of 0 and 20. Usually the goal in machine learning and artificial intelligence work is to strive to organize data in terms of linearity. Processing data to obtain a linear relationship between different input features and the target feature helps to reduce variance and computational complexities that may arise as a result of non-linearity. Similarly for WOB, the scatter diagram of other input features and ROP are depicted in Figure in Figures 3.
+
+
+Figure 1View largeDownload slideBox plot showing the outliers of Gamma ray present in the dataFigure 1View largeDownload slideBox plot showing the outliers of Gamma ray present in the data Close modal
+
+
+Figure 2View largeDownload slideScatter plot of weight on bit and ROPFigure 2View largeDownload slideScatter plot of weight on bit and ROP Close modal
+
+
+Figure 3View largeDownload slideScatter plot of other features and ROPFigure 3View largeDownload slideScatter plot of other features and ROP Close modal
+
+
+For a better representation of the correlation that exists in the data, heatmap plot showing numerical correlations that exists between the features was made.
+
+
+The feature extraction and dimensionality reduction procedure can reduce the time it takes to train a machine learning model. This procedure has the ability to increase the accuracy between the predicted and measured ROP (Okoroafor et al. 2022). Feature extraction was implemented to select the most important features which have greater influence on the target feature from the entire dataset. The feature extraction, coupled with the understanding of the physical behavior of the features contributed to the selection of some features from the dataset. In this study, feature extraction was performed using the PCA for the variable selection.
+
+
+Model Development
+
+
+After performing preprocessing and cleaning techniques on the dataset, the dataset was prepared for the modeling using different machine learning models. Various supervised learning and regression algorithms were utilized to predict ROP. It was discovered from the data exploration conducted using various plots that utilization of linear modeling approaches will not give very good result with the dataset because the plots showed that the input features were not linearly correlated with the target feature (ROP). Therefore, some non-linear approaches and ensemble modeling techniques were deployed. Most of the machine learning models implemented on the dataset were non-linear algorithms.
+
+
+Machine learning models were applied for the development of ROP prediction model. This was done after feature selection and dimensionality reduction were applied to select the relevant input features from the dataset. The dataset was divided into train and test data.Training was done on 80% of the data set while the remaining 20% was used for testing all the different algorithms.
+
+
+Two other scenarios were also analyzed using a train-test split ratio of 70%-30% and 85%-15% on all the developed models. Five different regression models were developed. The models are namely:
+
+
+Linear RegressionSupport Vector MachineDecision TreeRandom ForestMultilayer Perceptron Algorithm
+
+
+After developing these models, the Genetic Algorithm (GA) was implemented to update the input features of the models. The GA performed a heuristic search on the input parameters of the models to obtain the optimal input parameters for all the models. The optimized input parameters of the different models were then applied to train the different model using the same dataset. Various metrics were used for regression tasks to show the performance of the developed model. Thereafter regression coefficient (R2), mean absolute error, root mean square error, and accuracy score were used to evaluate the performance of the different models developed. Brief descriptions of some of the models are given below:
+
+
+Genetic Algorithm
+
+
+In this study, hybrid scheme was proposed to obtain the most accurate and efficient machine learning model for ROP prediction by applying genetic algorithm to obtain the optimal input variables and the structures of linear regession, decision tree, random forest, support vector machine and multilayer perceptron in other to achieve maximum ROP. The first step of this method is to generate an initial population of possible solutions (chromosomes) that is composed of these machine learning models with different parameters and types of inputs. These chromosomes were encoded into zeros and ones. Assessment of the accuracy of the possible solutions was carried out using an objective function known as cost function which predicts the accuracy of the machine learning model. The best performing chromosomes were selected by the GA which forms the parents. The parents were crossed to generate a new population of offspring whose accuracy is better than that of the parents. These offspring were further mutated by altering their genes (encoded zeros and ones) in order to induce diversity within the offspring to further improve the solution. These mutated offspring were used as parents for the next generation. The process was repeated for several generations until an optimal solution was achieved. After optimization, the input combination and model structure were obtained with the highest ROP prediction accuracy. Finally, the developed prediction model was then utilized to optimize the ROP.
+
+
+Artificial Neural Network
+
+
+Artificial neural networks (ANNs) are computer systems based on biological neural networks seen in animal brains. An artificial neural network is a network of interconnected nodes called artificial neurons that was inspired by a brain's simplicity of neurons (Nwosu et al. 2018). The nonlinear relationship between input and output values could be modeled using ANN. As a result, it may be used to swiftly mimic the output pattern of physical systems and solve difficult mathematical issues linked with them (Mohaghegh 2000; Thanh et al. 2019). ANN has been used to tackle wide range of petroleum and geoscience problems due to its capacity to handle nonlinear relationships between inputs and outputs (Adibifard et al. 2014 and Thanh et al. 2019). ANN has three layers namely; the input layer, hidden layer and output layer. The goal of ANN is to iteratively converge computed output to desired output (Kaymak et al. 2019). Based on experimental data, ANN translates complex and non-linear interactions into series of input-output training patterns. ANN uses its inherent capability to create non-linear mapping between inputs and outputs (Hornick et al. 1989; Bose and Liang 1996; de Souto et al. 2002; Garcia-Pedrajas et al. 2003; Ahmadi, 2012). This non-linear mapping between inputs and outputs is called feed forward network.
+
+
+Random Forest
+
+
+Random forest algorithm belongs to the ensemble methods. A random forest classifier is a meta estimator that combines multiple subpar base estimators to provide a superior overall result. The base estimator in random forest is a simple decision tree estimator (Onwuchekwa, 2018). Because random subsets of the training data (with resampling) are trained on random subsets of the features, the method is termed random forest. The result is a collection of decision trees based on these subsets of data. The average of the predictions of the collection of trees for the regression case is used to obtain a prediction from the model (Onwuchekwa, 2018). The For a classification problem, the random forest model aggregates numerous decision trees, just like in regression. To develop the random forest model, each decision tree is trained on a random subset of the data. The random forest aggregates the predictions from each tree to apply the model. The most prevalent class is chosen by the model (Mohamed et al. 2019). Random forests have been shown in a number of theoretical and empirical investigations to exhibit excellent prediction accuracy, good tolerance for outliers and noise, and are not prone to overfitting (Shi et al. 2019). Random forest classifiers can handle non-linear and high-dimensional samples. In the random forest, each tree is a binary tree. The top-down recursive splitting principle is used to create it. That is, the training set is divided from the root node in turn. The root node of the binary tree holds all of the training data. It separates into the left and right nodes according to the concept of minimum node purity. A portion of the training data is stored in each node. The node continues to split according to the same criteria until it approaches the branch rule and stops growing. Each decision tree learns how to classify specific data, while random sampling ensures that the same samples are classified by multiple decision trees, allowing the classification abilities of different decision trees to be assessed. A single decision tree's performance will be limited. Rather than depending on a single tree, it is preferable to combine the predictions of several trees. Aggregation will, on average, outperform a single predictor. The aggregation might be seen as emulating the concept of "wisdom of the crowd." (Sun and Li, 2018).
+
+
+Support Vector Machine
+
+
+Support vector machines (SVMs) were created to find the best hyperplane for maximizing the lowest distance between data points. The bias value on the hyperplane defines its departure from the origin point, whereas the weight value defines its orientation. The data points with the greatest effect on the separating hyperplane are called support vectors (Noshi and Schubert, 2018). The maximum spacing hyper plane, which separates the samples into two categories and meets the maximum spacing between the two types of data, is achieved in the situation of linear separability (Shi et al., 2019). SVMs locate the line that optimizes the separation between the points of each class in a two-dimensional, two-class classification issue. The margin is the distance between a line and the nearest point labeled one way or the other. There are many lines that can be used to divide the points, but the purpose is to select the line with the largest margin. Support vectors are the points nearest to the dividing line (Mohamed et al., 2019). The majority of problems encountered in practice are linearly inseparable or simply non-linear. The non-linear mapping algorithm is used in this case of linear inseparability to transform all of the original samples into a high-dimensional feature space, in which the samples become linearly separable, allowing the non-linear characteristics of the samples to be linearly analyzed using the linear algorithm. The computational complexity of mapping data from low-dimensional space to high-dimensional space increases, yet SVM solves this problem by employing the kernel function approach. In implicit mapping space, the kernel function is the inner product function of two vectors. It is no longer essential to calculate the inner product of a high-dimensional or even infinite-dimensional feature space using such a function. Support vector machines can be generated by a variety of kernel functions (Shi et al., 2019). SVM could be used to solve a regression problem in addition to classification. Support Vector Regression is the name of this method. To classify nonlinear boundaries, a nonlinear kernel function that transforms nonlinear hyperplane to linear hyperplane in higher dimensional space can be utilized (Noshi and Schubert, 2018). SVR learns how input data are mapped to output data using some training data and generates a function that offers a relatively excellent prediction of the output for any given input data (Guo et al., 2018). SVR employs the well-known kernel trick, which allows the original data to be mapped to a higher-dimensional space without having to explicitly define the higher dimensions. As a result, it can make accurate predictions for nonlinear regression problems (Onwuchekwa, 2018).
+
+
+Results
+
+
+The test data was used to test the performance of the different models. The individual models without the optimal input structures were first tested. Cross-validation was applied to avoid overfitting the model. The performance of these models on test data is shown in the Figure 5 below.
+
+
+Figure 4View largeDownload slideHeatmap showing the numerical relationships between the featuresFigure 4View largeDownload slideHeatmap showing the numerical relationships between the features Close modal
+
+
+Figure 5View largeDownload slideResults of test data before applying GAFigure 5View largeDownload slideResults of test data before applying GA Close modal
+
+
+The support vector machine, multilayer perceptron algorithm and the random forest, all had a good performance on the training data. Their performance on the test data, were not so good. This is attributed to poor correlation that exists between the input features and the target feature (ROP). Secondly, we tested the performance of these models with optimal input parameters determined by the Genetic algorithm. The results from the comparative study of all models developed showed that the implementation of the Genetic optimization algorithm increased the individual ROP models with the multilayer perceptron model having the highest coefficient of determination after GA optimization. The optimized multilayer perceptron model is computationally inexpensive and can be effectively utilized in real time drilling. The performance of these optimized models on the test data is shown in Figure 6 below.
+
+
+Figure 6View largeDownload slideResults of test data after applying GAFigure 6View largeDownload slideResults of test data after applying GA Close modal
+
+
+More regression metrics were also used to evaluate the performance of the model after applying GA. The result is as shown in Table 3 below;
+
+
+Table 3Different performance metrics used to evaluate model performance 
+            . MLP
+            . SVM
+            . LR
+            . RF
+            . DT
+            . MAE 0.357 1.001 2.665 0.697 0.905 RMSE 0.6044 1.915 5.301 1.328 1.738 Accuracy (%) 98.9 92.8 74.6 95.5 93.7 
+            . MLP
+            . SVM
+            . LR
+            . RF
+            . DT
+            . MAE 0.357 1.001 2.665 0.697 0.905 RMSE 0.6044 1.915 5.301 1.328 1.738 Accuracy (%) 98.9 92.8 74.6 95.5 93.7 View Large
+
+
+Conclusion
+
+
+Metaheuristic optimization was performed for just few input features of the models using just a single optimization algorithm which is Genetic Algorithm. As stated in the results, optimization of just few input parameters of the model, improved the accuracy of all the models. In future works, more exploration of the optimization algorithms will be done and all the input parameters of the models will be taken into consideration. Also more metaheuristic optimization algorithms will also be applied on machine learning models, and their performance will be evaluated.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Adibifard, M., Tabatabaei-NejadS. A. R., and KhodapanahE. (2014). Artificial Neural Network (ANN) to Estimate Reservoir Parameters in Naturally Fractured Reservoirs Using Well Test Data. Journal of Petroleum Science and Engineering122. Elsevier: 585–94. https://doi.org/10.1016/j.petrol.2014.08.007.Google ScholarCrossrefSearch ADS  Ahmadi, M. A., Zendehboudi, S., Lohi, A., Elkamel, A., and ChatzisL. (2012). Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization. https://doi.org/10.1111/j.1365-2478.2012.01080.Google Scholar Aljubran, J., Nwosu, C., Okoroafor, E., Smith, C., Ochie, K., and Gudmundsdottir, H. (2022). Recent Trends in Artificial Intelligence for Subsurface Geothermal Applications. 47th Workshop on Geothermal Reservoir Engineering Stanford University, CaliforniaGoogle Scholar Bose, N.K., and Liang, P. (1996). 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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212016-MS
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+

files/2022/Application of Hydrothermal Liquefaction Procedure for Microalgae-To-Biofuel Conversion.txt

@@ -0,0 +1,202 @@
+----- METADATA START -----
+Title: Application of Hydrothermal Liquefaction Procedure for Microalgae-To-Biofuel Conversion
+Authors: Faith Mmesomachukwu Kelechi, Chukwuebuka Samuel Nwafor
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212014-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The thermal depolymerization process is also known as Hydrothermal liquefaction(HTL) Is used in converting macro/micro molecules, under temperatures of about 280°C and 370°C and pressures that are in the range from 10 to 25 MPa and into crude such as oil. The oil is composed of high energy density and low heating values of 33.8-36.9 MJ/Kg and 5-20 wt% renewables and oxygen. Presently microalgae are used industrially in producing high-quality products for food additives. Also, the microalgae are environmentally friendly, as it is used in the treatment of wastewater, control in the mitigation of industrial CO2 emission and atmospheric CO2 capturing. Due to environmental issues, microalgal are converted from biomass to biofuel. Recently HTL has drawn more attention, as it can be used in the refinery industry. This paper is also concerned with solving environmental issues using microalgae as an effective method for biomass to biofuel conversion. One significant advantage of HTL is the possibility of using fresh microalgae after harvesting, the processing of biomass and increased thermodynamic efficiency. The latter is achieved due to high HTL temperature and pressure which creates an avenue for more heat recovery.
+
+
+
+
+Keywords:
+sustainability,
+biofuel,
+health & medicine,
+sustainable development,
+air emission,
+microalgae,
+consumer health,
+biomass,
+bioresource technol,
+conversion
+
+
+Subjects: 
+Environment,
+Sustainability/Social Responsibility,
+Air emissions,
+Climate change,
+Sustainable development
+
+
+
+
+Introduction
+
+
+Presently, the world has been so dependent on fossil fuels, Carbon dioxide (CO2) emissions are the leading causes of global warming of the potential (GWP) of greenhouse gases (GHG) (Lenton 2014). The rate of concentration of atmospheric CO2 has stroked 400ppm observed at Mauna Loa Observatory, Hawaii in May 2013 (www. CO2.earth), a height never attained before, in the history of human existence. Currently, the mean annual rate of increase has increased more than twice in the 1960s (0.9), and it is 2.29 ppm in the last decade (2007-2016), and it is still increasing. Efficient and Effective preventive strategies are needed to help in the reduction of excess concentration of CO2 (Bilanovic et al. 2009).
+
+
+Technologies specifically designed, for the removal of atmospheric CO2 and other particle emissions could reduce the concentration of CO2 faster than natural processes such as photosynthesis (Lemoine and Traeger 2012). For example, technologies such as bioenergy with carbon capture and sequestration (BECCS) employ photosynthetic capturing of CO2, storing the generated energy in the form of renewables such as biomass. Major terrible emissions are caused by CO2 capturing after the burning of biomass (Greene et al. 2017, Mathews 2008). According to research, it has been stated that, at a large scale, BECCS carried out on terrestrial biomass has an advanced impact on nutrients, food production, water use, land and biodiversity (Searchinger et al. 2015, Smith 2016). Controversially, according to conventional terrestrial biomass sequestration, scholars have noted the urgency of determining the potential for reduction of microalgae cultivation systems' CO2 emissions (Herzog and Drake 1996, Stewart and Hessami 2005, Wang et al. 2008).
+
+
+Furthermore, microalgae have proven to have an increased growth rate with increased photosynthetic efficiency (10-20%) than most land plants (1-2%). They are much suitable for atmospheric CO2 removal, also as a source of bioenergy (Greene et al. 2016). Food security and climate can be beneficial for bioenergy production from marine microalgae (Lenton 2014, Walsh et al. 2015, Walsh et al. 2016, Greene et al. 2016). Moreover, for increased carbon fixation photosynthesis by algae in seawater, CO2 must be steadily supplied, most especially in closed cultural systems (Lenton 2014). Higher demands of CO2 for the cultivation of algae can be reached by emissions of CO2 that are typically found in typical power plants' flue gas (6-12%) (Ho et al.2011). The relevance is GHG emissions reduction from power plant stack and an effective CO2 bio-fixation route. Mediated-microalgae CO2 fixation would be highly sustained by microalgal biomass cultivation coupling with an existing power infrastructure (Kumar et al. 2010).
+
+
+In our modern world today, the transportation sector accounts for 20% of global CO2 emissions (Rawat et al. 2013). Our societies have been sourcing their' energy from fossil fuels such as natural gas, petroleum crude oil and coal for both, domestic uses, transportation needs and heating duties.
+
+
+More sustainable renewable fuels are needed due to their' decreased environmental concerns, reduced availability and socioeconomic implications of fossil sources. Biofuels can be found in a state of matter either, liquid, solid or gaseous state. The production of biofuels driven by biological carbon sequestration can be the best effective alternative to supplement fossil fuels (Singh, Nigam and Murphy 2011). Biofuels can be used for numerous purposes such as transportation, heat and base fuel for electricity production.
+
+
+The production and the current use of biofuels as an alternative to replacing fossil fuels, signify the effort for GHG reductions. Biofuels are crucial topics in our world today and have been researched about for many decades (Hoekman 2009). Terminologies such as the "first-, second-, and third-generation" biofuels are distinguished and used to distinguish between simple, traditional biofuels and more complex, advanced ones. The "first-generation" term is used to refer, to biofuels produced from easily available, commonly edible feedstock using already established conversion technologies. Examples of first-generation biofuels include ethanol produced through the fermentation of sugars and biodiesel manufactured through the transesterification of triglycerides. The "second generation" term is used in referring to biofuels that are either produced from non-food feedstock, advanced or produced through advanced processing technology. Also, examples of advanced processing technology are catalytic hydroprocessing of triglycerides used in the production of renewable diesel and the conversion of lignocellulose (Hoekmann et al. 2012).
+
+
+Algal biofuels are considered and are also known as "third-generation" biofuels. The energy gotten from the sun, which is solar energy is converted into bioenergy through the green growth stimulated by the photosynthetic production process. Algal biomass can be used or can serve as a raw material for advanced biological or thermochemical transformation processes (Tian et al. 2014b). Records have it that biofuels produced using marine microalgae can help eliminate our society's overreliance on fossil fuels (Pacala and Socolow 2004). Many conversion technologies not limited to hydrothermal liquefaction (HTL), have shown significant feasibility in processing microalgae. As a wet method, HTL has the plus advantage of not demanding to dry feedstock.
+
+
+MICROALGAE FOR SUSTAINABLE ENVIRONMENT
+
+
+Microalgae are considered to be among the first evolving organisms that surfaced more than 3 billion years ago in the earth's ocean, during the formation of the earth's environment. Presently, there are more than thousands of organisms existing not only in the ocean but in freshwater also such as ponds, lakes and rivers. However, there are hundreds of thousands of algae species in human existence that ranges from microscopic organisms to 60m in length. Algae are evenly and widespread in the world, there are tendencies of seeing microalgae even in the arctic snow.
+
+
+MICROALGAE GROWTH ASPECTS
+
+
+The growth of microalgae consists of chlorophyll which produces oxygen through carbon dioxide atmospheric capturing during photosynthesis. However, microalgae continue to contribute to the atmospheric formation of the earth by continuously absorbing the emitted carbon dioxide in the earth's atmosphere through industrial activities carried out by humans and the respiratory activity of animals. microalgae also serve as an oxygen producers and zooplankton and fish feed. One of the significances of the cultivation of microalgae industrially is the positive encouraging environmental advance effect with carbon capture and oxygen production. As a result of the continuously expanding of fossil fuels, the cultivation of microalgae industrially becomes more significant. Microalgae grow by a process known as photosynthesis. Carbon dioxide and Solar light are absorbed by chlorophyll and transformed into adenosine triphosphate (ATP) and oxygen. The energy produced is used in the respiration, which helps in the development of algae cells (Brennan et al 2010) Photosynthetic reaction can be explained in the equation below:
+
+
+6CO2+6H2O+nhv→C6H12O6+6O2(1)
+
+
+where
+
+
+hν is the energy of a single photon
+
+
+n is the number of photons.
+
+
+A part of the solar spectrum is used by photosynthesis in the form of the wavelength range from 400 to 700 nm only. Spectral radiation is known as photosynthetic active radiation (PAR). According to the measured average solar spectrum, the total solar at the earth's surface within PAR is about 48.7%(KPAR) which is of the incident solar energy (Zhu et al 2008). A greater amount of radiation from solar falls outside the PAR region and has remained unusable in photosynthesis. According to equation (1), 8-10 absorbed photons from the PAR range are required in reducing one molecule of CO2 to glucose. About eight photons contain roughly 1.6 MJ of energy. However, one calorific glucose is 470 kJ/mol of the reduced CO2. The rate of light energy conversion can be calculated this way: KPS=≈ 470 1600 03 /. where KPS is the ratio of the energy content of the resulting occurring organic substance to the total energy of every photon reacting under photosynthesis (for PAR region). Moreover, KPS is highly smaller in real-life applications. Photosynthetic organisms possess multiple intracellular mechanisms for light-harvesting, thus enabling them to maximize radiations from certain wavelengths, or in a shorter range within the PAR region (1921). Due to this factor, the adaptability/manoeuvring of incident light is crucial to improving microalgal biomass production (Seo et al 2014). The highest value of KPS is estimated to be around 13%. Thus the efficiency of sunlight conversion to microalgal biomass is estimated using KPS and KPARK=KPAR×KPS=0.487×0.13≈0.06⁠: The most significant endangering factor for the cultivation of a large quantity of microalgal is centred on the issues of the uses of light. This is highly noted in large cultivation of microalgal outdoors as there is an increase in the growth and performance of every photosynthetic organism rely not solemnly on the light absorbed but on also the composition of the spectral light. The power of absorbed light relies on many factors such as; the volume fraction of culture, the specific position of a cell, etc. The spatial distribution of the light absorbed within the system cultivated is a key factor in microalgal photo physiology and ecology. However, the growth of microalgae is not possible without solar irradiation which also leads to heating water and suspended microalgae.
+
+
+Solar irradiation and heating of microalgae
+
+
+The suspension of microalgae life in water is not possible without significant thermal radiation supplied by the sun. Moreover, it does not rely just on photosynthesis but on also the heating of water within its surrounding. As already stated, for photosynthesis to occur, there has to be significant sunlight in the narrow part of the visible spectral light. It is necessary to understand that the energy from solar radiation is not just for photosynthesis but also the heating of microalgae. Water is highly transparent in the visible and near-infrared spectral radiation (Hale et al 1973) and this radiation from the sun is only absorbed by microalgae. This heating/heat is also transferred to the surrounding bodies of water (near a wavelength of λ=3µm). Due to the small sizes of microalgal cells, it does not require intense/direct heating from the sun, as a result of this, there is thermal equilibrium between microalgae and the surrounding bodies of water. This, however, simplifies the field temperature calculations in the volume where the microalgae are grown. Moreover, the remedy for the transient heat transferred is demanded considering both the integral (over the spectrum) volume absorption of radiation and the heating transfer as a result of heat transfer from conduction and convection. However, we are not considering the problems associated with heat transfer.
+
+
+RADIATIVE TRANSFER
+
+
+Radiative transfer of microalgae in a suspended layer in several studies considering radiative transfer in a composite medium, consisting of absorbing and scattering particles, the researchers have considered the traditional approach and with the radiative transfer equation (RTE) for the intensity of the spectral radiation (Dombrovsky et al 2010, Howell et al 2010, Modest, M. F. 2013). The intensity of radiation does not solemnly rely on spatial variables but also considers two angular coordinates used in determining the direction of radiation. The resultant effect of RTE in a scattered media consists of an integral term used in describing the scattered radiation. An authentic remedy to the RTE in a scattered medium is a highly complicated task. Unfortunately, in the stated issue, it is more convenient to look into the approximate transportation of the scattered phase function and the simplified differential method rather than the complete RTE (Dombrovsky, et al 2010, Dombrovsky 2012). However, scholars should recall recent studies related to this method in solving physically related issues (Dombrovsky et al 2007, Dombrovsky et al 2011, Dombrovsky et al 2012, Dombrovsky et al 2015, Dombrovsky et al 2016, Dombrovsky et al 2017, Dombrovsky et al 2018). A similar methodology has been proposed and used in the aforementioned theoretical papers by other researchers in calculating the radiative transfer in photobioreactors. The linear relationship of RTE makes it feasible and possible to consider differentiating the collimated and diffused components of the radiation field. In so doing, the differential techniques are relatively used in diffusing the components that are predominantly case sensitive to media scattering. The resultant boundary value for spectral irradiance can readily be solution-driven in the sense of complex-shaped photobioreactors. Moreover, it is enough to state that the developed theoretical and computational techniques are sufficient to calculate the field of absorbed spectral solar radiation in photobioreactors.
+
+
+FLUE GAS UTILIZATION BY MICROALGAE
+
+
+The cultivation of microalgae can be used in mitigating the CO2 emitted industrially from power plants by the biotransformation of microalgal biomass. Adjusting the use of microalgae that are based on CO2 sequestration can be used as a sustainable solution for the control of CO2 (Pavlik et al 2017). Approximately, about 1.83-1.88kg of CO2 can be contained in 1kg of microalgal biomass (6667). Typically, the concentration of CO2 in flue gas is around 15 vol.% which is approximately times 400 higher when compared to the atmospheric CO2 (Lam et al 2011, McGinn et al 2011). The various forms of solubilization of mass transferred CO2 into aqueous media of microalgae culture system upon feeding of CO2 includes CO2, CO32−, HCO3− and H2CO3 (depending on the medium pH) and the uptake of microalgae during photosynthesis (Van Den Hende et al 2012). Flue gas feeding significantly leads to the acidification of the growth media, which cannot be accommodated by algae. Aside from CO2, the gas flue generated during the combustion of industrial plants usually contain inhibitory toxic pollutants Sox and NOx, which rapidly affect the growth of microalgae. However, special cultivation that prevents harmful pollutants that strongly affect the growth of microalgae should be well sorted after. Adequate utilization of flue gas can serve as a subordinate for providing a source of carbon for the production of microalgae. Challenges associated with this include the high temperature of the flue gas and the presence of harmful pollutants that prevents the direct use of industrial gas flue for the cultivation of microalgae. The utilization of gas flue has been endorsed industrially and it is presently been used in breweries, cement factories and gas-fired power plants, like the aforementioned factories, generates minimal Sox and NOx concentrations (Moreira et al 2016). Flue gas driven from coal has also been used for the cultivation of microalgae when diluted with a small added amount of air (Van Den Hende et al 2012, Kao, C.-Y. et al 2014, Moheimani, N. R. J. et al 2016). The mitigation of industrial emission of CO2 from the unfiltered stacks of the smoke of about 4 MW coal-fired power plant through biotransformation into microalgal biomass with Desmodesmus sp. Which is the dominant microalgae was demonstrated by (Aslam, A., et al. 2017).
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+Wastewater treatment by microalgae
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+Aside from the consumption of light and CO2 by microalgae, microalgae also require nutrients for their growth and survival. The cultivation of microalgae successfully depends so much on nutrients such as nitrogen and phosphorus. In order words, to achieve an increased growth rate, inorganic fertilizers are used as the major nutrient sources (Lam, M. K., et al 2011). During the growth of microalgae, researchers have noted that microalgae can extract the necessary nutrients and chemicals from different sources including wastewater. The latter represents a renewable source of nutrients and chemicals for microalgae cultivation. Wastewater is currently in use as a source of nutrients for the cultivation of microalgae when it contains a high quantity/concentration of nitrogen and phosphorus. Generally, the processes of wastewater treatment using microalgae include but are not limited to the following; the removal of nutrients, heavy metal ions, and pathogens and the reduction of biological oxygen demand (Mohd Udaiyappan, et al 2017). Most likely, the process is associated with oxygen or biogas production. Microalgae rely on the area of their surface largeness for the uptake of water, nutrients and carbon dioxide when compared to terrestrial plants that require a proper system for water and nutrient uptake (Prajapati, S. K., et al. 2013). The usage of wastewater for the cultivation of microalgae can help improve the sciences of microalgae to biofuel conversion, as well as serve as a water purifier. Microalgae are widely used in the treatment of wastewater but their usage on a large industrial scale for the production of biofuel is widely restricted (Pittman, J. K., et al 2011). Microalgae are the major microorganisms used in the domestic treatment of wastewater in units for oxidation ditches (Butler, E., et al. 2017). Surveys gotten from global wastewater data showed that by utilizing 50% of domestic and industrial wastewaters (495 billion m3) for the growth of algal, with the assumption of 0.5 g/L biomass (Fozer, D., et al. 2017) with 20% lipid content can produce roughly 50 million ton of algal biodiesel (Bhatnagar, A., et al. 2011). The actual limitation for the treatment of wastewater with an algal system is to find some ideal microalgae that can survive in a wastewater environment that has huge efficiency of nutrient and micropollutants that boosts productivity to aid in the removal of high biomass/lipid.
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+The significant benefit of microalgae as promising renewable biomass is noted in figure 1. Include but are not limited to the following;
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+Microalgae can serve as a faster and more effective source of conversion of solar energy into biomass through the process known as photosynthesis.Microalgae have proven to grow from the grounds, which rather are not engrossed for plant growing.The growth of microalgae does not require herbicides and pesticides.Microalgae can grow in a salty water environment.Microalgae can grow in a wastewater environment, thereby making use of nutrients contained therein.Microalgae can generate their heat from gas exhaust coming from heat power plants, which will serve as a source of carbon for the production of microalgae.
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+Figure 1View largeDownload slideApplications of microalgae as renewable biomass.Figure 1View largeDownload slideApplications of microalgae as renewable biomass. Close modal
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+EXTRACTION OF HIGH-VALUE PRODUCTS FROM MICROALGAE
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+However, regardless of the booming interest in microalgae as a source of sustainable renewables biofuel, microalgae to biofuel conversion is controlled by economics. In our modern world, the global production of microalgae biomass industrially is about 10000 tons/year, while the cost of production is about 20-200$/kg, this result has served as a barricade to the use of microalgae as a source of biofuel (Wang, Y., et al. 2016). Reports have it that the cost of microalgae for human consumption ranges from 100 $/kg, and 5-20 $/kg for animals and feed for fish, 1-5 $/kg for the bulk chemicals and 0.4 $/kg for biofuel (Wijffels, R. H. et al 2008). Researchers have recorded that microalgae are composed of 40% lipids, 50% proteins and 10% carbohydrates. One-quarter of the generated lipids are sold to the food and chemical industry for about 2 $/kg, for biodiesel fuel at 0.5 $/kg, soluble proteins for (20%) for food at 5 $/kg, and the rest (80%) for a feed at 0.75 $/kg (Wijffels, et al 2010, Sathasivam, R., et al 2017). Just a few microalgae have commercial significance and they include Chlorella, Dunaliella, Tetraselmis, Botryococcus, Phaeodactylum, Crypthecodinium, Porphyridium, Nitzschia, Chaetoceros, Spirulina, Isochrysis, Haematococcus, Nannochloris, Schizochytrium, and Skeletonema (Sathasivam, R., et al 2017). Microalgae have been in use for decades as food and feed (Vigani, M., et al 2015). There are primarily two categories of market food products gotten from microalgae. The first set is the dried algae the Chlorella and Spirulina with great content of nutrients, most especially vitamin B12, C and D2. These products from microalgae can be sold and used as a dietary supplement and have a high potential to be used in large quantities as a source of protein and carbohydrates. The second category is a specially designed product that is added to food and feeds in others to enhance its nutritional value after its extraction from microalgae. These high worth products are pigments or carotenoids (e.g. ß-carotene, astaxanthin, lutein, zeaxanthin, canthaxanthin, chlorophyll, phycocyanin, phycoerythrin, fucoxanthin), antioxidants (e.g., catalases, polyphenols, superoxide dismutase, tocopherols), fatty acids (e.g. omega-3, docosahexaenoic acid – DHA, eicosapentaenoic acid – EPA and arachidonic acid – ARA), vitamins (e.g. A, B1, B6, B12, C, E, biotin, riboflavin, nicotinic acid, pantothenate, folic acid) and others (e.g. antimicrobial, antifungal, antiviral agents, toxins, amino acids, proteins, sterols). The Components of algae are regularly used in cosmetics as thickening agents, water-binding agents, and antioxidants. Microalgae species are established in the skincare market, the main ones being Arthrospira and Chlorella.
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+The extracts gotten from microalgae can be found in face and skincare products.
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+MICROALGAE-TO-BIOFUEL CONVERSION METHODS
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+In the industrial cultivation of microalgae in large quantities, the conversion of biomass to biofuel is the most assured way in utilizing microalgal biomass. The byproducts of microalgae to biofuel conversion can be used with already existing infrastructure, e.g. it can serve as a motor fuel. Figure 2 shows the life cycle of microalgae as a source of renewable biomass. It is, however, necessary to determine an optimal method for biomass-to-biofuel conversion.
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+Figure 2View largeDownload slideThe life cycle of microalgae as renewable biomass for biofuel productionFigure 2View largeDownload slideThe life cycle of microalgae as renewable biomass for biofuel production Close modal
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+Biodiesel production
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+Biodiesel fuel is typically produced through transesterification of fatty carboxylic acid triacylglycerides that are contained in microalgae. The transesterification reaction between low molecular alcohols (methanol, ethanol) and fatty acids progresses in the presence of alkali or acid catalysts. The chemical absorption of biodiesel fuel is different from that the fossil oil-derived diesel. The major property of the aforementioned biodiesel fuel is its similarity to diesel. The main disadvantage of this biodiesel is its high energy consumption and also the use of deadly organic solvents like methanol. However, it is only lipids that are further converted into fuel without any contribution from microalgae biomass including proteins and carbohydrates. In recent studies, whole lots of high strain microalgae has been discovered (Steinrücken et al. 2017, Kumar, V., et al 2018, Sun, X.-M., et al 2018). Microalgae that are highly enriched with lipids usually provide low biomass yield (Lopez Barreiro, D., et al 2013, Rodofil, L., et al 2009). Results gotten from analyzing thirty different microalgae strains (freshwater and marine) (Rodofil, L., et al 2009) to determine their yield and lipid content, further showed that marine microalgae strains (Porphyridium cruentum and strains Tetraselmis) yielded the highest productivity with a small content of lipid (8.5-14.7 %).
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+BIOETHANOL PRODUCTION
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+Another alternative technique used in the production of biofuel from microalgae is bioethanol production through aerobic and anaerobic fermentation (De Farias Silva, et al 2016). In this technique, the major concentrate that encourages microalgae to ethanol formation are carbohydrates (Ho, S.-H., et al 2014, Kumar et al 2018). Microalgae strains such as Scenedesmus, Chlorella and Chlamydomonas (Ho, S.-H., et al 2013, Kim, M,-S et al 2006) can cumulate a high amount of carbohydrates with over 50% of the total mass which represents mostly starch and cellulose. Furthermore, in the case of biodiesel production, biofuel production from microalgae permits partial biomass conversion. An alternative way of obtaining a high degree of microalgal biomass conversion to biofuel is a simultaneous production of diesel and bioethanol as contributed by (Wang, H., Ji, et al 2014). This conversion technique is expensive and complicated.
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+BIO-OIL PRODUCTION
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+Immediate conversion of microalgal biomass into biofuel can be operated through a process known as pyrolysis or hydrothermal liquefaction (Chiaramonti, D., et al 2015). Either method is used in the production of liquid hydrocarbon fuel known as bio-oil. Pyrolysis is a process for oxygen-free thermochemical biomass conversion under low moisture content and a temperature of 400–600 °С (Kumar, V., et al 2016). Several scholars have researched and noted the findings on microalgae pyrolysis (Peng, W., et al 2000, Grierson, S., et al 2009, Harman-Ware, et al 2013, Francavilla, M., 2015). Depending on the rate of heating of the biomass, pyrolysis can either be "quick" or "slow" (Raheem, A., et al 2015). One significant issue associated with microalgae conversion through pyrolysis is the high moisture content demand for this biomass (80-90 %). The highest biomass moisture content acceptable for pyrolysis is about 20% (Bennion, E. P., et al 2015). Biomass with high moisture content, the HTL is perceived as a promising conversion technique, because it does not demand the drying of microalgae preliminarily. This input was suggested by the most recent scholars (pyrolysis and HTL(Chiaramonti, D., et al 2015, Bennion, E. P., et al 2015, Chiaramonti, D., 2015).
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+HYDROTHERMAL LIQUEFACTION OF MICROALGAE
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+Thermodynamic efficiency
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+Generally, thermochemical techniques for biomass to biofuel conversion can be categorized into dry and wet processes. Biomass to biofuel conversion methods is done in the absence of little or no oxygen. The dry processes are conveyed near atmospheric pressure which is already the dry income raw. Processes such as the traditional torrefaction, gasification and pyrolysis are all dry processes. The P-T diagram for dry and wet (hydrothermal) processes is presented in Figure 3. Torrefaction is conveyed at 200-300°C and it is utilized for the upgrading of wood biomass (Pentananunt, R., et al 1990). The target product of torrefaction is known as a solid fuel (bio-char) such as lignite. However, dry pyrolysis is carried out under a temperature of about 400-600 °C. With pyrolysis, biomass is converted into bio-oil as a major by-product along with syngas (CO + H2) and solid bio-char (Guedes, R. E., et al 2018). The gasification of biomass to the combustible gas mixture is conveyed through partial oxidization of biomass under high temperature (Sansaniwal, S. K., et al 2017), in the range of 700-850 °C. The traditional dry thermochemical conversion technique has become more economically feasible with the increase in moisture content of the biomass. For example, to dry biomass with 80% humidity to a stage of 20% humidity at atmospheric pressure, it is crucial to spend about 10 MJ of thermal energy per 1 kg of dry biomass, the calorific value of the dry biomass is about 15 MJ/kg. The consumption of energy for traditional drying becomes huge. One of the issues associated with moisture is that it will become more significant if microalgae are generally used for the sustainable development of the environment and biofuel production. Moreover, all traditional thermochemical conversion techniques that are the dry processes also need the drying step. At a large scale microalgae usage demands new technical conversion methods; the efficiency of the technique should nevertheless be sensitive to the moisture content of the biomass. To further solve the issues of thermochemical conversion of biomass with a large amount of moisture content, the hydrothermal technologies however should be increasingly employed for usage (Vlaskin, M. S., et al 2017, Chernova, N.I., et al 2017, Chernova et al 2017, Vlaskin, M.S., 2017). Generally, hydrothermal processes are used to denote processes carried out in the presence of liquid water or steam at temperatures greater than 100 °C (Byrappa, K., et al 2001). Hydrothermal processes have a broad range of applications (Byrappa, K., et al 2001, Vlaskin, M. S., et al 2018, Kislenko, S.A., et al 2016, Vlaskin, M.S., et al 2016, Zhuk, A.Z., et al 2016). In the issue of treatment of biomass, the hydrothermal process is divided into hydrothermal carbonization (target product – solid fuel, process temperature of about 250 °C.), hydrothermal liquefaction (target product–liquid fuel, process temperature in the range of 250-450 °C.) and also hydrothermal gasification (target product- gaseous fuel, process temperature greater than 450 °C.).
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+Figure 3View largeDownload slideThermochemical methods for biomass conversion in P-T diagramFigure 3View largeDownload slideThermochemical methods for biomass conversion in P-T diagram Close modal
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+Hydrothermal processes are normally conveyed at high pressure. However, if the temperature of the water is below the critical temperature range, the pressure is normally maintained near the pressure that equates to the temperature of the water. An increased and high operational pressure is one of the major setbacks of hydrothermal processes. The high temperature provides the major advantage of hydrothermal treatment. Particularly, high temperature permits large thermodynamic efficiency of the process. The large thermodynamic efficiency of the hydrothermal treatment is attained by heat recovery.
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+Figure 4 depicts the contrasts between the two processes: the drying standards and the hydrothermal treatment (at 300 °C) of wet biomass. The T-S diagram in figure 4 shows the drying process at atmospheric pressure and the hydrothermal treatment at 300 °C with or without heat recovery. Figure 4a and 4b, illustrates the input of energy in the aforementioned processes, figure 4c, and 4d depicts the feature potential for the heat recovery process and 4e, and 4f show the input of energy in the processes of heat recovery. However, lines 1-2-3 In 4a tallies to the removal of water in the wet biomass at atmospheric pressure. The specific latent heat required to aid in the removal of water from wet biomass through drying at atmospheric pressure (heating to 100 °C and evaporation at 100 °C) is equal to h3−h1=2676−105=2571(kJ/kg), Where h3 is the enthalpy of saturated steam at 100 °C, and h1 is the enthalpy of water at standard condition. This quantity of heat equates to the shaded portion in Figure 4a. The Hydrothermal treatment of wet biomass at a temperature of 300 °C can be estimated by the heating of water conveyed in the sludge at a pressure that brings forth the maintaining of the liquid state of water (lines 1-4 in fig 4b). A specific heat that is demanded hydrothermal treatment at 300 °C is distinguished by the following difference h4−h1=1344−105=1239(kJ/kg)⁠, where h4 is the enthalpy of the liquid water at 300 °C. The quantity of heat corresponds to the shaded portion in 4b. The atmospheric drying demands two times greater energy than hydrothermal treatment at 300 °C with the same amount of moisture. However, the atmospheric drying at low-temperature steam with low recovery potential at a temperature of about 100 °C. An increasingly high enthalpy of this type of steam can be utilized for the heating of liquid water. The temperature difference of about 30 °C is normally utilized as the least gap needed for the transfer of heat from cold to some heated substances. It, therefore, means that the steam with a temperature of 100°C can be utilized for the heating of liquid water up to a temperature of about 70 °C (lines 1-2′ in Figures 4c, and 4e).
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+Figure 4View largeDownload slideT-S diagram showing the processes of drying at atmospheric pressure and hydrothermal treatment at 300 °C. a – the input of energy in the processes of drying at atmospheric pressure; b – the input of energy in the processes of hydrothermal treatment at 300 °C; c – the potential for heat recovery in the processes of drying at atmospheric pressure; d – the potential for heat recovery in the processes of hydrothermal treatment at 300 °C; e – the input of energy in the processes of drying at atmospheric pressure with heat recovery; f – the input of energy in the processes of hydrothermal treatment at 300 °C with heat recovery.Figure 4View largeDownload slideT-S diagram showing the processes of drying at atmospheric pressure and hydrothermal treatment at 300 °C. a – the input of energy in the processes of drying at atmospheric pressure; b – the input of energy in the processes of hydrothermal treatment at 300 °C; c – the potential for heat recovery in the processes of drying at atmospheric pressure; d – the potential for heat recovery in the processes of hydrothermal treatment at 300 °C; e – the input of energy in the processes of drying at atmospheric pressure with heat recovery; f – the input of energy in the processes of hydrothermal treatment at 300 °C with heat recovery. Close modal
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+CONCLUSION
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+In conclusion, it has been shown that the industrial contribution of microalgal biomass can be effectively increased as a result of its global green energy application of microalgae. Specifically, these applications can be utilized in wastewater treatment, industrial mitigation of CO2 emissions, also for oxygen production and capturing of atmospheric CO2. However, the evidence has been shown that it is actively in progress in our modern world. A wide spectrum of microalgae has been applied industrially due to its green application. A significant increase in microalgae demands an effective technique for utilizing microalgal biomass. One problem associated with large scale microalgae utilization is due to its high moisture content in microalgae.
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+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
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+References
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212014-MS
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files/2022/Application of Machine Learning Algorithm for Predicting Produced Water Under Various Operating Conditions in an Oilwell.txt

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+----- METADATA START -----
+Title: Application of Machine Learning Algorithm for Predicting Produced Water Under Various Operating Conditions in an Oilwell
+Authors: Eriagbaraoluwa Adesina, Bukola Olusola
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211921-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Production optimization is often required to manage increase of undesired reservoir fluids especially water in oil and gas wells. However, this activity needs to be guided by science and data rather than a trial-and-error approach of changing the operating conditions of the well to determine the corresponding production response. Well performance models are often used to predict well behavior at different operating conditions but one of the disadvantages of this method is the inability to predict the water cut based on given well parameters. In this work, we applied the random Forest Regression model, well test data and well performance model to predict the expected water cut while changing the operating conditions of a well.We had used four wells to demonstrate the application of machine learning to produced water prediction under different operating conditions. Well performance model which is a combination of Presssure Volume Temperature (PVT) model, inflow performance relationship (IPR) model and vertical lift performance (VLP) model was used to generate the well parameters transferred to the machine learning algorithm. A histogram and box plot were first drawn to understand the distribution of the data and filter the outliers within the dataset because outliers skew the model results. A correlation matrix was now used to understand the relationship between the water cut and the following variables: Flowing Tubing Head Pressure, the Bean Size, the Gas Oil Ratio, and liquid production.Thereafter the Random Forest model was applied to the well parameters to get the predicted values. After getting our predicted values from our model, the model results were evaluated with three regression evaluation metrics, the mean absolute error, the mean squared error and the root mean squared error to compare the predicted water cut values with the actual values and return the margin of error in the predictions. The Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error results were within acceptable tolerance. Therefore, given the minimal error values we can conclude that the model can successfully predict water cut values at different operating conditions.Based on our evaluation, the bar chart predicted values and actual values showed minimal error margins indicating the model's accuracy can be trusted.This paper presents a novel way to estimate the water cut of a well under various operating conditions, a prediction that is not available using existing well performance models.
+
+
+
+
+Keywords:
+upstream oil & gas,
+machine learning,
+artificial intelligence,
+decision tree learning,
+dataset,
+produced water discharge,
+prediction,
+machine learning algorithm,
+information,
+spe-211921-ms
+
+
+Subjects: 
+Formation Evaluation & Management,
+Environment,
+Information Management and Systems,
+Water use, produced water discharge and disposal,
+Artificial intelligence,
+Well Operations and Optimization
+
+
+
+
+Introduction
+
+
+In the industry, exisiting analytical software are unable to estimate water cut values when given well test parameters. Therefore, there is a need to develop a methodology to solve this problem. We present an inexpensive and cheaper way to solve a prevailing problem that will greatly reduce water handing costs in the production process.
+
+
+Excess produced water production has many primary and secondary costs. In 2011, through surveys it was predicted that the oil and gas industry spent in excess of 50 billion dollars on treating and handling produced water. The costs include discharge, trucking, reinjection and the treatment of produced water (Freeman Hill, SPE, Steve Monroe, SPE, and Reshmy Mohanan, SPE, Baker Hughes, 2012). Presently, the cost of produced water treatment has increased and the operators are being charged per barrel of water produced. Therefore, reducing the amount of produced water is an important task that a number of oil and gas companies have to face.
+
+
+The present volume of produced water has affected the global oil production capacity. Y. DU, L. Guan and H. Liang (2005) stated that the that the mean global water cut has reached 75%. This means that, around the world oil and gas operators produce significantly more water for each barrel of oil which is produced. Albeit specific figures of water production are diffcult to access, data collected shows that produced water amounts to more than 90% of waste stemming from the E&P industry).
+
+
+Produced water management has become necessary due to the volume involved, the cost of treatment and the significant environmental impact of pollutants. Many oil producing nations have made laws and guidelines on the quality of produced water to be released into the environment. Water treatment and dumping costs vary depending on quantity and location with the demand of environmental compliance making it a significant cost center in the oil and gas industry. According to Y. Du, L. Guan and H. Liang, the average disposal cost is predicted to be US $4 per barrel of produced water. This cost comprises only capital and operating costs and chemicals for treatment and injection. The annual cost of dealing with produced water is predicted to run into billions of dollas globally. This expenditure is solely as a direct cost of water not taking into account greater losses obtained through the loss of production and reduced reserves. Further resources are expended when taken into account money paid as fees for producing excess quantities of produced water.
+
+
+Increased water production decreases well productivity and forces operators to increase water treatment and disposal systems else it leads to serious environmental problems. The minimization of produced water and production water cut has never been more important as small reductions in the cost of treating produced water will result in the expending of less resources, significant cumulative savings and the elimination of adverse environmental impacts. Concerning environmental impacts, water expenditures and regulations are expected to increase annually; with the expected increase in water volume, it's related costs along with fees to be paid it is important to minimize produced water wherever it can be reduced.
+
+
+APPLICATION OF MACHINE LEARNING
+
+
+Predicting water production is a difficult obstacle due to the complexity and time-variant nature of produced water, an example of water cut trend is shown in Figure 1. This feature makes it difficult to apply common models and get accurate predictions, refined methods such as machine learning (decision trees, random forest algorithm and neural networks) can sort through the complex variable interactions and provide more accurate results. Advanced machine learning techniques like clustering algorithms and random forest algorithm understand production behaviours and correlation and make near-accurate predictions using dominant production attributes to estimate water cut values.
+
+
+Figure 1View largeDownload slideBSW values as measured over time for Well 10 (Nigeria). This figure shows the general trend of produced water to continually increase with an increase in time. Over time the BSW values are projected to further increase.Figure 1View largeDownload slideBSW values as measured over time for Well 10 (Nigeria). This figure shows the general trend of produced water to continually increase with an increase in time. Over time the BSW values are projected to further increase. Close modal
+
+
+This presents a novel way the estimate the water cut of well, trying out various combinations of the independent variables and confidently settling on the most optimized combination which minimizes excess produced water and maximizes profit. It's efficiency and ease of use would greatly revolutionize the well selection process. Figure 2 shows the processes involved in estimating the water cut in each well.
+
+
+Figure 2View largeDownload slideFlowchart showing all steps taken in the completion of this project.Figure 2View largeDownload slideFlowchart showing all steps taken in the completion of this project. Close modal
+
+
+Methods
+
+
+Supervised learning, which is a data analytical task that maps the input variables (well test variables) and output value (BS&W), is employed by learning from the well test data.
+
+
+A Random forest algorithm is a supervised machine learning algorithm that is popularly used in both classification and regression problems. It constructs decision trees on various samples and uses their majority vote for classification and mean in case of regression (Sruthi E.R. — 2021). Figure 3 shows a visual representation of this process.
+
+
+Figure 3View largeDownload slideRandom Forest Algorithm Process Flowchart showcasing how the algorithm works to make predictions.Figure 3View largeDownload slideRandom Forest Algorithm Process Flowchart showcasing how the algorithm works to make predictions. Close modal
+
+
+A Random Forest Regression based machine learning model was trained to predict water cut production once the necessary initial well test parameters were provided.
+
+
+The input variables into the model are Date, Flowing Tubing Head Pressure, the Bean Size, the Gas Oil Ratio, and the Current Gross Production (BPD). The output is the BS&W.
+
+
+The model was trained using real data across a range of at least 10 years. Only wells having stable and good behaviors and sufficient data to satisfy the needed information for prediction of the water cut value will be selected.
+
+
+For features, production data was accessible in monthly intervals, along with the specific date the test was administered.
+
+
+The total length of the dataset is 195 rows with 9 features to be used to train the machine learning model.
+
+
+A histogram and box plot are drawn to understand the distribution of the data and to ensure that trends for each feature are consistent.
+
+
+The data was cleaned, starting with searching for and removing all non-numerical values from the dataset. Next, missing values are identified and depending on the situation the rows are dropped, or the empty values are replaced with the median value for the well provided the well data values across months follows consistent trends.
+
+
+Outliers are identified through observing the created histogram and box plots, the outliers are treated by either dropping the outlier row in some cases or using statistical flooring and capping techniques to limit the values to an acceptable range.
+
+
+The well test dataset was randomly split into a training (80%) and testing (20%) dataset. To maximize the accuracy of the model a comparatively large enough training dataset is used in training the model. If this isn't ensured, the model could sometimes be overfitted and score very highly on the training dataset but not generalize well and score poorly of the testing and the validation dataset.
+
+
+A Random Forest regressor was then fitted to the training data. The model was then tuned by tweaking the hyperparameters to further bolster the generalization capabilities and improve the accuracy. Model accuracy was scored on the mean absolute error, the mean squared error and the root mean squared error.
+
+
+Results
+
+
+Due to confidentiality purposes, only a subset of the data used in the building of the prediction model for Well 10 are shown:
+
+
+In the analysis of the correlation matrix, the impact of all the independent variables is accounted for in the result. The figure shows that the value of the water cut has a significant positive correlation with the test date (0.50) and with the gas oil ratio (0.52). This shows the important time has as a factor in influencing the water cut production of well with the water cut value increasing with time. Other factors such as the Gross rate (0.09) and the Flowing Well Head Pressure (FTHP) (-0.16) show lesser significance on the BSW value based on the analysis of the well test data used in this experiment.
+
+
+This assertion is further supported by the feature importance graph (Figure 6) showing in order of their importance the features most considered when predicting a water cut value based on well test parameters. After data cleaning and arranging, the well test data was randomly split into two categories. 20% of the data was put in the testing group while the remaining 80% was put in the training group. A Random Forest Regressor machine learning algorithm was used in fitting the data. After the building and training of the model, the accuracy and usability were tested using the test data.
+
+
+Figure 4View largeDownload slideSubset of well test data used in the training and the validation of the prediction model from Well 10. All parameters used in the training of the model are present here.Figure 4View largeDownload slideSubset of well test data used in the training and the validation of the prediction model from Well 10. All parameters used in the training of the model are present here. Close modal
+
+
+Figure 5View largeDownload slideSubset of dataset for Well 10 showing the comparison between the actual BSW values and the BSW values predicted by the trained model.Figure 5View largeDownload slideSubset of dataset for Well 10 showing the comparison between the actual BSW values and the BSW values predicted by the trained model. Close modal
+
+
+Figure 6View largeDownload slideCorrelation Matrix of well test data features. This shows the relationship between all the numerical variables present in the dataset on a scale of -1 to +1. Variables which are strongly correlated either positively or negatively tend to fall at the extremes.Figure 6View largeDownload slideCorrelation Matrix of well test data features. This shows the relationship between all the numerical variables present in the dataset on a scale of -1 to +1. Variables which are strongly correlated either positively or negatively tend to fall at the extremes. Close modal
+
+
+Figure 7 shows the plot of the residual values – which is gotten by getting the difference between the actual BS&W values and the predicted BS&W values. From the graph it is seen that the model predicts well with most of the residual values falling in or around 0.
+
+
+Figure 7View largeDownload slideFeature importance of random forest regression algorithm showing how important the model considers each variable when making predictions. In our model, the date is the most weighted variable.Figure 7View largeDownload slideFeature importance of random forest regression algorithm showing how important the model considers each variable when making predictions. In our model, the date is the most weighted variable. Close modal
+
+
+Figure 8 further shows the residual dots on the graph being clustered around the middle indicating high accuracy predictions by the machine learning model. The dots are the residual values clustered around a middle line of 0. The further away from the line the dots are the larger the error in prediction by the model.
+
+
+Figure 8View largeDownload slideLine plot of model residuals which is gotten by subtracting the actual BSW values from the predicted values. The further away from 0 the residuals are, the larger the error made in prediction by the model.Figure 8View largeDownload slideLine plot of model residuals which is gotten by subtracting the actual BSW values from the predicted values. The further away from 0 the residuals are, the larger the error made in prediction by the model. Close modal
+
+
+Figure 9View largeDownload slideFitted vs Residual Plot showing the error margin made by the model predictions. The further the dots are from the dotted line, the larger the error made in the predictions by the model. High accuracy by the model can be inferred from the dots clustered around the dotted line.Figure 9View largeDownload slideFitted vs Residual Plot showing the error margin made by the model predictions. The further the dots are from the dotted line, the larger the error made in the predictions by the model. High accuracy by the model can be inferred from the dots clustered around the dotted line. Close modal
+
+
+The train and test results are illustrated in Figure 7 and Figure 8, where the regression metrics are weighted against each other to measure the accuracy of the model. For the train data sample, the mean absolute error (MAE) was 0.846425, the root mean square error (RMSE) was 1.298971, and the coefficient of determination – the adjusted R2 was greater than 0.95. For the train data sample, the mean absolute error (MAE) was 2.432883, the root mean square error (RMSE) was 3.746367, and the coefficient of determination – the adjusted R2 was 0.905109. While the accuracy figures for the model are reduced when used on the test data, the figures are still strong and accurate with minimal error.
+
+
+Figure 10View largeDownload slideMetrics breakdown of model on training data. All metrics are within acceptable ranges. The R-squared value is ∼0.98 showing that the model explains a large portion of the information contained in the dataset.Figure 10View largeDownload slideMetrics breakdown of model on training data. All metrics are within acceptable ranges. The R-squared value is ∼0.98 showing that the model explains a large portion of the information contained in the dataset. Close modal
+
+
+Figure 11View largeDownload slideMetrics breakdown of model on test data. All metrics are within acceptable ranges. The R-squared value is ∼0.91 showing that the model explains a large portion of the information contained in the dataset.Figure 11View largeDownload slideMetrics breakdown of model on test data. All metrics are within acceptable ranges. The R-squared value is ∼0.91 showing that the model explains a large portion of the information contained in the dataset. Close modal
+
+
+The test data results show that the random forest regression algorithm is very promising in predicting the water cut value and optimizing the amount of produced water gotten from the exploration and production process. This improves the applicability of the predictive model.
+
+
+Conclusion
+
+
+Considering the predictions made regarding the oil producing wells and the values of the metrics used to evaluate the model. The prediction weighted against the actual values of the test dataset has a Mean Absolute Error of 2.432, a Root Mean Squared Error of 3.746 and an Adjusted R-squared of 0.891. Given the minimal error values; we can conclude the model is a successful one and can be used for the future prediction of water cut values for oil and gas wells.
+
+
+In the analysis of the correlation matrix, the impact of all the independent variables is accounted for and the relationship between the variables behave as expected. However, from the tests performed on the result of the regression model we can conclude that this approach for predicting the water cut values of wells based on preexisting data is a functional one. The correlation matrix showed that the independent variables affected the water cut as expected. The bar chart graphing the predicted values and actual values showed minimal error margins indicating the model's accuracy can be trusted.
+
+
+This method is limited by both the quantity and quality of data which is used in the training the model. The data should be of a high quality as well as be treated for outliers as those skew the results and increase the margin for error and reduce the accuracy.
+
+
+Another limitation is the tendency of some oil producing wells to bread their accustomed production trends throwing the model askew.
+
+
+This method presents a novel way the estimate the water cut of well, trying out various combinations of the independent variables and confidently settling on the most optimized combination. It's efficiency and ease of use would greatly revolutionize the well selection process.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+FreemanHill, SPE, SteveMonroe, SPE, and ReshmyMohanan, SPE, BakerHughes – "Water Management – An Increasing Trend in the Oil and Gas Industry".ArunKharghoria, SantiagoGonzalez, and AbdullahAbdul Karim Al-Rabah, Kuwait Oil Company; AlokKaushik, ManuUjjal, ManuSinghal, JacoboMontero, GregorioGonzalez, MikeCheers, EllenZijlstra, and KeithRawnsley, Shell – "Application of Big Data Techniques in a Greenfield Heavy Oil Asset in Kuwait – a Feasibility Study".T.Cross*, K.Sathaye, K.Darnell, D.Niederhut, K.Crifasi (Novi Labs) – "Predicting Water Production in the Williston Basin Using a Machine Learning Model".
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211921-MS
+
+
+

files/2022/Application of Sodium Lauryl Sulfate for Enhanced Oil Recovery of Medium Crude Oil in the Niger Delta Fields.txt

@@ -0,0 +1,215 @@
+----- METADATA START -----
+Title: Application of Sodium Lauryl Sulfate for Enhanced Oil Recovery of Medium Crude Oil in the Niger Delta Fields
+Authors: Kingsley Kelechi Ihekoronye, Hussein Mohammed, Pius Chukwuebuka Onuorah
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211978-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+This research work focuses on the performance of sodium lauryl sulfate as surfactant in enhanced oil recovery of medium crude oil in the Niger Delta fields. Characterization of the sodium lauryl sulfate (surfactant) was carried out to determine the functional groups and morphology of the sample. Different tests such as interfacial tension reduction and adsorption test were conducted to evaluate the effectiveness of the sample in enhanced oil recovery. Core-flooding experiment was performed using the sample to determine the potency of sodium lauryl sulfate in enhanced oil recovery process. The results from this work showed that incremental oil recoveries of 47.8 %, 54.6 % and 56.1 % using Berea core sample (C1F) and 49.3 %, 57.6 % and 58.5 % for core sample (C2F) was observed. The results showed that sodium lauryl sulfate achieve macroscopic sweep displacement efficiency via interfacial tension reduction between the surfactant slugs and the trapped oil which helps to improve oil production.
+
+
+
+
+Keywords:
+waterflooding,
+surfactant,
+upstream oil & gas,
+chemical flooding methods,
+recovery,
+sodium lauryl sulfate,
+surfactant flooding,
+surfactant concentration,
+reservoir,
+reduction
+
+
+Subjects: 
+Improved and Enhanced Recovery,
+Waterflooding,
+Chemical flooding methods
+
+
+
+
+Introduction
+
+
+Surfactants are chemical enhanced oil recovery used in the oil and gas industry to improve oil production due to its ability to alter the wettability of the rock and oil-water interface (Hirasaki et al., 2004). Surfactant is a surface active material that can alter the behavior of the rock surface and reduce the interfacial tension of the oil-water interface (Hocine et al. 2016). Kamal et al., (2017) noted that surfactants are used to reduce interfacial tension, which resulted to an additional mobilization of trapped oil by capillary forces in the rock matrix. The trapped oil in the reservoir (residual oil) cannot be displaced because of the high energy needed to overcome the capillary pressure (Pc) under normal oil–water interfacial tension. Surfactant can be used to overcome this problem due to its ability to reducing oil–water interfacial tension. Samanta and Ojha (2011) noted that in surfactant flooding, the chemical slug may undergoes channeling of oil/water into the reservoir, however, the introduction of polymer mixture, can lead to mobility control, thus preventing chemical slug that could form viscous fingering. Elraies (2010) observed that the Interfacial tension reduction causes the lowering of capillary forces of trapped and residual oil, surfactant adsorb on reservoir rocks can change rock wettability, thereby, increasing oil recovery. Alvarado and Manrique (2010) reported that surfactant can be employed for reduction of the interfacial tension of the oil/water interface hence leads to mobilization of oil left in the reservoir by capillary forces, gravity drainage of the rock matrix. Hematpur et al. (2017) reported that surfactant may be used to overcome the challenge of capillary pressure by reducing the oil/water interfacial tension from 10−30 mN/m to 0.001mN/m which can help in increasing the capillary pressure making oil droplets to be displaced and oil recovery increased.
+
+
+Yu et al., (2010) reported that surfactant flooding maintains low interfacial tension to move the trapped oil, and maintain the surfactant slug during displacement efficiency of the reservoir. In addition, surfactant helps to reduce the interfacial tension between oil and water, so that the trapped oil in the reservoir can be mobilized (Wang et al., 2010). In addition, (Izuwa et al. 2021) noted that surfactants are good displacing fluid which can reduce the interfacial tension of the crude oil in order to improve oil recovery. The experimental study on the use of sodium lauryl sulfate for enhanced oil recovery processes is limited. Hence, there is need to study the behavior of sodium lauryl sulfate and its potential application in enhanced oil recovery.
+
+
+Therefore, the focus of this research work is the performance evaluations of sodium lauryl sulfate as surfactant in enhance oil recovery of medium crude oil in the Niger Delta fields.
+
+
+Materials
+
+
+The surfactant used in this study is sodium lauryl sulfate shown in figure 1. Table 1 presents the property of the surfactant. The oil sample used for this study was collected from a marginal field operating within the Niger Delta environment with API gravity of 29.4°Cand viscosity 1.2 cp. The cores were collected from offshore oil field. The core-flooding equipments were supplied from Tulsa Oklahoma USA (UFS-200 core laboratory).
+
+
+Figure 1View largeDownload slideSample of sodium lauryl sulfateFigure 1View largeDownload slideSample of sodium lauryl sulfate Close modal
+
+
+Table 1Property of the sodium lauryl sulfate Surfactant
+            . Type
+            . Properties
+            . Characteristic
+            . Supplier
+            . Sodium lauryl sulfate Anionic surfactant Appearance: white pH: 7.3 Density: 1.102 g/cm2 at 28˚C Very soluble in water.Slightly viscous in solution High foam-forming ability. PCC Exol SA Surfactant
+            . Type
+            . Properties
+            . Characteristic
+            . Supplier
+            . Sodium lauryl sulfate Anionic surfactant Appearance: white pH: 7.3 Density: 1.102 g/cm2 at 28˚C Very soluble in water.Slightly viscous in solution High foam-forming ability. PCC Exol SA View Large
+
+
+Methods
+
+
+Characterization of sodium lauryl sulfate
+
+
+Fourier Transform Infrared Spectroscopy Analysis (FTIR)The FTIR analysis was used to determine the functional group of the sample. The figure 2 showed the present of NH group at broad band of (721 cm−1 - 1091 cm−1) due to the vibration of amide. There is present of carbonyl (C=O) group at band spectrum (1466 cm−1 – 1654 cm−1). The figure shows the present of OH group corresponding to the broad band (2847 cm−1 - 2953cm−1).Scanning Electron Microscopy Analysis (SEM)SEM analysis was performed on the surfactant to determine the morphology of the sample. Figure 3 showed intermolecular hydrophobic association of the Sodium Lauryl Sulfate chains due to the present of the C-H group in the sample. There is evidence of interconnection in the crystalline structure of the sample associated with the present of the OH molecules.
+
+
+Figure 2View largeDownload slideFunctional group of the Sodium Lauryl SulfateFigure 2View largeDownload slideFunctional group of the Sodium Lauryl Sulfate Close modal
+
+
+Figure 3View largeDownload slideMorphology of Sodium Lauryl SulfateFigure 3View largeDownload slideMorphology of Sodium Lauryl Sulfate Close modal
+
+
+Test to evaluate the effectiveness of the sample on EOR Process
+
+
+Interfacial tension test (IFT)
+
+
+Interfacial tension test was done using tensiometer machine to determine the surface tension of the fluid. Figure 4 indicates that the IFT test result obtained showed a decreasing interfacial tension reduction at increase in surfactant concentration which could suggest that the sample can alter the wettability of the cores from oil-wet to water-wet which can result to enhance oil recovery. Nowrouzi et al. (2020) observed that increase in surfactant concentration leads to interfacial tension reduction to a minimum value referred to critical micelle concentration (CMC) of the surfactant. Manshad and Rezaei (2017) reported that interfacial tension reduction is the main mechanism to surfactant flooding in order to improve hydrocarbon productivity.
+
+
+Figure 4View largeDownload slideGraph of IFT against concentrationFigure 4View largeDownload slideGraph of IFT against concentration Close modal
+
+
+Adsorption test
+
+
+Adsorption test was done using spectrum-lab machine to determine the rate of adsorption of sodium lauryl sulfate on the cores. The result (figure 5) showed that the sodium lauryl sulfate had low adsorption rate at different surfactant concentrations. (Belhaj et al., 2020) reported that high surfactant adsorption rate can decrease the surfactant concentration in the injected solution, which may affect the capacity to reduce the oil/ water interfacial tension. In addition, the effectiveness of surfactant to mobilize the trapped oil in enhanced oil recovery can be influenced by the adsorption process (Liu et al., 2019). Low surfactant adsorption rate plays a significant role in wettability change (oil-wet to water-wet) (Ojo et al., 2020). Excess surfactant adsorption can reduce the concentration in the aqueous phase and negatively affect the recovery efficiency of surfactant in enhance oil recovery processes (Saxena et al., 2019).
+
+
+Figure 5View largeDownload slideGraph of adsorption rate (sodium lauryl sulfate)Figure 5View largeDownload slideGraph of adsorption rate (sodium lauryl sulfate) Close modal
+
+
+Formulation of fluid for Core-Flooding Experiment
+
+
+Sodium lauryl sulfate was dissolved in water at different surfactant concentrations of 2g (0.2 wt %), 4g (0.4 wt %) and 6g (0.6 wt %) respectively. The dissolved samples (figure 6) were used in core flooding experiment to determine the ultimate oil recovery. Table 2 showed the property of the cores used.
+
+
+Figure 6View largeDownload slideSodium Lauryl Sulfate in solutionFigure 6View largeDownload slideSodium Lauryl Sulfate in solution Close modal
+
+
+Table 2Property of the cores Properties
+            . Core C1F
+            . Core C2F
+            . Height (cm) 8.54 8.38 Radius (cm) 1.40 1.38 Weight of the dry core (g) 9.27 7.45 Weight of the saturated core (g) 15.23 14.02 Pore volume (cm) 14.17 12.58 Bulk volume (cm) 52.59 50.14 Swi 0.23 0.26 Porosity (%) 28.8 27.8 Permeability (md) 155.2 176.5 Properties
+            . Core C1F
+            .��Core C2F
+            . Height (cm) 8.54 8.38 Radius (cm) 1.40 1.38 Weight of the dry core (g) 9.27 7.45 Weight of the saturated core (g) 15.23 14.02 Pore volume (cm) 14.17 12.58 Bulk volume (cm) 52.59 50.14 Swi 0.23 0.26 Porosity (%) 28.8 27.8 Permeability (md) 155.2 176.5 View Large
+
+
+Procedures of the Core-Flooding Experiment
+
+
+The core-flooding experiment was conducted at a temperature of 90 ˚C and pressure of 800 psi. In this experiment, two methods of core-flooding experiment were employed. In the first stage, waterflooding was used as the displacing fluid to recovered oil. In the second stage, sodium lauryl sulfate (surfactant) was used as a tertiary recovery mechanism to recovered additional oil after waterflooding recovery process. The core flooding experiment were repeated at different surfactant concentrations of 2g (0.2 wt %), 4g (0.4 wt %) and 6g (0.6 wt %) respectively in order to evaluate the effect of the surfactant in enhanced oil recovery. Figure 7 shows the experimental set-up of the core-flooding process.
+
+
+Figure 7View largeDownload slideCore-flooding experimental set-upFigure 7View largeDownload slideCore-flooding experimental set-up Close modal
+
+
+RESULTS AND DISCUSSION
+
+
+Comparison of result between water-flooding and surfactant flooding
+
+
+Discussion of Results
+
+
+Table 3 presents the outcome of the result in the core-flooding experiment using Berea core sample denoted as (C1F). The result shows that water-flooding had oil recovery of 8.2 mils (40. 0 %) of the total oil in place. However, the addition of surfactant into the formation causes increase in oil recovery to 9.8 mils (47.8 %), 11.2 mils (54.6 %), 11.5 mils (56.1 %) of the total oil in place at different surfactant concentration of 0.2 wt %, 0.4 wt %, and 0.6 wt % respectively. More so, table 4 shows the core-flooding result using Berea core sample (C2F). The table indicates oil recovery of 8.4 mils (41.0 %) by water flooding while surfactant flooding had oil recovery of 10.1 mils (49.3 %), 11.8 mils (57.6 %) and 12.0 mils (58.5 %) at the same surfactant concentration.
+
+
+Table 3Core-flooding result of the performance of oil recovered with surfactant flooding using core C1F Concentrations
+            . Core samples
+            . Oil recovery (%)
+            . Total oil in place (mil)
+            . Total oil recovered (mil)
+            . Water-flooding C1F 40.0 20.5 8.2 Surfactant conc. (0.2 wt %) C1F 47.8 20.5 9.8 Surfactant conc. (0.4 wt %) C1F 54.6 20.5 11.2 Surfactant conc. (0.6 wt %) C1F 56.1 20.5 11.5 Concentrations
+            . Core samples
+            . Oil recovery (%)
+            . Total oil in place (mil)
+            . Total oil recovered (mil)
+            . Water-flooding C1F 40.0 20.5 8.2 Surfactant conc. (0.2 wt %) C1F 47.8 20.5 9.8 Surfactant conc. (0.4 wt %) C1F 54.6 20.5 11.2 Surfactant conc. (0.6 wt %) C1F 56.1 20.5 11.5 View Large
+
+
+Table 4Core-flooding result of the performance of oil recovered with surfactant flooding using core C2F Concentrations
+            . Core samples
+            . Oil recovery (%)
+            . Total oil in place (mil)
+            . Total oil recovered (mil)
+            . Water-flooding C2F 41.0 20.5 8.4 Surfactant conc. (0.2 wt %) C2F 49.3 20.5 10.1 Surfactant conc. (0.4 wt %) C2F 57.6 20.5 11.8 Surfactant conc. (0.6 wt %) C2F 58.5 20.5 12.0 Concentrations
+            . Core samples
+            . Oil recovery (%)
+            . Total oil in place (mil)
+            . Total oil recovered (mil)
+            . Water-flooding C2F 41.0 20.5 8.4 Surfactant conc. (0.2 wt %) C2F 49.3 20.5 10.1 Surfactant conc. (0.4 wt %) C2F 57.6 20.5 11.8 Surfactant conc. (0.6 wt %) C2F 58.5 20.5 12.0 View Large
+
+
+Figure 8-9 investigated the effects of surfactant concentration on oil recovery. The graphs indicate that injection of surfactant played a significant role in improvement of hydrocarbon productivity via mobilization of residual oil from trapped oil in the reservoir. The figure also shows that increase in surfactant concentration leads to increase in oil recovery at different surfactant concentration. However, there is no additional oil recovery observed above surfactant concentration of 0.6 wt%. The increase in oil recovery is due to IFT reduction and wettability change of the cores. Al-Sulaimani et al. (2012) reported that oil recovery is mainly due to the volumetric sweep displacement efficiency that occurs during water-flooding injection.
+
+
+Figure 8View largeDownload slideGraph of oil recovery against time using Core (C1F)Figure 8View largeDownload slideGraph of oil recovery against time using Core (C1F) Close modal
+
+
+Figure 9View largeDownload slideGraph of oil recovery against time using Core (C2F)Figure 9View largeDownload slideGraph of oil recovery against time using Core (C2F) Close modal
+
+
+Figure 10-11 presents the result comparison between water-flooding and surfactant flooding. The results show the effectiveness of the surfactant to improve oil recovery. The use of sodium lauryl sulfate for EOR applications showed great improvement as a good displacing fluid and mobility control. Torres et al. (2011) reported that surfactant has the ability to breakdown the capillary forces existing between two immiscible liquids (that is surfactant slugs and residual oil). The higher the surfactant concentration, the greater is the performance of the surfactant to improve oil recovery through oil/water interface.
+
+
+Figure 10View largeDownload slideGraph of oil recovery against concentration for Core (C1F)Figure 10View largeDownload slideGraph of oil recovery against concentration for Core (C1F) Close modal
+
+
+Figure 11View largeDownload slideGraph of oil recovery against concentration for Core (C2F)Figure 11View largeDownload slideGraph of oil recovery against concentration for Core (C2F) Close modal
+
+
+Conclusion
+
+
+In this study, the use of sodium lauryl sulfate as surfactant was investigated in enhanced oil recovery of medium crude sandstone reservoir in the Niger Delta oil fields. In this experiment, surfactant flooding achieved macroscopic sweep displacement efficiency via interfacial tension reduction between the surfactant slugs and the trapped oil. The following conclusions can be established from this study:
+
+
+The effectiveness of sodium lauryl sulfate as surfactant was investigated for use in enhanced oil recovery of medium crude oil in the Niger Delta fields.Sodium lauryl sulfate shows better oil recovery when compared to conventional water-flooding.Surfactant interfacial tension reduction contributed to improved oil recovery.The core-flooding experimental results showed that incremental oil recoveries of 47.8 %, 54.6 % and 56.1 % using Berea core sample (C1F) and 49.3 %, 57.6 % and 58.5 % for Berea core sample (C2F) respectively was observed.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Al-SulaimaniH., Al-WahaibiY, Al-BahryS, ElshafieA, Al-BemaniA, JoshiS (2012). Residual-Oil Recovery through Injection of Bio-Surfactant, Chemical Surfactant, and Mixtures of Both Under Reservoir Temperatures: Induced-Wettability and Interfacial Tension Effects. S.P.E Reserv. Eval. Eng. (15): pp 210–217.Google Scholar AlvaradoV., ManriqueE., (2010). Enhanced Oil Recovery: Update Review. Energies (3), pp 1529–1575.Google ScholarCrossrefSearch ADS  Belhaj, A. F., Elraies, K. A., Mahmood, S. M., Zulkifli, N. N., Akbari, S., Hussien, O. S., (2020). The Effect Of Surfactant Concentration, Salinity, Temperature, And Ph On Surfactant Adsorption For Chemical Enhanced Oil Recovery: A review. Pet J. Explor. Prod. Technol. 10, 125–137.http://dx.doi.org/10.1007/s13202-019-0685-y.Google ScholarCrossrefSearch ADS  Elraies, K. A., Tan, I. M., Awang, M., Saaid, I. (2010). The Synthesis and Performance of Sodium Methyl Ester Sulfonate For Enhanced Oil Recovery. Pet. Sci. Technol. (28), pp 1799–1806.Google ScholarCrossrefSearch ADS  HematpurH., MahmoodS. M., AkbariS., NasrN. H., AwangM., NegashB., AkhirN. M., LubisL., RafekA. M. (2017). Comparison Study on Anionic Surfactants and Mixed Surfactant Behavior in SAG Foam Process. In: (eds) ICIPEG. Springer.Google ScholarCrossrefSearch ADS  Hirasaki, G.J., Miler, C. A., Pope, G. A., Jackson, R. E. (2004). Surfactant Based Enhanced Oil Recovery and Foam Mobility Control. 2nd Annual Technical Report, July 2004, DE-FC26-03NT15406.Google Scholar ManshadA. K, RezaeiM. (2017). Wettability Alteration and Interfacial Tension (IFT) Reduction In Enhanced Oil Recovery (EOR) Process With Ionic Liquid Flooding. Mol J. Liq. (248): pp 153–162.Google ScholarCrossrefSearch ADS  Nowrouzi, I., Mohammadi, A. H., Manshad, A. K., (2020). Water-Oil Interfacial Tension (IFT) Reduction And Wettability Alteration In Surfactant Flooding Process Using Extracted Saponin From Anabasis Setifera Plant. Petrol J. Sci. Eng. (189), pp 106–118.http://dx.doi.org/10.1016/j.petrol.2019.106901.Google ScholarCrossrefSearch ADS  Liu, Z., Ghatkesar, M. K., Sudhölter, E. J. R., Singh, B., Kumar, N., (2019). Understanding The Cation-Dependent Surfactant Adsorption On Clay Minerals In Oil Recovery. Energy Fuels33, pp 12319–12329.http://dx.doi.org/10.1021/acs.energyfuels.9b03109.Google ScholarCrossrefSearch ADS  Ojo, O. F., Farinmade, A., John, V., Nguyen, D., (2020). A Nano-Composite of Halloysite/Surfactant/Wax to Inhibit Surfactant Adsorption onto Reservoir Rock Surfaces for Improved Oil Recovery. Energy Fuels34, 8074–8084.http://dx.doi.org/10.1021/acs.energyfuels.0c00853.Google ScholarCrossrefSearch ADS  SamantaA., OjhaK. (2011). Surfactant and Surfactant-Polymer Flooding for Enhanced Oil Recovery. Adv. Petrol. Explor. Dev. 2: pp 13–18.Google Scholar HocineS., CuencaA., MagnanA., TayA., MoreauP. (2016). The Enhanced Oil Recovery: An Extensive Study of the Thermal Stability of Anionic Chemical EOR Surfactant—Stability in Aqueous Solutions. In: IPTC-18974-MS.Google Scholar IzuwaN. C., NwoguN. C., WilliamsC. C., IhekoronyeK. K., OkerekeN. U.OnyejekweM. I. J. (2021). Experimental Investigation of Impact of Low Salinity Surfactant Flooding For Enhance Oil Recovery: Niger Delta Field Application. Petrol. & Gas Eng. Vol. 12 (2), pp. 55–64.Google ScholarCrossrefSearch ADS  KamalM. S, HusseinI. A, SultanA. S (2017). Review on Surfactant Flooding: Phase Behavior, Retention, IFT, And Field Applications. Energy Fuels31(8): pp 7701–7720.Google ScholarCrossrefSearch ADS  Saxena, N., Kumar, A., Mandal, A., (2019). Adsorption Analysis of Natural Anionic Surfactant for Enhanced Oil Recovery: The Role Of Mineralogy, Salinity, Alkalinity and Nanoparticles. Petrol J. Sci. Eng. (173), pp 1264–1283.http://dx.doi.org/10.1016/j.petrol.2018.11.002.Google ScholarCrossrefSearch ADS  TorresL, MoctezumaA, AvendanoR. J, MunozA, GracidaJ. (2011). Comparison of Synthetic Surfactant and Bio-Surfactant for Enhanced Oil Recovery. Pet J. Sci. Eng. (76) pp 6–11.Google ScholarCrossrefSearch ADS  Wang, Y., Zhao, F., Bai, B., Zhang, J., Xiang, W., Li, X., & Zhou, W. (2010). Optimized Surfactant Interfacial Tension and Polymer Viscosity for Surfactant-Polymer Flooding in Heterogeneous Formations. SPE 127391. Paper presentation at the SPE Improved Oil recovery Symposium held in Tulsa, Oklahoma, USA, 24-28 April 2010.Google Scholar Yu, Q., Jiang, H., Zhao, C., (2010). Study Of Interfacial Tension Between Oil and Surfactant Polymer Flooding. Pet. Sci. Technol. (28), pp 1846–1854.Google ScholarCrossrefSearch ADS  
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211978-MS
+
+
+

files/2022/Artificial Neural Networks for Geothermal Reservoirs Implications for Oil and Gas Reservoirs.txt

@@ -0,0 +1,358 @@
+----- METADATA START -----
+Title: Artificial Neural Networks for Geothermal Reservoirs: Implications for Oil and Gas Reservoirs
+Authors: Calista Dikeh, Chinaza Ikeokwu, ThankGod Itua Egbe, Murphy Nnamdi Ochuba, Moromoke Adekanye, Emmanuel Anifowose, Esuru Rita Okoroafor
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212028-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Subsurface numerical models take a significant time to build and run. For this reason, the energy industry has been looking towards proxy models that could reduce model computational time. With the advancement of artificial neural network algorithms, building proxy models has become more efficient, and has enabled quick forecasting and quick reservoir management decision-making.In this study, we used a geothermal reservoir to evaluate the suitability of two deep learning algorithms, feed forward neural network and convolutional neural network, for proxy modeling. We used metrics such as the mean square error, losses, number of parameters for the model, and time to run, to compare the two deep learning algorithms.From our study, we determined that the convolutional neural network resulted in less error than the feed forward network and used less hyperparameters. However, the feed forward network was significantly faster than the convolutional neural network. The process of building the proxy model shows how a similar approach can be followed for oil and gas reservoir modeling and demonstrates the feasibility of neural networks in subsurface reservoir modeling and forecasting.
+
+
+
+
+Keywords:
+algorithm,
+artificial neural network,
+reservoir,
+neural network,
+fracture aperture,
+prediction,
+upstream oil & gas,
+artificial intelligence,
+learning algorithm,
+dataset
+
+
+Subjects: 
+Information Management and Systems,
+Neural networks
+
+
+
+
+Introduction
+
+
+Background on Geothermal Energy
+
+
+Energy is a very essential part of our lives. Over the last few decades, energy consumption has increased exponentially due to population growth, rising economic activities, and technological advancements. The use of fossil fuel to meet energy demand has resulted in some adverse effects on the environment, particularly increased carbon footprint, higher risk of climate change, and volatile fuel prices among other challenges. Some of the challenges with the use of fossil fuels include anthropogenic greenhouse gas emissions, and fossil fuels are susceptible to exhaustion, i.e., they are nonrenewable. These issues have led the energy industry to search for sustainable energy sources that can meet energy demand in the near and long term.
+
+
+Geothermal energy is among the existing renewable sources of energy whose potential of increasing the energy capacity of certain regions is yet to be fully maximized. Geothermal energy as heat energy obtained from the earth crust. While geothermal energy shares many of the advantages of low carbon emitting and renewable energy sources, geothermal power is a very predictable and reliable source of energy making it an appropriate source for meeting baseload energy demand.
+
+
+(Onay, 2020) identified three crucial conditions that must be present to harness geothermal energy, which include high heat content, sufficient permeability, and heat carrier fluid. The heat carrier fluid is either native to the geothermal reservoir or circulated through permeable formations with high heat content in order to gain heat from formation and transfer to the surface for further utilization.
+
+
+To improve the yield from geothermal systems for robust power generation and expand the areas in which geothermal energy can be tapped, significant research on technology improvements for geothermal resources has been carried out by various researchers. One of such resulted in the emergence of cutting-edge enhanced geothermal systems (EGS) (NREL 2021). Enhanced geothermal systems offer the opportunity of exploiting the vast energy resources contained in hot low permeability rocks where the natural flow capacity of the system may not be sufficient to support adequate power production until it is enhanced by opening up existing fractures and propagating new fractures, thus creating permeability (Okoroafor and Horne, 2018). To harness the geothermal resource using enhanced geothermal technology, heat contained in the hot low permeability rock is captured through an injected fluid after permeability is increased. Permeability is increased by either opening existing fractures or by propagating new ones. An enhanced geothermal system involves extracting heat energy from a hot, fractured dry rock by drilling an injection well through which cold water is injected into the hot rock layers. The fractures help the cold water migrate through the hot rock layers, and one or more production well(s) are used to return the heated water to the surface for power generation.
+
+
+The temperature of the produced fluid, which in essence is the measure of the thermal performance of the geothermal system, is greatly affected by the nature of the fracture aperture. Brown (1987) showed that real fractures are not smooth but have rough surfaces which could lead to tortuous flow path. The presence of rough fracture aperture may result in flow channeling. Channeling is a flow condition where a significant portion of the fracture area is not contributing to the overall flow (Hakami and Larsson, 1996). The surface area available for heat flow is reduced with reduction in fracture flow area and this would in turn reduce the heating thereby reducing the total energy produced by geothermal reservoirs. That been said, it can be concluded that any geothermal system without the three major conditions spelled out by Onay (2020) can be harnessed through the enhanced geothermal technology.
+
+
+The Concept of Machine Learning and Deep Learning
+
+
+Machine learning is a branch of artificial intelligence concerned with the development of computer software that can learn on its own. It is the study and construction of algorithms that can learn from data and make predictions. Machine learning algorithms are mathematical functions with a large number of parameters that map inputs (or features) to one or more outputs (or targets). Machine learning is closely related to, and frequently overlaps with, computational statistics, a discipline that specializes in prediction. It also has strong ties to mathematical optimization, which provides the field with methods, theory, and application domains.
+
+
+Depending on the nature of the learning signal or feedback available to a learning system, machine learning tasks are typically classified into three broad categories - supervised learning, unsupervised learning, and reinforcement learning. Supervised learning refers to a set of problems in which a model is used to learn a representation between input examples and output variables. In this case, the machine learning algorithm is trained on labeled data. There are two types of supervised learning problems - classification and regression. Classification entails predicting a class label, while regression entails predicting a numerical value. One or more input variables can be used in both classification and regression problems, and input variables can be of any data type, such as numerical or categorical. Both variables in this study have continuous values, indicating that it is a regression problem.
+
+
+In unsupervised learning, the learning algorithm is given no labels and is left to find structure in its input on its own. Unsupervised learning algorithms can adapt to the data by dynamically changing hidden structures or discovering hidden patterns in data instead of a defined and fixed problem statement. The machine is trained on an unlabeled dataset and predicts the outcome without any human assistance.
+
+
+In reinforcement learning, a computer program interacts with a dynamic environment in which it must accomplish a certain aim without any instructor directly informing it if it has achieved its goal or not. Reinforcement learning is a feedback-based process in which an agent actively explores its surroundings by striking a trail, taking action, learning from experiences, and improving its performance.
+
+
+Deep learning (also known as deep machine learning, deep structured learning or DL) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data using model architectures with complex structures or otherwise composed of multiple non-linear transformations. Deep learning is a subset of an extended group of machine learning techniques that rely on learning data representations and in which artificial neural networks, which are inspired by the human brain, learn from massive quantities of data. A deep learning model is programmed to analyze data in a logical manner, similar to how a human would draw conclusions. This model allows an algorithm to determine whether or not a prediction is accurate using its own neural network, without the need for human intervention. Deep neural networks (DNNs) are defined in this study as an artificial neural network with multiple hidden layers of units between the input and output layers (Schmidhuber, 2014). In deep learning, the algorithm is branched into deep neural networks, convolutional neural networks, recurrent neural networks, and generative models that can produce or generate new content, such as the generative adversarial networks and autoencoders (Okoroafor et al., 2022).
+
+
+Two deep learning techniques were chosen for this study and utilized to create regression models based on the dataset. These regression models may be used to evaluate the correlations between the dependent variable and the independent variable. The algorithms used here are Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). Figure 1 shows the categories of machine learning and deep learning techniques.
+
+
+Figure 1View largeDownload slideCategorization of machine learning and deep learning techniques used in this study, modified from the work by Okoroafor et al. (2022).Figure 1View largeDownload slideCategorization of machine learning and deep learning techniques used in this study, modified from the work by Okoroafor et al. (2022). Close modal
+
+
+An artificial neural network (ANN) learning algorithm, also referred to as "neural networks", is a machine learning technique based on how biological nervous systems process data. In other words, it is a computer simulation of biological structures. Artificial neural networks are versatile computer techniques that may be used for both classification and regression. It is composed of many highly interconnected processing elements(neurons) working in unison to solve specific problems (Chu et al., 2016). ANNs are usually used to model complex relationships between inputs and outputs. The model is trained by providing the input and output data samples to get the ANN to provide a desired output from a given input. In a typical ANN structure, there are three layers: Input layer, one or more hidden layers, and output layer. Figure 2 shows the structure of an ANN model. The input is the values of the independent variables, and it takes raw domain input; the hidden layers compute all of the features entered through the input layer and send the results to the output layer; the output is the values of the dependent variables, and it brings the knowledge gathered through the hidden layer and outputs the final value as a result. In each of the layers in ANN, there are nodes called neurons. A node is the building block that processes the data in the network through a sum and transfer function (Badr et al., 2019). The tuning of these parameters, is what leads to developing the model that can be used for forecasting and predictions.
+
+
+Figure 2View largeDownload slideStructure of an Artificial Neural Network (TIBCO, 2022)Figure 2View largeDownload slideStructure of an Artificial Neural Network (TIBCO, 2022) Close modal
+
+
+Convolutional neural networks (CNNs), which are based on human vision, have emerged as the de facto standard for computer vision tasks. These neural networks are built to withstand invariant changes such as scaling, translation, and rotation. It is also a well-known fact that CNNs have been extensively used in significant image recognition tasks. CNNs and their invariants are frequently employed in many areas other than computer vision. Convolutional Neural Networks, or ConvNets for short, are a type of Deep Neural Network that detect and categorize certain characteristics in pictures and are frequently used in image analysis. CNNs operate based on the mathematical function of convolution - a linear operation where two functions produce a third function through their dot product; the third function expresses how the shape of one function is modified by the other (Gurucharan, 2020). Figure 3 shows a typical CNN architecture model.
+
+
+Figure 3View largeDownload slideBasic Convolutional Neural Network Architecture (Gurucharan, 2020)Figure 3View largeDownload slideBasic Convolutional Neural Network Architecture (Gurucharan, 2020) Close modal
+
+
+In this research, we describe the process for constructing an ANN model and a CNN model that can predict the temperature profile of a geothermal reservoir. We also analyze and contrast the results of both neural network model development.
+
+
+Statement of the Problem
+
+
+The study by Okoroafor et al. (2022) explained that most geothermal resources have attracted little or no private investors due to the medium to high project and financial risks associated with the early stages of resource development, hence they have remained undeveloped. To effectively discover and de-risk the development, a lot of technological input is required in all aspects of the resource development from exploration to production. Geothermal resource development is one area that has need for technological input, and the ability to have models that can predict the performance of a geothermal resource, confirm its viability, will be valuable in derisking the project. Subsurface numerical models however take a significant time to build and run. For this, the energy industry has been looking towards AI-based subsurface models. Specifically for enhanced geothermal systems, accounting for the fracture roughness would lead to an increase in the model computational time. Thus, it is necessary to develop proxy or surrogate models that use the fracture roughness to predict the fluid temperature at the producer, which is a time series data. This therefore would constitute the aim of this research. Figure 4 shows the images of two different fracture apertures and their resulting temperature profiles.
+
+
+Figure 4View largeDownload slideTwo different fracture images resulting in different temperature profiles.Figure 4View largeDownload slideTwo different fracture images resulting in different temperature profiles. Close modal
+
+
+Objectives
+
+
+The main purpose of this study is to develop an AI-based model for predicting the thermal performance of an enhanced geothermal system. The input parameter would be the fracture roughness while the temperature profile is the output. This study would be designed such that two deep learning algorithms are explored, and their performance compared. The two deep learning algorithms are the feed forward neural network and the convolutional neural network. Validation of the models would be done with some of the acquired data set. The testing of the developed models would also be done with a good percentage of the data at hand. The models would be trained to optimum performance so they can be adopted in other fields that have challenges similar to those in the field of geothermal energy, such as the oil and gas industry. This would, among other things, foster the implementation of transfer learning.
+
+
+Scope of the Study
+
+
+The application of machine learning to the development of models for predicting the temperature profile of an EGS is not a new idea, nor is developing a model for predicting the temperature profile of an EGS a new idea. Pandey and Singh (2020) used an artificial neural network (ANN) to predict the temperature profile of an EGS but excludes the use of fracture roughness as an input parameter. Onay (2020) derived an analytical solution to investigate heat transfer mechanisms taking place in an EGS mainly for predicting temperature profile of produced fluid based on various wellbore and completion design parameters. This research seeks to compare the performance of two deep learning algorithms when used to develop surrogate models for predicting temperature time series data of an EGS. The selected deep learning algorithms are feed forward neural network and convolutional neural network. The input parameter for both algorithms would be the fracture aperture which exists as images. The number of hidden layers will be one of the hyperparameters to be determined during the implementation of the chosen architecture for each of the deep learning algorithms. The performance of both algorithms would be compared using the developed models metrics of mean squared error (MSE) and coefficient of determination (R2), the training time for each algorithm, and the total number of parameters deployed by each algorithm for the training. In addition, libraries such as Tensorflow and Keras will be used to support the model’s training.
+
+
+Methodology
+
+
+A proper selection of the machine learning algorithm to be utilized is critical for effective training and deployment of a machine learning model. In most situations, various algorithms are examined, and the algorithm with the best performance is selected in the end. Figure 5 depicts the research workflow, which consists of five steps. The acquisition of data, also known as data gathering, is the first step. Data processing, which includes data normalization and data splitting for the various algorithms, is the second step. The third step is to select the model architecture of the different algorithms. The next step is to train models using the different algorithms. Model evaluation, which includes evaluating, predicting and comparing alternative algorithm models, is the final step.
+
+
+Figure 5View largeDownload slideSteps taken in the machine learning model developmentFigure 5View largeDownload slideSteps taken in the machine learning model development Close modal
+
+
+Data Acquisition
+
+
+The dataset, which is the foundation of modeling, is the most important component in machine learning analysis. A large amount of usable data may be obtained and important insights into the relationship between different values can be gained with the aid of data gathering and processing. In this step, incorrect variables are filtered out, misfits are eliminated, missing values are filled in, and a combined dataset for model training is generated. It is vital to carefully evaluate the parameters that determine the temperature of the fluid produced by any enhanced geothermal system in order to completely comprehend its thermal performance. One of such parameters is the fracture aperture of the system. In this study, we built a spatio-temporal database from 4000 numerical simulation runs in order to train, validate, and test the data for successful prediction of the thermal performance. The dataset collected and utilized in this work comprises 4000 images of the fracture aperture and the related temperature profiles, with each profile including 20 temperature discrete points measured at different times. Since both variables have continuous values, this may be classified as a regression problem. The temperature profile was used as the target variable in the regression model, while the fracture aperture was used as the input variable. The data was visualized using python libraries such as seaborn and line plots were made to create a pictorial representation of the input and output for easy understanding. The heatmap of the input which is the fracture aperture is shown in Figure 6, whereas Figure 7 shows a line plot of the temperature profile.
+
+
+Figure 6View largeDownload slideHeatmap plot of the input featureFigure 6View largeDownload slideHeatmap plot of the input feature Close modal
+
+
+Figure 7View largeDownload slideLine plot of the output featureFigure 7View largeDownload slideLine plot of the output feature Close modal
+
+
+Data Pre-processing
+
+
+In the construction of a neural network system, data preparation is crucial. Underlying data is difficult to break down and analyze. That is why it must be preprocessed before any information can be extracted from it. Data Splitting and Data Normalization were employed as preprocessing methods in building the different neural network models in this study.
+
+
+– Data Normalization
+
+
+Normalization is the process of translating the input data's numerical range to [0, 1] (or any other range) to give it a common scale, without distorting differences in the ranges of values. It promotes training fairness by preventing a high-valued input from drowning out a lower-valued but equally important input. Normalization is essential because the network training parameters can be set for a certain range of input data. This allows the training process to be applied to similar tasks. The purpose of normalization is to convert data to a scale that is comparable. By starting the training process for each feature on the same scale, data normalization can help improve training time. The effectiveness of any learning algorithm is heavily dependent on the normalization method (Nayak et al., 2014).
+
+
+To normalize all of the neural network's input variables, we employed the Min-Max Normalization approach. The input data was scaled to a range of [0,1] 0r [-1,1] in this technique. Using the formula, this technique changes the input value x of the attribute X to Xnorm in the range [min,max].
+
+
+Xstd=x−minXmaxX−minXXnorm= Xstd* (max−min) + min
+
+
+Where, Min,max = feature_rangeminX = Minimum feature valuemaxX = Maximum feature value
+
+
+The normalization technique discussed above was the same for both algorithms.
+
+
+– Data Splitting
+
+
+Data splitting is a standard method for model validation that divides the dataset into a training and testing set, with the training set being used to train the model and the testing set being used to evaluate its performance. The technique employed to split the data here is Random subsampling - the most used approach for data splitting, that is, randomly sample without replacement some rows of the dataset for testing and keep the rest for training (Joseph et al., 2022). The 80:20 ratio was applied to the 4000 datasets of collected data, which had 2500 input features and 20 output features. The 80:20 split draws its justification from the well-known Pareto principle, but that is again just a thumb-rule used by practitioners (Joseph et al., 2022). This means that 80 percent of the data was used for training and 20% for testing.
+
+
+Selecting the Model Architecture of the Different Algorithms
+
+
+There are several factors to consider when building neural network models. These factors include selecting the neural network architecture, as well as hyperparameters such as the learning rate, the number of nodes in each layer, the number of hidden layers, batch normalization, dropout, the type of weight initializer, and the type of activation function. The effectiveness of the perfect outcome is greatly influenced by the neural network architecture used to resolve the problem. For several reasons, the performance of a neural network is highly dependent on the architecture used. For instance, the design has a significant influence on the prediction made by the neural network. Indeed, various architectures of neural networks can yield varied outputs for the same input (Massimiliano Lupo et al., 2021).
+
+
+Activation Function
+
+
+The Activation Function helps the neural network in utilizing important data while reducing irrelevant data points. The activation function determines whether or not a neuron should be stimulated by generating a weighted sum and then adding bias to it. Each activation function adds nonlinearity to the neural network, increasing its representation potential. There are several activation functions including Sigmoid, Tanh, Linear, and ReLU activation functions. One of the commonly used activation functions is the Rectified linear activation function or ReLU for short. The ReLU activation function was selected because it is easy to train and typically results in better performance. It has a derivative function and supports backpropagation while also being fast and accurate. The ReLU function does not simultaneously stimulate all of the neurons. It is a simple computation that returns the value specified as input directly, or 0.0 if the input is 0.0 or less. This indicates that if the input is positive, it will be output directly; otherwise, it will be output as zero. Figure 8 is a graphical representation of the ReLU activation function. The ReLU is given by (He et al., 2018).
+
+
+Figure 8View largeDownload slidePlot of the ReLU activation functionFigure 8View largeDownload slidePlot of the ReLU activation function Close modal
+
+
+ReLU(x)= max(x,0)
+
+
+Data Training procedure
+
+
+The machine learning models were trained using Python's Tensorflow and Keras deep network libraries. Pandas, a machine learning package, was used to read the data into the Python environment as a dataframe. A sequential model was used to build in layers for both the ANN and CNN model.
+
+
+For the ANN model development, differences in the number of hidden layers, the number of neurons in each hidden layer, and the number of iterations were attempted, preceded by model training to obtain the best weights for the input data parameters in order to test the network with the least amount of error and predict with better accuracy. Results showed that more than three hidden layers did not increase the network's performance for this particular training dataset, however increasing the number of hidden neurons in the networks did minimize the error and lead to better accuracy. Eventually, a network with three hidden layers and 512,128,64 neurons respectively supplied the optimal weights to the model's input data parameters. To find the ideal number of iterations for training the network, we examined the number of iterations deployed from 10 to 150.
+
+
+For the CNN, the network consists of four convolutional layers, four max pooling layers and three fully connected layers. A total of 240 filters, each of size 5 by 5 were deployed in the convolutional layer for the training. The fully connected layer consists of two hidden layers with 512 and 128 neurons respectively and output layers with 20 neurons which represents the number of expected output. Moreso, the input to each of the layers during training were standardized using the Batch Normalization technique for both algorithms. This not only handles the problems of internal covariate shift but also stabilizes the learning process and dramatically reduces the number of training epochs required to train deep networks.
+
+
+Compiling a model is required to finalize the model and make it fully functional. Compiling a model involves employing hyperparameters like optimizer and loss function to boost the performance of the model. There are various optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMS prop), Momentum-based gradient descent, Adaptive moment estimation (Adam), etc. To achieve this, Adaptive moment (Adam) and mean square error were used as optimizer and loss function for the development of the models. According to research, this Adam optimizer combines the strength of the momentum-based GD and the RMS prop while working with large sets of data and parameters, making it exceptionally efficient.
+
+
+The model was then trained by fitting it to the training data set after it had been compiled for both algorithms. However, the training data set was split into two groups: the first set was used to train the network and the second set was used to test for errors during the training. This cross-validation process was used to monitor the performance of the network.
+
+
+The model optimum performance was achieved with 150 and 100 Epochs for the ANN and CNN respectively.
+
+
+The models from both techniques were introduced to the test data set to assess model performance, and metrics like mean squared error and coefficient of determination were used to evaluate model performance. Table 1 shows the hyperparameters used for both algorithms.
+
+
+Table 1Hyperparameters of the two deep learning algorithms Hyperparameters
+            . CNN
+            . ANN
+            . Convolutional layers 4 __ Pooling Layers 4 __ Number of hidden layers 2 3 Activation function ReLU ReLU Weight Initializer He Initializer He Initializer Batch Normalization ✔ ✔ Dropout __ 0.1 Optimizer Adam Adam Validation split 0.2 0.2 Number of epochs 100 150 Batch size __ 128 Metrics Mean Square Error Mean Square Error Hyperparameters
+            . CNN
+            . ANN
+            . Convolutional layers 4 __ Pooling Layers 4 __ Number of hidden layers 2 3 Activation function ReLU ReLU Weight Initializer He Initializer He Initializer Batch Normalization ✔ ✔ Dropout __ 0.1 Optimizer Adam Adam Validation split 0.2 0.2 Number of epochs 100 150 Batch size __ 128 Metrics Mean Square Error Mean Square Error View Large
+
+
+Model Evaluation
+
+
+Careful statistical analysis was carried out on the predicted data set for the developed model. The performances of the models were evaluated based on the overall mean squared error (MSE) and coefficient of determination(R2).
+
+
+Mean Square Error (MSE) is the standard deviations of the residuals (prediction errors). It is technically defined as the average of the square of the difference between the actual and the predicted values. MSE is mathematically defined as:
+
+
+MSE=1n∑i=1nEi2
+
+
+Where E = Error
+
+
+E = (ŷ) -y), where (ŷ) is the predictions and y is the actual value.
+
+
+n = is the number of samples.
+
+
+Coefficient of determination (R2), measures the correlation between the model output and the target. It is measured on a scale of 0 to 1. Usually, the closer to unity (1) the R2, the higher the correlation between predicted and actual values.
+
+
+Mathematically:
+
+
+R2=1 −SSresSStotal    SSres∑i=1n(y−y_)2    SStotal∑i=1nEi2
+
+
+Where, SSres is the sum of squares of the residual error
+
+
+SStotal is the total sum of squares
+
+
+Results and Discussion
+
+
+Results and Discussion on the Artificial Neural Network (ANN)
+
+
+A neural network is used as a regressor in this study to predict the temperature profile of a geothermal reservoir. The nature of the fracture aperture has been demonstrated to have a significant impact on the temperature profile. The fracture aperture was fed into the ANN model that was developed with the temperature profile as the target. We used the supervised machine learning approach and an artificial neural network in this study.
+
+
+In this study, a three-hidden layer feed forward neural network of 512,128,64 hidden nodes in the hidden layers was generated utilizing the trial-and-error approach. Table 2 displays the mean square error and coefficient of determination results for the training and testing sets. Using an 80/20 split, the samples were divided into training and testing sets. Appropriate hyperparameters were selected that had a significant influence on the model's performance during training. The results revealed that the ANN model had satisfactory MSE and R2 values. Table 4 shows the comparison between the two models. Figure 9 depicts a cross plot of actual versus predicted network performance.
+
+
+Table 2Model Evaluation performance 
+            . Training
+            . Testing
+            . MSE 1.7610 3.8701 R2 0.94 0.92 
+            . Training
+            . Testing
+            . MSE 1.7610 3.8701 R2 0.94 0.92 View Large
+
+
+Table 3Model Evaluation performance 
+            . Training
+            . Testing
+            . MSE 1.46 2.67 R2 0.96 0.93 
+            . Training
+            . Testing
+            . MSE 1.46 2.67 R2 0.96 0.93 View Large
+
+
+Table 4Model Comparison 
+            . CNN
+            . ANN
+            . Train MSE 1.46 1.76 Test MSE 2.67 3.87 Train R2 0.96 0.94 Test R2 0.93 0.92 Time taken 50 mins 10 mins Total number of parameters 418,244 3,167,956 
+            . CNN
+            . ANN
+            . Train MSE 1.46 1.76 Test MSE 2.67 3.87 Train R2 0.96 0.94 Test R2 0.93 0.92 Time taken 50 mins 10 mins Total number of parameters 418,244 3,167,956 View Large
+
+
+Figure 9View largeDownload slideCross plot of Actual vs Predicted values for the Artificial Neural NetworkFigure 9View largeDownload slideCross plot of Actual vs Predicted values for the Artificial Neural Network Close modal
+
+
+Results and Discussion on the Convolutional Neural Network (CNN)
+
+
+Exploratory data is a critical step in data analysis, which involves the use of visual techniques in analyzing data for better understanding. It is used to discover trends, patterns or to check assumptions with the help of statistical summary and graphical representation.
+
+
+Due to the nature of the data set used for this research, mostly univariate analysis was performed. In univariate analysis, only one quantity that changes is considered. It does not deal with causes or relationships and the main purpose of the analysis is to describe the data and find patterns that exist within it.
+
+
+There were three basic questions about the data asked before starting the data analysis. The first was related to whether the data is discrete or continuous. The second question is related to the upper and lower boundaries of the data and the final question determines the likelihood of observing extreme values in the distribution. The data used in his study is continuous with values in a finite interval. The issues of the upper and lower boundaries were handled using the minmax scaling preprocessing.
+
+
+The continuous variable (regression) model was developed in this study using CNN. The model was developed to be used to predict the thermal performance of a selected geothermal system. The model was tested using the test data set to see how well it is performing.
+
+
+Figure 10 is a cross plot of actual and predicted data which shows the network performance.
+
+
+Figure 10View largeDownload slidePlot of performance of CNN Model Showing Cross plot of Actual Values vs Predicted Values of the Temperature Profile.Figure 10View largeDownload slidePlot of performance of CNN Model Showing Cross plot of Actual Values vs Predicted Values of the Temperature Profile. Close modal
+
+
+The model performance using the evaluation metrics of mean squared error and coefficient of determination for the training and test data set are shown in Table 3 below.
+
+
+Implications for modeling oil and gas reservoirs
+
+
+The numerical reservoir model used to generate the data for this study took one month of computational time for the 4000 datasets. From this study, we see that both the CNN and the ANN took less than one hour to run. This shows that the proxy model saves computational time while providing a means for accurate forecasting of thermal performance. A similar approach can be used for oil and gas reservoir modeling. However, from this study, we see that using a convolutional neural network would be an overkill for oil and gas reservoir modeling since the computational time is large and oil and gas reservoir models do not typically have images as input data. We however anticipate that deep learning algorithms such as recurrent neural networks that account for the temporal data in reservoir models, will be suitable for proxy modeling and building on the architecture of the ANN.
+
+
+Conclusion
+
+
+Neural networks have been demonstrated to save computational time in building subsurface models for geothermal temperature prediction. From the comparison between the CNN and ANN using metrics of MSE and R2, it can be concluded that the CNN algorithm was slightly better than the ANN for this image problem. However, the larger coefficient of regression and lower mean square error of the CNN was overshadowed by the long amount of time it took to run the CNN algorithm. Thus, the ANN became the preferred proxy model for the geothermal reservoir model.
+
+
+Recommendations
+
+
+Based on the limitations and areas of improvement observed from this research, the following recommendations will improve the model’s performance:
+
+
+Inclusion of other variables that directly influence thermal performance of the geothermal systems for better predictions.The use of more data to train the models to improve accuracy.Other deep learning techniques, such as recurrent neural networks (RNNs), can be used for further research.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Nayak, S. C., Misra, B. B., and Behera, H. S., 2014. Impact of Data Normalization on Stock Index Forecasting. International Journal of Computer Information Systems and Industrial Management Applications., 6, 257–269.Google Scholar Bahr, B. A., Adewale, G., Shadi, W.H., 2019. Current trends and future developments in (Bio-) Membranes. https://doi.org/10.1016/C2016-0-02118-6.Google Scholar Juncai, H., Lin, L., Jinchao, X., Chunyue, Z., 2018. ReLU Deep Neural Networks and Linear Finite Elements. J. comput. Math., 38(3), 502–527. http://doi.org/10.4208/jcm.1901-m2018-0160.Google Scholar Massimiliano, L. P., Junqi, Y., Ying, W.L., Markus, E., 2021. A scalable algorithm for the optimization of neural network architectures. arXiv:1909.03306v3.Google Scholar Reynold Chu, S., Shoureshi, R., Tenorio, M., 2018. Neural Networks for Systems Identification. IEEE controls system magazines., 10(3), 31–35. https://doi.org/10.1109/37.55121.Google Scholar Ahmed, K. A., Haidar, A., Hayder, A., Jawad, D., 2019. Application of Machine Learning Approach for Intelligent Prediction for Pipe Sticking. Presented at theAbu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, 11-14 November. https://doi.org/10.2118/197396-ms.Google Scholar Brown, S. R. "Fluid flow through rock joints: the effect of surface roughness". J Geophys Res Solid Earth. (1987); 92(B2): 1337–1347Google ScholarCrossrefSearch ADS  Hakami, E. and Larsson, E. "Aperture measurements and flow experiments on a single natural fracture". Int J Rock Mech Min Sci GeomechAbstr. (1996); 33(4):395–404.Google ScholarCrossrefSearch ADS  Onay, M. E. (2020). Analytical Solutions for Predicting Fracture Outlet Temperature of Produced Fluid from Enhanced Geothermal Systems with Different Well-Completion Configurations. Graduate School, The University of Tulsa, Tulsa, OKGoogle ScholarCrossrefSearch ADS  NREL, 2021. U. S. Geothermal Power Production and District Heating Market Report, 2021. National Renewable Energy Laboratory. Retrieved from. https://www.nrel.gov/news/press/2021/new-nrel-report-details-current-state-vast-future-potentialus-geothermal-power-heat.html.Okoroafor, E. R.; RolandN. H., 2018. Impact of Fracture Roughness on the Thermal Performance of Enhanced Geothermal Reservoir.Google Scholar Roshan, V. J., 2022. Optimal ratio for Data splitting. Milton Stewart School of Industrial and Systems Engineering. arXiv:2202.03326v1.Google Scholar Pandey, S. N., Singh, M., 2020. Artificial Neural Network to Predict the Thermal Drawdown of Enhanced Geothermal Systems. ASME. J. Energy Resour. Technol. January2021; 143(1): 010901. https://doi.org/10.1115/1.4048067.Google ScholarCrossrefSearch ADS  Gupta, H. K., SukantaR., 2007. Geothermal Energy: An Alternative Resource for the 21st Century Elsevier.Google Scholar Okoroafor, E. R.; Connor M.Smith, KarenIfeoma O., ChineduJoseph N., Halldora-Gudmundsdottir, Mohammad Aljubran, 2022. Machine learning in subsurface geothermal energy: Two decades in review.: www.elsevier.com/locate/geothermics.Google Scholar TIBCOhttps://www.tibco.com/reference-center/what-is-a-neural-network
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212028-MS
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+

files/2022/Assessment of Nigerias Role in the Global Energy Transition d Maintaining Economic Stability.txt

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+----- METADATA START -----
+Title: Assessment of Nigeria's Role in the Global Energy Transition d Maintaining Economic Stability
+Authors: Itoro Koffi, Israel Bassey
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211959-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Over the years, immediate action has been required to prevent climate change effects through clean energy. However, this step represents a threat of existence to third-world countries such as Nigeria, which relies heavily on royalties and tax revenues from oil and gas reserves. The Nigerian government is a signatory to the Paris Agreement, but as part of that decarbonization project and the transition to net-zero, issues of gas come up, and we talk of just and equitable transition. It is thus important to consider the various realities of developing economies.This paper discussed Nigeria's role in a fair and balanced global energy transition towards achieving net-zero by 2050, without jeopardizing the lives of millions. In this study, the prospects, and challenges of using natural gas as a driver of sustainability and energy transition to leverage the massive gas potential across the country is also presented to build an economy that can support a sustainable energy future.
+
+
+
+
+Keywords:
+upstream oil & gas,
+sustainable development,
+africa,
+sustainability,
+climate change,
+poverty,
+government,
+social responsibility,
+transition,
+investment
+
+
+Subjects: 
+Environment,
+Sustainability/Social Responsibility,
+Climate change,
+Sustainable development
+
+
+
+
+Introduction
+
+
+The Sustainable Development Goals (SDGs), which were approved in 2016, recognize the importance of economic development in a strategic way based on the principle of "leaving no one behind" and ensuring everyone has a chance to economic and environmental stability. The seventh goal (Ensure access to affordable, reliable, sustainable, and modern energy for all) acknowledges the importance of affordable, reliable, sustainable, and modern energy for all (United Nations, 2016).
+
+
+Transitioning to lower emission sources of energy is essential in combating global warming and its implications, which is why over 190 countries collectively signed to the Paris Agreement in COP21 to work together to reduce the global average recorded temperature to under 2°C. Countries want to accomplish global capping of greenhouse gas emissions as soon as feasible to produce a climate neutral world by 2040 in order to meet this long-term temperature objective. (UNFCCC, 2022).
+
+
+However, there is need to understand that for developing nations such as Nigeria, with an economy which relies largely on hydrocarbon, a reasonable and equitable method to energy transition is essential so that no country is left behind in the transition to cleaner energy.
+
+
+To make the most of Nigeria's vast oil reserves, energy experts say the government should analyze strategic policies that will significantly improve its exploration and development interest through the growth of the value chain and promotion of alternative energy, thereby hastening its effort to recover its reserves indefinitely and make the most out of what it had before the green era. During COP26 in Glasgow, President Buhari stated that "Nigeria is more of a gas-producing nation than an oil-producing one, and it is seeking funding for projects that use transition fuels like gas.". The President also acknowledged the potential for Nigeria to properly explore and use gas till 2040 without jeopardizing its Paris Agreement obligations. (Ashurt, 2022).
+
+
+The broad ramifications of the shifting global energy transition for Nigeria are discussed in this research work. Nigeria's opportunities and challenges in integrating its energy transition strategy initiatives are examined, and recommendations are suggested to strike a balance between the development of alternative energy sources and the continuous expansion of the country's petroleum sector, both of which are vital to the growth of the economy.
+
+
+An Energy Poor Continent in Africa
+
+
+Despite having nearly, a fifth of the world's population, Africa utilizes less than 4% of global energy. The continent suffers from energy poverty, or a lack of access to modern energy services, despite its natural abundance of fossil resources and renewable energy sources. (Chong, 2021).
+
+
+Despite considerable growth of centralized electrical networks, energy poverty is one of the worldwide concerns of this century. Energy poverty can be eradicated by implementing Goal 7 of the Sustainable Development Goals (SDGs), which aims to achieve sustainable development by 2030 by providing inexpensive and universal power access, (Bhattacharyya, 2016). When expressed from a non-income dimension, energy poverty can be expressed by two indicators: shortage of power and a reliance on traditional cooking fuels (Njiru and Letema, 2018).
+
+
+A number of residents across the African continent still rely on charcoal and firewood for cooking purposes. According to World Economic Forum, worldwide, more than one billion people do not have any access to sustainable energy, and a vast majority of that population is found on the African continent. Low digitalization rates, dependency on imported fossil fuels, inefficient transmission, frequent power failures, high rural electrification costs, demand for energy surpassing generating capacity, and utility companies' lack of access to modern infrastructure and grids are all contributing factors. (Aliyu and Adam, 2017). The global COVID-19 pandemic has also played a role in inflation and supply chain disruption which has led to significant decline in the stock of capital investments that would enhance and improve the energy infrastructure in the world.
+
+
+The International Energy Agency (IEA) in Figure 1 depicts the proportion of Africa's population with access to electricity (IEA). Statistics from figure 1 demonstrate that, excluding South Africa and some portions of Northern Africa, the remaining one billion people in Sub-Saharan Africa are served by a power producing capacity of just 81 gigahertz, contributing less than 1% of total emissions.
+
+
+Figure 1View largeDownload slideFigure Showing the Electricity Availability by Countries in Africa. (IEA, 2019)Figure 1View largeDownload slideFigure Showing the Electricity Availability by Countries in Africa. (IEA, 2019) Close modal
+
+
+Most countries on the African continent are low emission, energy-poor countries with per capita emissions of somewhere in the order of 0.8 to 1%, with an average of under two tons per capita if South Africa and northern Africa counties are included (Adeleye et al., 2021). This proves that even though the electricity consumption is tripled across sub-Saharan solely through natural gas, only about 0.6% will be added to Nigeria's gas industry global emissions as shown in Table 1. These numbers are very minute in comparison to the western world, where the United States which has an installed capacity of over 1200 gigahertz of power and with a population of over 331 million people, its emission stands at 15.5% per capita.
+
+
+Table 1Average energy consumption per capita for selected countries (EIA, 2019) Rank
+            . Country
+            . Kwh/h/year
+            . Kwh/h
+            . 1 China 5,885 671 2 United States 12,154 1,387 3 India 935 107 4 Russia 6,685 763 5 Japan 7,150 816 6 Brazil 2,830 323 7 Canada 14,612 1,667 8 South Korea 10,192 1,163 9 Germany 6,306 719 10 France 6,702 765 21 South Africa 3,591 410 68 Nigeria 144 16 Rank
+            . Country
+            . Kwh/h/year
+            . Kwh/h
+            . 1 China 5,885 671 2 United States 12,154 1,387 3 India 935 107 4 Russia 6,685 763 5 Japan 7,150 816 6 Brazil 2,830 323 7 Canada 14,612 1,667 8 South Korea 10,192 1,163 9 Germany 6,306 719 10 France 6,702 765 21 South Africa 3,591 410 68 Nigeria 144 16 View Large
+
+
+More than half of Africa's population is compelled to depend on biomass for cooking and energy, such as charcoal and firewood, an inefficient and ecologically damaging alternative, due to lack of access to clean energy sources. The inefficiency of the energy industry and power outages cost the continent over four percent of Gross Domestic Profit annually, hurting sustainable economic development, employment, and investment. These energy shortfalls deepen poverty, particularly for people in rural regions, which leads to the poorest people paying among the world's highest rates for electricity.
+
+
+Nigeria's Role in the Global Energy Transition
+
+
+Revenues from the petroleum industry today account for more than 80% of Nigeria's GDP. (Statistica, 2021). The nation is blessed with massive oil wealth as well as physical and human resources, which places it in a great position to combat energy poverty. From a historical viewpoint, bad management and execution have impeded attempts to accomplish this. The economy is currently struggling to leverage the country's massive fossil fuel deposit to alleviate the country's energy poverty. The strategy to alleviate this problem is to divert revenue generated from fossil fuel energy sources such as natural gas over the next two decades to greener alternative investments that encourage the development of new technologies from environmentally friendly sources.
+
+
+In 2017, Nigeria ranked 9th in the world with proven gas reserves of over 187 trillion cubic feet (Tcf), accounting for roughly three percent of the world's total natural gas reserves of 6,923 Tcf. Nigeria has 306.3 times its annual consumption rate in proved reserves. (Worldometer, 2019). Gas is a high demand fuel for electricity grid supply, road transport, generation in urban regions, ship bunkering fuel, blue hydrogen production, etc. Figure 2 represents the world energy consumption prediction for 2040 by International Energy Agency (IEA). Global energy consumption will climb approximately 50 percent over the next 30 years, and although petroleum will remain the major energy source, natural gas use will expand to virtually the same level by substantial percentages.
+
+
+Figure 2View largeDownload slideWorld energy consumption by energy source (1990-2040) (IEA, 2017).Figure 2View largeDownload slideWorld energy consumption by energy source (1990-2040) (IEA, 2017). Close modal
+
+
+The National Gas Policy (NGP) established in 2017 was created with the purpose of transforming the nation from an oil dependent economy to a predominantly gas-based economy. The concepts of the NGP include generating greater value from Nigeria Liquified Natural Gas (NLNG), taking a project-based strategy to domestic gas development as opposed to current approach, and forging strong ties between the power, agriculture, transportation, and industrial sectors of the economy. The NGP provides a thorough outline of the government's policy goals and the steps it plans to take to achieve them. Reforms in the gas sector's governance (legislation and regulation), enforcement of domestic supply obligations, fiscal provisions, reworking the industry structure with a focus on partnerships within the public and private sector, providing incentives and opportunities for investing in natural gas products like Natural Gas Liquids (NGL) and Liquefied Petroleum Gas (LPG), developing gas infrastructure, and building markets are some of the frameworks in place to implement these objectives. (Aelek, 2021).
+
+
+Figure 3 represents the renewable energy investments in the African continent from 2000 to 2020. Of the 59.5 billion dollars invested, West Africa's contribution is less than 8%. Nigeria therefore must do more in ensuring it meets its target as the global energy transition currently poses a threat to her economy with local and international organizations already pulling out of funding fossil fuel projects.
+
+
+Figure 3View largeDownload slideRenewable Energy Investment in Africa (Ajala, 2021)Figure 3View largeDownload slideRenewable Energy Investment in Africa (Ajala, 2021) Close modal
+
+
+World Bank, European Investment Bank, African Development Bank (AFDB), East African Development Bank are some of the organizations to sign the pledge to discontinue public financing of fossil fuel projects.
+
+
+There is also need for the diversification of the Nigerian economy to promote competitiveness in other sectors and provide an opportunity for Nigeria to maximally utilize her vast natural resources, while making the most of the abundant resource base to rebuild the economy and reap a variety of benefits, such as building human capital, linkages and synergy, exploiting new opportunities, economies of scale, growth in national technology and foreign investment profile, lowering average operational costs, increasing national competitiveness, and increasing citizens' confidence in the country's renaissance ( Ite, 2020)
+
+
+A Just and Equitable Transition
+
+
+Nigeria has significant petroleum reserves, expected to be 37 billion barrels of crude oil and 209.5 trillion cubic feet of natural gas as of January 2022, placing the nation in an enviable position among global oil and gas producers. Nigeria is presently racing against time to obtain maximum value from its hydrocarbon endowment and guarantee its energy future. Even at the moment of global energy transition, Nigeria's oil and gas industry remains essential to the country's economy since it provides the necessary cash flow for other sectors to function. Nigeria is presently racing against the clock to enhance the utilization of its hydrocarbon assets and secure its energy future by considering energy strategies, which is a key tool for national development and economic liberation, while taking into account the shift in demand and clamour for cleaner fuels.
+
+
+Implementation of low-carbon technologies across the petroleum industry value chain, as well as the deepening and penetration of natural gas usage locally to boost energy sufficiency and reduce energy poverty, and prudent investment in cleaner fuels and renewable sources, are all critical strategies for ensuring a just and equitable transition. Countries from around the world are developing innovative strategies and implementing enabling policy to attract the integration of substantial quantities of green and affordable alternative energy sources. However, as shown in Figure 4, there is currently no suitable or dependable solution for addressing Nigeria's and the broader sub-Sahara Africa region's massive energy deficit.
+
+
+Figure 4View largeDownload slideNigeria Electricity Access Rate by State (Adewuyi et al., 2020)Figure 4View largeDownload slideNigeria Electricity Access Rate by State (Adewuyi et al., 2020) Close modal
+
+
+By encouraging industry participants through regulatory service instruments, the government must be pledged to creating the right environment for investment opportunities in the midstream and downstream sectors of the industry. President Buhari in 2021 announced "The Decade of Gas" campaign, an initiative designed to ensure Africa's biggest oil producer can take advantage of the global energy transition. The launch comes just as the government was pushing through some major reforms for the sector's Petroleum Industry Bill (PIB)
+
+
+Development of the country's gas will allow for the generation of more revenue and produce sufficient electricity to put an end to power shortages and blackouts. It would also assure the supply of gas to key industrial facilities and manufacture of enough cooking gas, LPG, to ensure households do not have to depend on polluting sources of energy for their daily activities such as wood and charcoal fires, which are dangerous to the environment and prone to explosion and causing harm to individuals.
+
+
+Conclusion
+
+
+Nigeria's energy transition strategy will require the country to continue to develop its large gas reserves and resources while also advancing the adoption of green energy, especially solar, at a community level, such as the establishment of micro-grids. The ongoing divestment has resulted in the emergence of indigenous companies in the country playing a major role in exploration and production activities, but on the other hand, reluctance of IOCs to make further investments in the sector has resulted in the repatriation of funds out of Nigeria. In regard to the conversion of Nigeria from a hydrocarbon-dependent economy to a diverse sector that can both meet the requirements of its growing population and reach the net-zero targets set at COP26, the role of gas as a transition fuel cannot be overlooked.
+
+
+The next generation of engineering talent needs to utilise digital tools and technologies alongside existing knowledge of the industry to understand how to take advantage of the emerging job sector in the transition. The energy sector overall requires real world, foundational engineering, involving sensors, data interpretation, and machine learning to achieve the kind of efficiencies that the market needs, while Nigeria continues to consolidate its resources to guarantee energy security.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion SDGSustainable Development Goals UNFCCCUnited Nations Framework Convention on Climate Change IEAInternational Energy Agency TCFTrillion Cubic Feet NGPNational Gas Policy NGLNatural Gas Liquids NLNGNigeria Liquified Natural Gas LPGLiquified Petroleum Gas AFDBAfrican Development Bank PIBPetroleum Industry Bill KWh/h/yearKilowatt-hour per head per year KWh/hKilowatt-hour per head
+
+
+References
+
+
+Adeleye, B., Osabohien, R., Lawal, A. and Alwis, T.2021. Energy use and the role of per capita income on carbon emissions in African countries. PLOS ONE, 16(11): pp. 25–37. https://doi.org/10.1371/journal.pone.0259488.Google ScholarCrossrefSearch ADS  Adewuyi, O., Kiptoo, M., Afolayan, A., Amara, T., Alawode, O. and Senjyu, T.2020. Challenges and prospects of Nigeria's sustainable energy transition with lessons from other countries experiences. Energy Reports. 6(20): pp 993–1009. https://doi.org/10.1016/j.egyr.2020.04.022.Google Scholar Aelek. 2021. A Quick Overview of the New National Gas Policy. Available at: https://www.aelex.com/a-quick-overview-of-the-new-national-gas-policy. Accessed 28 March 2022.Ajala, S.2021. On Nigeria's Clean Energy Plans and Improved Energy Access by 2030. Dataphyte. Available at: https://www.dataphyte.com/latest-reports/climate/on-nigerias-clean-energy-plans-and-improved-energy-access-by-2030. Accessed 3 February 2022.Google Scholar AkinloyeB., OshevireP. and EpemuA.2016. Evaluation of system collapse incidences on the Nigeria power system. Multidiscip J. Eng. Sci. Technol. 3 (1): pp. 3707–3711.Google Scholar AliyuA., DadaJ. and AdamI.2017. Current status and future prospects of renewable energy in Nigeria. Renewables and Sustainable Energy Reviews. 48 (01): pp. 336–346.Google Scholar Chong, L., 2021. Addressing Energy Poverty in Africa - The Borgen Project. The Borgen Project. Available at: https://borgenproject.org/energy-poverty-in-africa. Accessed 8 April 2022.Google Scholar EIA, 2019. List of countries by electricity consumption. Available at: https://www.eia.gov/international/data/world/electricity/electricity-consumption. Accessed 9 April 2022,EmodiN.V. and EbeleN.E., 2016. Policies enhancing renewable energy development and implications for Nigeria. Sustain. Energy. 4 (1): pp. 7–16.Google Scholar GiwaS.O., NwaokochaC.N. and OdufuwaB.O., 2017. Mitigating gas flare and emission footprints via the implementation of natural gas vehicles in Nigeria. Energy Policy, 111 (01): pp. 193–203, https://doi.org/10.1016/j.enpol.2017.09.027.Google Scholar Kende-Robb, C., 2016. Africa's energy poverty is keeping its people poor. World Economic Forum. Available at: https://www.weforum.org/agenda/2016/09/africa-s-energy-poverty-is-keeping-its-people-poor. Accessed 15 March 2022.Google Scholar Ashurst, 2022. Nigeria's Energy Transition. Available at: https://www.ashurst.com/en/news-and-insights/legal-updates/nigerias-energy-transition. Accessed 4 February 2022.Bhattacharyya, S. and Palit, D., 2016. Mini-grid based off-grid electrification to enhance electricity access in developing countries: What policies may be required. Energy Policy. 94(02): 166–178. http://dx.doi.org/10.1016/j.enpol.2016.04.010.Google Scholar BrimmoA., SodiqA., SofelaS. and KoloI.2017. Sustainable energy development in Nigeria: Wind, hydropower, geothermal and nuclear. Renewables and Sustainable Energy Reviews. 74 (01), pp. 474–490.Google Scholar Njiru, C. and Letema, S., 2018. Energy Poverty and Its Implication on Standard of Living in Kirinyaga, Kenya. Journal of Energy, 18(19): pp 1–12. https://doi.org/10.1155/2018/3196567Google Scholar Statista. 2021. Nigeria: contribution oil sector to GDP 2018-2021. Available at: https://www.statista.com/statistics/1165865/contribution-of-oil-sector-to-gdp-in-nigeria. Accessed 8 April 2022UNFCC, 2022. The Paris Agreement. Available at: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement. Accessed 12 March 2022.United Nations, 2016. Sustainable Development Goals. Available at: https://www.un.org/sustainabledevelopment/energy. Accessed 10 April 2022.Uwem. I.. Stranded Nation with Stranded Resources: Sustainability Imperatives for Nigeria. Paper presented at the SPE Nigeria Annual International Conference and Exhibition. https://doi.org/10.2118/203631-MSWorldometers, 2022. Nigeria Natural Gas Reserves, Production and Consumption Statistics. Available at: https://www.worldometers.info/gas/nigeria-natural-gas. Accessed 16 March 2022.
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211959-MS
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files/2022/Assessment of the Prospect and Challenges of the African Oil and Gas Industry in Harnessing Energy for a More Sustainable World.txt

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+----- METADATA START -----
+Title: Assessment of the Prospect and Challenges of the African Oil and Gas Industry in Harnessing Energy for a More Sustainable World
+Authors: Jesujoba Olubodun
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211937-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+In 2015, the United Nations General Assembly set up the Sustainable Development Goals as a follow up on the Millennium Development Goals and a masterplan to attain a better and more sustainable world by the year 2030. One of these goals (SDG 7), seeks to achieve affordable and clean energy for all the world's population by the year 2030. As a result, global efforts are being made to reduce greenhouse gas (GHG) emissions to limit pollution as well as enhance the development of renewable energies such as solar, wind, hydrothermal amongst others into the worlds energy system.The oil and gas industry has played a vital role in meeting the world's energy demand to date. 60% of world energy consumption was supplied by the oil and gas industry for year 2020 and as this demand keeps rising, the industry will continue to play this vital role in powering and enabling industries. Therefore, even as the world clamours for a shift from a world powered by fossil fuels to one sustained by green energy, the success of this global energy transition would still be heavily dependent on the drivers and players of fossil fuel technology. As such, there is a need for the African oil and gas industry to realize that although it may no longer be business as usual, this shift presents an opportunity for the industry to contribute to the emerging energy mix as well as correct negative perceptions the general public might have of the industry. To remain relevant, companies must adapt, invest in renewable energy research and development and build on existing technology.This paper explores and gives insight into the ways oil and gas companies have begun harnessing renewable energy in their operations, challenges being faced to reduce carbon emissions to achieve net zero and how hydrocarbon production operations can be done in a more environmentally safe manner. To strengthen this cause, stakeholders, policy makers and engineers in the African clime must investigate the dynamics, parallels and interdependency between conventional and renewable energy, to be strategically positioned to play a key role in the transition to a more sustainable future.
+
+
+
+
+Keywords:
+sustainability,
+climate change,
+air emission,
+challenge,
+world commission,
+social responsibility,
+harnessing energy,
+african oil,
+gas industry,
+sustainable world
+
+
+Subjects: 
+Environment,
+Sustainability/Social Responsibility,
+Air emissions,
+Climate change,
+Sustainable development
+
+
+
+
+Introduction
+
+
+The oil and gas industry over the years has acted as an essential player role in powering various industries- The pharmaceutical and healthcare industries, automobiles, helping to build industrial cities. Many of the medical equipment used today, many of which are life-saving devices, are made from oil. Not only are heart valves and artificial limbs made from petroleum, but also many of the cleaning and safety products medical personnel use. Plastics, petrochemicals, transportation industries are all beneficiaries of fossil fuel. Figure 1 below shows the Organization for Economic Co-operation and Development (OECD) assessment of the distribution of oil demand and consumption.
+
+
+Figure 1View largeDownload slideThe graph above shows how various sectors needed for productivity have demand for oil. Transportation by road leading the way with petrochemicals a close secondFigure 1View largeDownload slideThe graph above shows how various sectors needed for productivity have demand for oil. Transportation by road leading the way with petrochemicals a close second Close modal
+
+
+However, with growing concerns on environmental damages (atmosphere, water and land), there has been no small demand for climate activists and environmentalist on the pollution caused by oil and gas industry in Nigeria, Africa and the world.
+
+
+Figure 2 above shows the spread and concentration of CO2 emissions caused by the burning of fossil fuel for energy production. In Africa, countries of high intensity CO2 emission include Egypt, South Africa and Nigeria.
+
+
+Figure 2View largeDownload slideFigure 2View largeDownload slide Close modal
+
+
+What does this mean for the industry? extinction, reduced production or efficient harmonization and transitioning?
+
+
+According to BP's 2018 Energy Outlook, renewable energy will be the fastest-growing source of energy, increasing five-fold by 2040 thus providing around 14% of global primary energy at this future point in time. Similarly, oil majors are gradually facing potential prospects as a declining industry: while peak demand for oil has not yet occurred so far, it may be expected that this scenario is indeed approaching as oil demand growth slows and eventually peaks.
+
+
+Considering this, the oil industry is confronted with the question of whether it should try and at least partially reinvent itself as renewables businesses. The rising cost of hydrocarbon extraction may create an incentive to consider accelerating the energy transition away from hydrocarbons toward progressively more affordable renewable sources. Following COP21, more than 170 countries agreed to try limiting global warming to well below two degrees Celsius, an effort that will require major investments in low-carbon energy sources.
+
+
+However, the current business models of oil majors and renewable companies are distinctively different, and the oil industry is likely to have, for example, a different cost of capital to the renewables sector. Most renewable ventures, like wind and solar projects, churn out cash flows akin to annuities for several years after initial up-front capital expenditure generally with low price risk quite different from the business models of oil majors that face oil price risk. However, one could argue that with an increasing share of intermittent renewables, the power business is becoming more akin to the oil industry requiring a trader's skillset to manage increasing volatility and provides a hedge in a future low carbon environment.
+
+
+A More Sustainable World
+
+
+Sustainable development has been in global discussions in recent years. The term sustainability itself has different definitions. However, a most accurate one is that by the 1987 Brundtland Commission (formerly referred to as the World Commission on Environment and Development), which defines sustainability as
+
+
+"Meeting the needs of today without compromising the ability of future generations to meet their own needs" (WCED,1987).
+
+
+Given the challenges stated in production as well as usage of hydrocarbon, some think it ironic, even almost impossible for the oil and gas sector to not only contribute to the sustainability agenda but to do so immensely and efficiently. The oil and gas industry has always been one of adaptation, dynamism and resilience.
+
+
+Hence, the data trend shows that
+
+
+Figure 3View largeDownload slideSUSTAINABLE DEVELOPMENT PILLARSFigure 3View largeDownload slideSUSTAINABLE DEVELOPMENT PILLARS Close modal
+
+
+Renewable Energy: A Driver for Sustainability
+
+
+Renewable energy, also called clean energy refers to energies whose sources are obtained via energy sources or processes which are constantly replenished. Examples include solar, wind, biofuels and geothermal energy.
+
+
+Renewable energy and renewables have always been in the picture, however, this has not always been on a large scale comparable to the demand, use and consumption of fossil fuel sources. Yet, as technology becomes increasingly innovative and the demand for a less polluted world amplifies, renewable energy sources have come to the fore as a potential large share contributor to the world's energy mix. (Source)
+
+
+Considering a number of renewable energy sources
+
+
+Figure 4View largeDownload slideFigure 4View largeDownload slide Close modal
+
+
+Hydropower
+
+
+Hydropower also referred to as hydroelectric power, refers to the Potential energy of water which has been trapped in dams. It also makes uses of energy obtained from tides caused by gravity effects Hydropower, otherwise known as hydroelectric power, offers a number of advantages to the communities that they serve. Hydropower and pumped storage continue to play a crucial role in our fight against climate change by providing essential power, storage, and flexibility services.
+
+
+Wind Power
+
+
+Differential heating of earth's surface leads to the movement of air and is exploited with wind turbines.
+
+
+Solar
+
+
+Solar energy comes up as the third most harnessed form of renewable energy, it involves the use of solar panels to store and convert energy obtained from solar rays.
+
+
+Geothermal Energy
+
+
+Geothermal energy is a form of energy conversion in which heat energy is captured and harnessed from within the earth for domestic and industrial purposes. Mainly existing via three different channels;
+
+
+–Direct Use Applications: This involves the direct use of heated water from ground without employing specialized equipment. This involves use of low temperature geothermal water and steam to warm houses, pools, for cooking and certain industrial applications–Geothermal Heat Pumps: GHPs consists of a heat exchanger (a loop of pipes buried in the ground) and a pump. The heat exchanger transfers heat energy between the ground and air at the surface through fluid (water in most cases) that circulates through the pipes. In warmer periods, heat from warm air is transferred to the heat exchanger and into the fluid. The heat is then dispersed to the rocks, soil, and groundwater. The pump is reversed during the colder months. Heat energy stored in the relatively warm ground raises the temperature of the fluid. The fluid then transfers this energy to the heat pump, which warms the air inside buildings. They are very efficient, using 25–50 percent less electricity than comparable conventional heating and cooling systems, while producing less pollution. (44%).–Electric Power Generation : GE can also be used to generate electricity via geothermal power plants, in this process, excess water vapor at the end of each cycle is condensed and returned to the ground, where it is reheated for later use, making geothermal power considered a form of renewable energy
+
+
+View largeDownload slideGeothermal Power Generation.View largeDownload slideGeothermal Power Generation. Close modal
+
+
+The Oil & Gas-Renewable Energy Nexus
+
+
+To show the reality, that indeed a possibility exists of the fossil fuel industry harnessing renewable energy in operations. Oil giants have made promises to cut emissions by sourcing renewable electricity to power their oil and gas operations. However, the data (data source) shows there is still much more work to be done to improve the prospects of the oil and gas industry in driving sustainability.
+
+
+Figure 5View largeDownload slideShows the total CAPEX (capital expenditure) from selected top Oil and Gas firms with only one (Eni) having openly put out their CAPEX for selected year. However, the percentage of expenditure spent on renewables still need to be improved upon. To drive a system of sustainability, more transparency would be needed from these firms.Figure 5View largeDownload slideShows the total CAPEX (capital expenditure) from selected top Oil and Gas firms with only one (Eni) having openly put out their CAPEX for selected year. However, the percentage of expenditure spent on renewables still need to be improved upon. To drive a system of sustainability, more transparency would be needed from these firms. Close modal
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Carbon Capture Sequestration
+
+
+Carbon capture and sequestration/storage (CCS) is the process of capturing carbon dioxide (CO₂) formed during power generation and industrial processes and storing it so that it is not emitted into the atmosphere. CCS technologies have significant potential to reduce CO₂ emissions in energy systems(source). The carbon capture challenge provides an opportunity for oil and gas companies curb the long-term effects of CO2 being released into the atmosphere.
+
+
+Challenges for the African Sustainability Agenda
+
+
+A barrage of challenges come into play in preparing a people for a total shift from what was to what is to come. These challenges include but are not limited to
+
+
+Technical Know HowNational PoliciesEconomic and Financial ChallengesInsecurity of Nation StatesPolitical and Legislative Issues
+
+
+A Data Driven Headway
+
+
+Follow the data has become a phrase linked to this 21ST Century. Truthfully, projections show that although challenging, renewable energies will eventually come to stay. It is left for we to accept this truth and begin making preparations in our education and industrial sectors.
+
+
+Conclusion
+
+
+The oil industry has been a key contributor in meeting the energy demand of the world's growing population through the production of hydrocarbons. As the world moves towards renewable energy in a bid to achieve carbon-neutrality, it is essential that petroleum engineers integrate renewable energy into their strategies, skill sets and operations. This aspiration can be accomplished by, for example, integrating renewable energy technologies into existing and future upstream oil and gas processes.
+
+
+Apart from the traditional aspects of energy generation, several industries depend on derivative products from the activities of petroleum engineering. These include the healthcare industry through its use of plastics and organics for the manufacture of pharmaceuticals, agricultural industry through use of fertilizers containing ammonia, as well as the construction industry that uses asphalt for road construction and other applications.
+
+
+Therefore, there is a need for a gradual harmonization of the skills and technology available to the oil and gas production, with that of renewable energies. However, the key players in the African clime – government, business leaders, leading scientists/engineers- must be ready to lay aside differences and collaborate in order to successfully prepare for the coming shift from fossil fuels as a source of energy alone to a sustainable energy mix
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Abbreviations
+
+
+AbbreviationsNomenclatureExpansion CAPEX- Capital Expenditure CSS- Carbon Capture GHG- Green House Gas WECD– World Commission on Environment and Development SDG– Sustainable Development Goals WECD– World Commission on Environment and Development
+
+
+References
+
+
+BP Energy Outlook2018https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2018.pdfGetting Started with the Sustainable Development Goals: A Guide for Stakeholders," UN Sustainable Development Solutions Network (December2015).Guérin, P., Gaszynskiet al. . 2018. New Energy Revolution for Oil & Gas. Presented at Offshore Technology Conference Kuala Lumpur, Malaysia, 20-23 March. OTC-28255-MS. https://www.statista.com/statistics/1268403/co2-emissions-per-capita-in-africa-by-country/Google Scholar https://ourworldindata.org/co2-emissionsIEA (International Energy Agency). 2018. World Energy Outlook.The World Commission on Environment and Development (WCED)1987. Our Common Future. Oxford University PressKnox, R., and Buck, E.2018. Renewable Generated Energy for Subsea Power Infrastructure. Presented at Offshore Technology Conference, Kuala Lumpur, Malaysia, 20-23 March. OTC-28762-MS.Google Scholar UN General Assembly, Transforming our world: the 2030 Agenda for Sustainable Development, 21October2015.YALILEARNS | "UNDERSTANDING RENEWABLE ENERGY" FACILITATION GUIDE
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211937-MS
+
+
+

files/2022/Characteristic Curvature Assessment of Some Natural Surfactants for Chemical Enhanced Oil Recovery Applications in Nigeria.txt

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+----- METADATA START -----
+Title: Characteristic Curvature Assessment of Some Natural Surfactants for Chemical Enhanced Oil Recovery Applications in Nigeria
+Authors: Jeffrey Gbonhinbor, Ann Obuebite, George Kuradoite, Augustine Agi
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211996-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Chemical enhanced oil recovery (CEOR) application of natural surfactants is based on potential interfacial tension (IFT) alterability and eco-friendly considerations. The reduced IFT is associated with microemulsion formation in relation to a surfactant’s characteristic curvature. Lately, surface activities of natural surfactants have gained interest in Nigerian laboratory studies with no attention given to their hydrophilicity/hydrophobicity. This research focuses on molecular weight determination, micelle formation, and characteristic curvature evaluation of readily available natural surfactants. Four plants that are known to possess relevant surfactant properties were selected for this evaluation. Freezing point dipping method was used to determine the average molecular weight of each surfactant. Critical micelle concentration (CMC) was ascertained by electric conductivity tests. Characteristic curvature was evaluated from microemulsion formulations of toluene and aqueous surfactant mixtures. Formulated aqueous surfactant mixture consists of a combination of selected natural surfactant and a reference surfactant. Sodium dodecylsulphate (SDS) was adopted as the reference surfactant throughout this work. The analysis was configured in line with the hydrophilic-lipophilic deviation (HLD) model set to 0. Results yielded average molecular weights of examined surfactants between 128.3 g/mol to 186.7 g/mol. Critical micelle concentrations values of 0.45 to 0.60 were derived for all natural surfactants. Estimated characteristic curvature values suggested hydrophobicity with values from 0.116 to 0.194. As a consequence, these natural surfactants possess a tendency to form reverse micelles due oleic phase attraction. Their low positive values make them suitable for lowering IFT in order to mobilise trapped formation oil.
+
+
+
+
+Keywords:
+surfactant,
+concentration,
+jatropha curca,
+enhanced recovery,
+curvature,
+assessment,
+chemical flooding methods,
+dialium guineense willd,
+enhanced oil recovery application,
+natural surfactant
+
+
+Subjects: 
+Improved and Enhanced Recovery,
+Chemical flooding methods
+
+
+
+
+Introduction
+
+
+Oil production sustainability from a watered-out oilfield due to water injection has invoked different techniques in the petroleum industry. Revitalising such production wells often requires augmenting existing injection wells to match a new/different recovery scheme. This succeeding scheme targets discountinued and isolated oil pockets in the formation surrounded by previously injected saline fluid. Hence, progressive exploitation of oil is tied to surmounting viscous and capillary forces present in the formation. These forces can be surpassed by injecting chemicals into the formation in the form of Chemical Enhanced Oil (CEOR) processes. A popular variant of this process encompasses utilising surfactant solutions to adjust the interfacial tension (IFT) between oil and water. Maximum yield of oil is usually accomplished by amending the IFT value to its ultralow state (Healy and Reed, 1973; Sheng, 2015). A bi-continous (middle phase) microemulsion formed by the interaction between oil-water-surfactant systems guarantees this suitable IFT. This becomes attainable at optimal saline concentration owing to the existence of an indistinguishable phase around the invariant point. The magnitude of microemulsion or aqueous micelle formed complements the equivalent value of the system’s IFT (Miller et al., 1977; Mahboob et al., 2022). However, microemulsion systems formed by this interaction is strongly controlled by the surfactant’s characteristic curvature (Nguyen and Sabatini, 2011).
+
+
+Characteristic curvature is an evaluation of a surfactant’s capacity to produce normal micelles, reverse micelles, or intermediate aggregates (Acosta et al., 2008). These conditions are captured by a dimensionless negative or positive numeral which represent a surfactant’s hydrophilicity or hydrophobicity respectively. A hydrophilic surfactant inclines to normal micelles formulation, while reverse micelles lean towards a hydrophobic surfactant in micellar solutions (Acosta et al., 2008). Micellar solutions in oil-water-surfactant systems are oil-external microemulsion, water-external microemulsion, and middle phase microemulsion. Contextually, normal micelles are attributed to oil-external (Type I) microemulsions which indicate a surfactant’s preferential solubility in water. Water-external (Type II) microemulsions are linked to reverse micelles tendencies of a surfactant invidiously soluble in oil. Middle phase (Type III) microemulsions characterise a surfactant’s solubility in both oil and water at zero hydrophilic-lipophilic deviation (HLD) (Salager et al., 1979; Witthayapanyanon et al., 2008). The HLD concept accounts for molecular composition of surfactant, salinity, temperature, oil composition, and presence of cosurfactant or cosolvent (Salager et al., 1979; Acosta et al., 2008; Witthayapanyanon et al., 2008; Nguyen and Sabatini, 2011; Wesson et al., 2012; Jin et al., 2015; Mejia and Kostarelos, 2015; Mavaddat et al., 2016; Kittithammavong et al., 2021). Surfactant selection based on molecular composition has given rise to HLD in relation to surfactants of synthetic and natural origins. While synthetic surfactants are readily sought after, natural surfactants are being considered for ecological reasons.
+
+
+Natural surfactants are amphiphilic compounds derived from plant or animal source via some production means. Surfactants of the animal origin entails synthesising a wide range of microorganisms with varied compositions. The cost implication of producing some natural surfactant from animal source is most likely greater than their synthetic counterparts (Holmberg, 2001). The production process may yield amphiphilic compounds of low or high molecular weight surfactants. Conversely, plant source focuses on selecting a plant with saponin constituent due to its foaming ability in solvents (Sahu et al., 2008; Rai et al., 2021). These surfactants have gained interest in the petroleum industry due to their ability to relatively modify surface in liquid systems (Bachari et al., 2018). Nigeria has adopted this trend with key emphasis on locally sourced plant-based natural surfactants for CEOR processes without field application (Olafuyi et al., 2010; Gbonhinbor and Onyekonwu, 2015; Alawode and Falode, 2021). These studies represented in Table 1 show laboratory investigations with no attention given to their characteristic curvature and molecular weight. Thus, native plant-based natural surfactants like Aspilia africana, Dialium guineense Willd, Vernonia amygdalina, and Jatropha curcas were considered in this study. Okello et al. (2020) phytochemical analysis showed the richness of saponins and other compounds in Aspilia africana. Kone et al. (2004) established the existence of saponins, tannins, alkanoids, flavonoids, terpenoids, and glycosides in Dialium guineense Willd. Wang et al. (2018) isolated four steroidal saponins from aqueous ethanol in combination with leaves of Vernonia amygdalina. Rahu et al. (2021) quanlitative ascertained the presence of saponins and other phytochemicals in extracts of Jatropha curcas.
+
+
+Table 1Summary of Natural Surfactant Investigations in Nigeria Author
+            . Year
+            . Investigation
+            . Gap
+            . Obuebite et al. 2020 CMC assessment on hibiscus plant extract, Elaeis guineenis, and Moringa oleiferaPeformed phase behaviour analysis on natural surfactantsComparative secondary sand pack flooding with natural surfactants and SDS No phytochemical assessementNo characterization in terms of molecular weight and characteristic curvature Izuwa et al. 2021 IFT measurement in oil-water-surfactant systems using local surfactant such as PBAS, RPAS, UPAS, and SCSChemical structure analysis of natural surfactants Ignored molecular weight assessment and characteristic curvature Author
+            . Year
+            . Investigation
+            . Gap
+            . Obuebite et al. 2020 CMC assessment on hibiscus plant extract, Elaeis guineenis, and Moringa oleiferaPeformed phase behaviour analysis on natural surfactantsComparative secondary sand pack flooding with natural surfactants and SDS No phytochemical assessementNo characterization in terms of molecular weight and characteristic curvature Izuwa et al. 2021 IFT measurement in oil-water-surfactant systems using local surfactant such as PBAS, RPAS, UPAS, and SCSChemical structure analysis of natural surfactants Ignored molecular weight assessment and characteristic curvature View Large
+
+
+Materials and Method
+
+
+Materials
+
+
+The leaves of Aspilia africana, Dialium guineense Willd, Vernonia amygdalina, and Jatropha curcas were harvested from scrublands in Niger Delta. They were all sundried for about 6 days prior to their respective pulvurisation and storage in sealed containers. Sodium dodecyl sulphate (SDS) of 288.38 g/mol with an assay of 85% was utilized as the reference surfactant thoroughout this study. A known characteristic curvature value of the reference surfactant (SDS) was taken as -0.92 (Acosta et al., 2008). Toluene of 0.87 g/cm3 density with a 99% purity and EACN of 1 was adopted as the hydrocarbon in this work. Saline solutions were formulated using sodium chloride (NaCl) of 58.44 million Dalton with 99% purity.
+
+
+Experimental Tests
+
+
+Experimental tests consist of molecular weight determination, critical micelle concentration (CMC) measurement, and phase behaviour tests. Molecular weight of all natural surfactants was ascertained using a cryoscopic technique in line with the freezing point depression. This method assesses molecular weight by the dissolution of a solute in a pure solvent. The dissolution of solute molecules in the solvent possesses an inverse relationship with the pure solvent’s freezing temperature. Distilled water of 10g was placed in an enclosed test tube fitted with a wire stirrer, mercury thermometer, and a rubber stopper. The test tube setup was immersed in an ice water bath while being constantly stirred to a few degrees less than its freezing point. A stable temperature reading was readoff from the thermometer when the distilled water’s freezing point was established. A prepared natural surfactant in powdered form was added to the distilled water enclosed in a test tube. The mixture was allowed to stand for 72 hours prior to sieving of the aqueous mixture. Mass of dissolved solute in the aqueous mixture was determined and the experimental procedure was reconducted to measure the freezing point. This approach was employed to all natural surfactants in dried powdered form. Molecular weight of each surfactant was determined by a colligative equation in physical chemistry given as,
+
+
+Mw=kf×M1ΔTf×M2(1)
+
+
+where,
+
+
+Mw = molecular weight of solute, g/mol
+
+
+M1 = mass of solute, g
+
+
+M2 = mass of solvent, kg
+
+
+ kf = cryoscopic constant, (1.853 °K kg/moles of water)
+
+
+∆Tf = change in freezing point depression of pure solvent and solution, °C
+
+
+The critical micelle concentration for each natural surfactant was determined by the performance of concentration-electric conductivity test. This test allows the CMC value to be derived from the point of inflection of an electric conductivity-concentration graphical assessment. A constant salinity of 0.1 g/100ml brine solution was utilized to effect mass concentration of dissolved surfactant. A selected powdered surfactant was soaked in the aqueous saline solution for 72 hours prior to sieving. Progressive mass concentration of a natural surfactant was verified by dissolved mass gained in 0.1 g/100ml brine solution. Each dissolved mass gained by the aqueous solution was configured to suit concentrations ranging from 0.1% to 1%. Electric conductivity measurements were implemented for each concentration using an electric conductivity meter. The procedure was repeated for each natural surfactant studied in this research.
+
+
+Phase behaviour tests or salinity scan tests were adapted to experimentally establish the characteristic curvature of all natural surfactants. These tests apply a mixing rule between a surfactant of known characteristic curvature and another surfactant of unknown characteristic curvature. The fundamental concept of this evaluation was taken from the general HLD model given as (Salager et al., 1979),
+
+
+σHLD=ln⁡(S)−K(EACN)−f(a)+σ−aT(ΔT)(2)
+
+
+where,
+
+
+S = electrolyte concentration of surfactant
+
+
+K = empirical constant determined by surfactant’s head group of molecules
+
+
+EACN = equivalent alkane carbon number
+
+
+f(a) = alcohol type and concentration adjustment term
+
+
+σ = characteristic curvature of surfactant
+
+
+aT = empirical temperature constant
+
+
+ΔT = temperature differential
+
+
+Since the characteristic curvature is to be evaluated, Nguyen and Sabatini (2009) suggested that for mixed surfactant systems Eq. 2 becomes,
+
+
+σσln⁡(S*S1*)=X2(σ1−σ2)(3)
+
+
+where,
+
+
+S∗ = optimal salinity of mixed surfactant
+
+
+S1* = optimal salinity of reference surfactant
+
+
+X2 = mole fraction of test surfactant
+
+
+σ1 = characteristic curvature of reference surfactant
+
+
+σ2 = characteristic curvature of test surfactant
+
+
+Phase scans were performed with toluene with an aqueous mixture of a natural surfactant and SDS at different ratios. Tests utilised 2 ml of toluene and 2 ml of surfactant in a 5 ml test tube. The loaded test tubes were agitated daily for 3 days and left to equilibrate for 14 days. Since optimal salinity were established by visual inspection, scans were fine tuned between suspected optimal electrolyte concentrations.
+
+
+Results and Discussion
+
+
+The average molecular weight of Aspilia africana, Dialium guineense Willd, Vernonia amygdalina, and Jatropha curcas were obtained as 128.29g/mol, 186.73g/mol, 158.58g/mol and 150.3g/mol respectively. These values were calculated from the average of the 3 experimental investigations for each natural surfactant. This analysis was based on a fairly stable freezing point of distilled water measured from -2.7°C to -3.1°C. All analysed freezing point of aqueous natural surfactants showed an inverse relationship between mass gained and temperature obtained. These results conform to the fundamental principles associated with colligative properties employed in freezing dipping technique. All determined molecular weight of analysed natural surfactants fall below the molecular weight of SDS. Tables 2 through 5 show a summary of the molecular weight analyses of each natural surfactant.
+
+
+Table 2Molecular Weight Analysis of Aspilia Africana S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.49 7.0 −2.9 −4.0 118.36 2 0.50 6.5 −3.0 −4.1 140.90 3 0.52 7.0 −3.1 −4.2 125.60 S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.49 7.0 −2.9 −4.0 118.36 2 0.50 6.5 −3.0 −4.1 140.90 3 0.52 7.0 −3.1 −4.2 125.60 View Large
+
+
+Table 3Molecular Weight Analysis of Dialium guineense Willd S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.64 11.9 −2.70 −3.1 196.14 2 0.57 10 −2.60 −3.2 176.70 3 0.55 10.5 −2.80 −3.3 187.36 S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.64 11.9 −2.70 −3.1 196.14 2 0.57 10 −2.60 −3.2 176.70 3 0.55 10.5 −2.80 −3.3 187.36 View Large
+
+
+Table 4Molecular Weight Analysis of Vernonia amygdalina S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.53 8.1 −2.8 −3.6 152.125 2 0.55 8.3 −2.9 −3.7 166.894 3 0.60 8.9 −3.0 −3.8 156.729 S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.53 8.1 −2.8 −3.6 152.125 2 0.55 8.3 −2.9 −3.7 166.894 3 0.60 8.9 −3.0 −3.8 156.729 View Large
+
+
+Table 5Molecular Weight Analysis of Jatropha curcas S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.52 9.5 −2.9 −3.5 169.679 2 0.53 9.8 −3.0 −3.7 155.668 3 0.54 10 −3.1 −3.9 125.54 S/N
+            . M1 (g)
+            . V (cc)
+            . θ1 (°C)
+            . θ2 (°C)
+            . MW (g/mol)
+            . 1 0.52 9.5 −2.9 −3.5 169.679 2 0.53 9.8 −3.0 −3.7 155.668 3 0.54 10 −3.1 −3.9 125.54 View Large
+
+
+CMC values of Aspilia africana, Dialium guineense Willd, Vernonia amygdalina, and Jatropha curcas were estimated as 0.6%, 0.45%, 0.50%, and 0.50% respectively. Their corresponding points of inflection suggest an inverse relationship with molecular weights estimated. These trends implies that a higher molecular weight surfactant will result in a lower CMC value. Similar results were obtained by Obuebite et al. (2020) in their analysis of other native natural surfactants relative to SDS. This infers that more relative concentration will be needed for natural surfactants in CEOR tests. Figures 1 through 4 illustrates concentration-electric conductivity test for analysed natural surfactants.
+
+
+Figure 1View largeDownload slideElectric Conductivity Test for Aspilia africanaFigure 1View largeDownload slideElectric Conductivity Test for Aspilia africana Close modal
+
+
+Figure 2View largeDownload slideElectric Conductivity Test for Dialium guineense WilldFigure 2View largeDownload slideElectric Conductivity Test for Dialium guineense Willd Close modal
+
+
+Figure 3View largeDownload slideElectric Conductivity Test for Vernonia amygdalinaFigure 3View largeDownload slideElectric Conductivity Test for Vernonia amygdalina Close modal
+
+
+Figure 4View largeDownload slideElectric Conductivity Test for Jatropha curcasFigure 4View largeDownload slideElectric Conductivity Test for Jatropha curcas Close modal
+
+
+The characteristic curvature of Aspilia africana, Dialium guineense Willd, Vernonia amygdalina, and Jatropha curcas were derived as 0.136, 0.116, 0.131, and 0.194 respectively. These positive values of characteristic curvature infer hydrophobicity of natural surfactants and surfactant tendencies to form reverse micelles (Acosta et al., 2008). Such positive values favour water-external microemulsions in relation to high salinity in phase scans. However, their corresponding low hydrophobic values suggest a minimum tendency to form reverse micelles. This implies that these natural surfactants may be useful for high saline Niger delta formations. Figures 5 through 8 represent optimal salinity assessment of natural surfactant mixtures with SDS.
+
+
+Figure 5View largeDownload slideOptimal Salinity Assessment of Aspilia Africana + SDSFigure 5View largeDownload slideOptimal Salinity Assessment of Aspilia Africana + SDS Close modal
+
+
+Figure 6View largeDownload slideOptimal Salinity Assessment of Dialium guineense Willd + SDSFigure 6View largeDownload slideOptimal Salinity Assessment of Dialium guineense Willd + SDS Close modal
+
+
+Figure 7View largeDownload slideOptimal Salinity Assessment of Vernonia amygdalina + SDSFigure 7View largeDownload slideOptimal Salinity Assessment of Vernonia amygdalina + SDS Close modal
+
+
+Figure 8View largeDownload slideOptimal Salinity Assessment of Jatropha curcas + SDSFigure 8View largeDownload slideOptimal Salinity Assessment of Jatropha curcas + SDS Close modal
+
+
+Conclusion
+
+
+The study approach of this research implies the following:
+
+
+All natural surfactants emanating from plant-based sources exhibit relatively low molecular weight against their synthetic counterparts. However, their low cost, negligible toxicity, and availability may be regarded as an advantage over their synthetic competitors.Selected plant-based natural surfactants demonstrated relatively higher CMC values over reported value of SDS. This may permit a wider range of concentration investigative studies for surfactant flooding practices.A very low hydrophobic trend was observed for all natural surfactants in this study. These surfactants may fair favourably with IFT reduction in CEOR operations for high saline reservoirs.
+
+
+Based on findings, it is recommended that:
+
+
+Studies must be expanded to accomodate dry extraction production, other phase behaviour tests, adsorption tests, and core flooding tests. These gray areas must be addressed in order to promote further clarity prior to pilot field tests.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Acosta, E.J., Yuan, J.S., and Bhakta, A.S.2008. The Characteristic Curvature of Ionic Surfactants. J. Surfactant Deterg. 11(2): 145-158.Google ScholarCrossrefSearch ADS  Alawode, A.J., and Falode, O.A.2021. Enhanced Oil Recovery Practices: Global Trend, Nigeria’s Present Status, Prospects and Challenges. CJAST40(8): 79-92.Google Scholar Bachari, Z., Isari, A.A., Mahmoudi, H., Moradi, S., and Mahvelati, E.H.2018. Application of Natural Surfactants for Enhanced Oil Recovery – Critical Review. Proceedings at the World Multidisciplinary Earth Sciences Symposium, Prague, Czech Republic.Google Scholar Gbonhinbor, J.R., and Onyekonwu, M.O.2015. Experimental and Simulation Study on Aqueous Protein to Improve Oil Recovery. Int. J. Petroleum Engineering1(4): 271-289.Google ScholarCrossrefSearch ADS  Holmberg, K.2001. Natural Surfactants. Current Opinion in Colloid and Interface Science6(2): 148-159.Google ScholarCrossrefSearch ADS  Healy, R.N., and Reed, R.L.1973. 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Characteristic Curvature Evaluation of Anionic Surfactants for Enhanced Oil Recovery Applications. Proceedings of NCUR, Cheney, WA.Google Scholar Miller, C.A., Hwan, R-.N., Benton, W.J., and Fort, T.Jr.1977. Ultralow Interfacial Tensions and Their Relation to Phase Separation in Micellar Solutions. J. Colloid Interface Sci. 61(3): 554-568.Google ScholarCrossrefSearch ADS  Izuwa, N.C., Nwankwo, I.V., Nwachukwu, A., Ohia, N.P., Uwaezuoke, N., Duru, U.I., Obah, B., and Ekwueme, S.T.2021. Suitability Evaluation of Locally Sourced Surfactants for Enhanced Oil Recovery (EOR) in Niger Delta. International Journal of Oil, Gas, and Coal Engineering9(5): 71-82.Google ScholarCrossrefSearch ADS  Jin, L., Jamili, A., Li, Z., Lu, J., Luo, H., Shiau, B.J.B., Delshad, M., and Harwell, J.H.2015. Physics Based HLD-NAC Phase Behavior Model for Surfactant/Crude Oil/Brine Systems. J. Pet. Sci. Eng. 136(1): 68-77.Google Scholar Kittithammavong, V., Charoensaeng, A., and Khaodhiar, S.2021. A Normalized HLD (HLDN) Tool for Optimal Salt-Concentration Prediction of Microemulsions. Appl. Sci. 11(19): #9151. https://doi.org/10.3390/app11199151.Google ScholarCrossrefSearch ADS  Nguyen, T.T., and Sabatini, D.A.2009. Formulating Alcohol-Free Microemulsions Using Rhamnolipid Biosurfactant and Rhamnolipid Mixtures. J. Surfactants Deterg. 12(2): 109-115.Google ScholarCrossrefSearch ADS  Nguyen, T.T., and Sabatini, D.A.2011. Characterisation and Emulsification Properties of Rhamnolipid and Sophorolipid Biosurfactants and Their Applications. Int. J. Mol. Sci. 12(2): 1232-1244.Google ScholarCrossrefSearch ADS PubMed Obuebite, A.A., Onyekonwu, M., Akaranta, O., Ubani, C., and Uzoho, C.2020. An Experimental Approach to Low Cost, High Performance Surfactant Flooding. Scientific African8(1): #e00361. https://doi.org/10.1016/j.sciaf.2020.e00361.Google Scholar Olafuyi, O.A., Chukwuma, M.C., and Adewole, E.S.2010. Enhanced Oil Recovery (EOR) Potential in Nigeria. Journal of Economics and Engineering. 1(1): 35-40.Google Scholar Okello, D., Lee, J., and Kang, Y.2020. Ethnopharmacological Potential of Aspilia Africana for the Treatment of Inflammatory Dieseases. Evidence-Based Complementary and Alternative Medicine2020(1): #8091047. https://doi.org/10.1155/2020/8091047.Google ScholarPubMed Rahu, M.I., Naqvi, S.H.A., Memon, N.H., Idrees, M., Kandro, F., Pathan, N.L., Sarker, N.I., and Bhutto, M.A.2021. Determination of Antimicrobial and Phytochemical Compounds of Jatropha Curcas Plant. Saudi Journal of Biological Science28(5): 2867-2876.Google ScholarCrossrefSearch ADS  Rai, S., Acharya-Siwakoti, E., Kafle, A., Devkota, H.P., and Bhattarai, A.2021. Plant Derived Saponins: A Review of Their Surfactant Properties and Applications. Sci3(4): #44. https://doi.org/10.3390/sci3040044.Google ScholarCrossrefSearch ADS  Sahu, N.P., Banerjee, S., Mondal, N.B., and Mandal, D.2008. Steroidal Saponins. Fortschr. Chem. Org. Naturst. 89(1): 127-141.Google Scholar Salager, J.L., Morgan, J.C., Schechter, R.S., Wade, W.H., and Vasquez, E.1979. Optimum Formulation of Surfactant/Water/Oil Systems for Minimum Interfacial Tension or Phase Behavior. SPE J. 19(2): 107-115. SPE-7054-PA.Google Scholar Sheng, J.J.2015. Status of Surfactant EOR Technology. Petroleum1(2): 97-105.Google ScholarCrossrefSearch ADS  Wang, J., Song, H., Wu, X., Zhang, S., Gao, X., Li, F., Zhu, X., and Chen, Q.2018. Steroidal Saponins from Vernonia Amygdalina Del. Molecules23(3): #579. https://doi.org/10.3390/molecules23030579.Google ScholarCrossrefSearch ADS  Wesson, L.L., Lohateeraparp, P., Harwell, J.H., and Shiau, B-.J.2012. Next Generation Surfactants for Improved Chemical Flooding Technology. Final Report for University of Oklahoma, Norman, OK.Google Scholar Witthayapanyanon, A., Harwell, J., and Sabatini, D.2008. Hydrophilic-Lipophilic Deviation (HLD) Method for Characterizing Conventional and Extended Surfactants. J. Colloid Interf. Sci. 325(1): 259-266.Google ScholarCrossrefSearch ADS  
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211996-MS
+
+
+

files/2022/Collation Analysis of Oil and Gas Production Reports Using Excel Python and R A Data Science Approach in Handling Large Data.txt

@@ -0,0 +1,828 @@
+----- METADATA START -----
+Title: Collation, Analysis of Oil and Gas Production Reports Using Excel, Python and R: A Data Science Approach in Handling Large Data
+Authors: Opeyemi Oluwalade, Yisa Adeeyo, Frank Emeruwa, Nnamdi Nwabulue, Adaora Obi-Okoye, Adekanmi Adesola
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212031-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The ability to have data and manipulate it to uncover meaningful information is a must-have skill in this day and age. In this paper, practical techniques were applied to combine and analyze 65 sets of well test data received from the Field Engineers for a particular well (Well-001). Comparisons were made between manually collating (copy and paste) and analyzing the data and applying Data Science techniques. Analysis was also done after collation of this data.It was on the basis of this review that it was observed that the well had a corroded bean box and that was replaced, while further analysis on the other hand showed that in the future, a Water Shut Off (WSO) and perforation extension opportunity could be carried out to boost and optimize production in this particular well.The emphasis of this paper is not on the analysis of the data but comparing various tools that can be used to combine large data from different excel files and collating them into one sheet for analysis and pointing out how man-hours can be optimized by applying Data Science. Data used in this paper were routine Field reports stored in a file that pertains to a Well in one of the Fields of interest. One of the takeaways from the job done here is that we can achieve more in less time from Data Science tools and codes like R, Python, VBA and also other tools like Power Query and Pivot Tables.
+
+
+
+
+Keywords:
+activechart,
+production control,
+cobol,
+production monitoring,
+upstream oil & gas,
+reservoir surveillance,
+textframe2,
+forecolor,
+cutcopymode false application,
+select activechart
+
+
+Subjects: 
+Well & Reservoir Surveillance and Monitoring,
+Formation Evaluation & Management,
+Information Management and Systems,
+Data mining
+
+
+
+
+INTRODUCTION
+
+
+The objective of having data is not limited to storage alone but also requires organization and analysis of the data. We must be able to derive some sense from the data, observe trends, patterns and also make business decisions to optimize outputs. Properly organized data can be analysed with appropriate tools and reports can be made that tell the required story.
+
+
+In the oil and gas industry, data is churned out in large volumes on a regular basis ranging from daily production reports, well test data, Carbon Oxygen (CO) logging data, daily drilling reports, bottom hole pressure reports, well head pressures, casing pressures among others. Data, they say is the new oil, without which minor and major decisions cannot be taken.
+
+
+Data science is a systematic art that helps in uncovering the insights and trends in data through the use of simple tools and codes. The challenge is that when information is voluminous, it takes time to arrange the data and this also affects effective and prompt analysis which in turn delays timely decision making. Alot of value is gained or eroded by ability or inability to make key decisions promptly. Using Microsoft Excel, we will be able to save, assemble, and look at data. R was used as a programming language for statistical computing and graphics used to clean, analyze and display the data on graphs. Python was used for data analytics, visualization and automation since it is a popular and adaptable programming language.It can also be used in machine learning applications.
+
+
+Information that cannot be retrieved is not usable, information needs to be measured to be monitored, from monitored data, patterns can be inferred, and this can further help in the analysis of data. Information can then be optimized by the knowledge of trends.
+
+
+In this paper, we have demonstrated how large volumes of data can be collated systematically, analyzed and automated using data science techniques through excel and programming languages like Python and R. This enabled the Engineer and analyst make quick decisions within a short period of time, that is, doing more in less time. Large volumes of oil and gas reports from a Field was used in showcasing this effective data science approach. We have moved away from manual, cumbersome and time-wasting approaches to automated techniques.
+
+
+The various techniques used in this paper include the following:
+
+
+Microsoft ExcelManualPower Query/Pivot TableVBAR Programming LanguagePython Programming Language
+
+
+This implied that five (5) different methods were used to collate, visualize and analyze the data.
+
+
+GENERAL APPROACH
+
+
+Identify where the Field data was stored and identify the path:C:\Users\OneDrive\Desktop\New folderCheck the data sets to be sure they have the same number of columns and column titles, that is, the same structure in this case.   All the data files have the same structures, same number of columns (8 columns in this case) and the same column titlesCollate the data from 65 different excel workbooks and merge into a single workbook on a single sheet
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Figure 1View largeDownload slidePictorial representation of 65 different excel sheetsFigure 1View largeDownload slidePictorial representation of 65 different excel sheets Close modal
+
+
+Steps taken in Collating, Cleaning, Visualizing and Analyzing the data provided
+
+
+Figure 2View largeDownload slideFlow Chart of steps taken towards collating and analyzing the dataFigure 2View largeDownload slideFlow Chart of steps taken towards collating and analyzing the data Close modal
+
+
+METHODS USED
+
+
+EXCEL – MANUAL (COPY, PASTE, PLOT)
+
+
+Microsoft Excel is a software application for making spreadsheets, charts and graphs that was created and sold by Microsoft. It is part of the Microsoft Office package. In Excel, data is set up in columns and rows. Rows and columns meet in a space known as a cell. Each cell contains information such as text, a number, or a formula.
+
+
+It is also a powerful data visualization and analysis software that stores, organizes and tracks data sets using formulas and functions in spreadsheets. Engineers, accountants, data analysts and other professionals use Excel.
+
+
+Microsoft Excel can be used for the following among others:
+
+
+Data Entry and ManagementBusiness AnalysisPerformance ReportingStrategic AnalysisProgrammingCharting and Graphing
+
+
+In this method, we manually copied and pasted the well test data from each of the 65 excel files and collated them into a single excel sheet (Figure 3), thereafter the data was plotted (Figure 4). It took about an hour and thirty minutes (90 minutes) to collate the data and another twenty minutes (20 minutes) to plot the collated data. This process took a total of one hour and fifty minutes (110 minutes). The process was very boring, repetitive, and took a lot of time. This method works better for small amounts of data.
+
+
+Figure 3View largeDownload slideManually combined data by copy and pasting 65 sheets one by oneFigure 3View largeDownload slideManually combined data by copy and pasting 65 sheets one by one Close modal
+
+
+Figure 4View largeDownload slidePlot of the collated Well Test dataFigure 4View largeDownload slidePlot of the collated Well Test data Close modal
+
+
+EXCEL POWER QUERY
+
+
+In Microsoft Excel, Power Query is a feature that can streamline the process of importing data from a variety of source files and sorting it into an Excel sheet in the most convenient and usable format. It is not necessary for users to learn any specific code in order to use Power Query, which is a user-friendly business intelligence tool. It took only about ten (10) minutes in the process of collating the 65 data points from the various files into a single file and about twenty (20) minutes to carry out the required visualization and analysis of the data. It therefore took a total of thirty (30) minutes for the collation, visualization and analysis of the Field data. This is a very simple method and saved a lot of time (an hour and twenty minutes saved compared to the copy and paste approach).
+
+
+How we used Power Query to combine the Excel Files
+
+
+Move the files you want to merge into a single folder and find the path. In this case, all the 65 well test data were stored in the path below:C:\Users\OneDrive\Desktop\New folderCheck the data sets to be sure they have the same number of columns and column titles, that is, the same structure. The columns and titles in the well test data are all the same.In Excel, go to the "Data" tab.Click on Get Data, then click on "From File" and then move to "From Folder".Browse and choose the folder path.Click "Ok".If the files are prepared for merging, click "Combine & Load".
+
+
+Figure 5View largeDownload slidePath showing the folder where all the data is stored in the "New folder"Figure 5View largeDownload slidePath showing the folder where all the data is stored in the "New folder" Close modal
+
+
+Using the Get and Transform data in Power Query, these were the steps taken in collating the 65 excel data sheets.
+
+
+Figure 6View largeDownload slideUtilizing "Get and Transform data" in Power Query to collate the dataFigure 6View largeDownload slideUtilizing "Get and Transform data" in Power Query to collate the data Close modal
+
+
+How to Plot Data in Power Query/Pivot Table
+
+
+Figure 7View largeDownload slidePivot Table of collated data from Power QueryFigure 7View largeDownload slidePivot Table of collated data from Power Query Close modal
+
+
+Figure 8View largeDownload slidePlotting the collated the data using the Pivot Table and ChartFigure 8View largeDownload slidePlotting the collated the data using the Pivot Table and Chart Close modal
+
+
+EXCEL -VBA
+
+
+VBA is an abbreviation for Visual Basic for Applications. It integrates Visual Basic, Microsoft's event-driven programming language, with Microsoft Office programs like Excel. It is a powerful built-in programming language that allows you to code functions or commands in a spreadsheet. VBA is a coding language used by millions of people around the world to automate tasks in Microsoft Office products. It is a language that has been around for decades and is one of the easiest coding languages to learn if you do not have a computer science background.
+
+
+VBA is used to perform a variety of other functions in addition to automation, creating and organizing spreadsheets. For example, users may need to automate some aspects of Excel, such as repetitive tasks, frequent tasks, generating reports, preparing charts, graphs and performing calculations, among other things. This kind of automation is also called "Macro." If you do the same things over and over in Microsoft Excel, you can record a macro to do them for you. A macro is a single action or a group of actions that you can do over and over again. This allows users to save time spent on repetitive tasks. In the collation of the 65 well test data, macros were generated based on smaller data and then scaled up to accommodate the 65 well test data points in the collation, visualization and interpretation. A total of forty-five (45) minutes was used for the entire process of data collation, visualization and interpretation using VBA.
+
+
+COMBINING FILES IN EXCEL -VBA
+
+
+Please refer to  Appendix 1 for the VBA codes used in combining/collating the 65 excel sheets. The codes were quite long as they were recorded.
+
+
+PLOTING EXCEL USING VBA
+
+
+Please refer to  Appendix 2 for the VBA codes used in plotting the 65 excel sheets. The codes were quite long as they were recorded.
+
+
+Figure 9View largeDownload slide65 sets of data collated data using VBA automation codeFigure 9View largeDownload slide65 sets of data collated data using VBA automation code Close modal
+
+
+Figure 10View largeDownload slide65 sets of collated data Plotted using VBA automation codeFigure 10View largeDownload slide65 sets of collated data Plotted using VBA automation code Close modal
+
+
+R PROGRAMMING LANGUAGE
+
+
+Foundation for Statistical Computing with the R Language was developed by statisticians Ross Ihaka and Robert Gentleman for data analysis and statistical software development by data miners and statisticians. Users have developed packages to extend the functionality of the R programming language.
+
+
+According to user polls and investigations of databases of scientific literature, R is one of the most popular programming languages for data mining. R ranks eleventh on the TIOBE index, a measure of programming language popularity, as of April 2022. Under the GNU General Public License, the official R software environment is an open-source, free software environment included in the GNU package. It is mostly written in C, Fortran, and R. (partially self-hosting).
+
+
+Precompiled executables are provided for various operating systems. R has a command line interface. Multiple third-party graphical user interfaces are also available, such as R Studio, an integrated development environment, and Jupyter, a notebook interface.
+
+
+R CODE FOR COMBINING DATA
+
+
+This R code was used to combine datasets in different workbooks into a single sheet in a workbook. In this example, data from 65 excel sheets were combine into a single excel sheet, that is, 65 sets of well test data were combined and also analyzed after visualization. A total of twenty-four (24) minutes was used for the entire process of data collation, visualization and interpretation using the R code.
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+R CODE FOR PLOTING DATA
+
+
+This R code was used to plot the 65 data sets that were combined into a single excel sheet
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Figure 11View largeDownload slide65 sets of data collated data using R Programming LanguageFigure 11View largeDownload slide65 sets of data collated data using R Programming Language Close modal
+
+
+Figure 12View largeDownload slide65 sets of collated data Plotted using R Programming LanguageFigure 12View largeDownload slide65 sets of collated data Plotted using R Programming Language Close modal
+
+
+PYTHON PROGRAMMING LANGUAGE
+
+
+Python is a general-purpose, high-level programming language. It supports several programming paradigms, including structured (especially procedural), object-oriented, and functional programming. It constantly ranks as one of the most popular programming languages. As at April 2022, Python ranks 1st in the TIOBE index, a measure of programming language popularity.
+
+
+Guido van Rossum started working on Python as a replacement for the ABC programming language in the late 1980s. He released Python 0.9.0 for the first time in 1991. Python 2.0 came out in 2000 with new features like list comprehensions, garbage collection that works when a cycle is found, reference counting, and support for Unicode. Python 3.0, which came out in 2008, was a major update that isn't fully compatible with older versions. Python 2 was discontinued with version 2.7.18 in 2020.
+
+
+PYTHON CODE FOR COMBINING DATA
+
+
+This Python code was used to combine datasets in different workbooks into a single sheet in a workbook. In this example, data from 65 excel sheets were combine into a single excel sheet, that is, 65 sets of well test data were combined and also analyzed after visualization. A total of twenty-one (21) minutes was used for the entire process of data collation, visualization and interpretation using the Python code.
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+PYTHON CODE FOR PLOTING THE DATA
+
+
+This Python code was used to plot the 65 data sets that were combined into a single excel sheet.
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Figure 13View largeDownload slide65 sets of data collated data using Python Programming LanguageFigure 13View largeDownload slide65 sets of data collated data using Python Programming Language Close modal
+
+
+Figure 14View largeDownload slide65 sets of collated data Plotted using PythonFigure 14View largeDownload slide65 sets of collated data Plotted using Python Close modal
+
+
+SUMMARY OF RESULTS
+
+
+The activities carried out to collate 65 sets of well test data into a single sheet using different methods was a very interesting one. It is very clear that inorder to be more productive, certain techniques can be applied to increase output at a reduced timeline. This implies that man hours can be optimized.
+
+
+Five (5) different methods were used to collate data from sixty-five (65) sets as depicted in Table 1 below. They all achieved the same results but at varying durations. The basic copy and paste technique will only be good for small data sets but it is advised to use more robust data science techniques for larger data sets and also, to automate routine jobs or simply have a refresh option when additional updates are added.
+
+
+Table 1SUMMARY OF RESULTS FROM THE FIVE (5) DIFFERENT TECHNIQUES USED IN COLLATING, PLOTTING AND ANALYZING THE DATA   View Large
+
+
+Figure 15View largeDownload slideData Analysis Techniques and Time taken to Collate, Plot and Analyze the DataFigure 15View largeDownload slideData Analysis Techniques and Time taken to Collate, Plot and Analyze the Data Close modal
+
+
+The 65 data points were later reduced to 46 data points since some of the other data points were repetitions to some of the existing data. In our well tests we call those points confirmatory tests. It was also observed that with the same choke size there was a sudden increase in gross rates, oil rates and even GOR indicating a possible eroded choke. This was confirmed with a bean box inspection and the eroded bean was later replaced. This helped in preventing further sand production and guaranteed further preservation of facilities that could suffer from erosional velocity issues.
+
+
+Figure 16View largeDownload slideCleaned data from 65 data points to 46 data pointsFigure 16View largeDownload slideCleaned data from 65 data points to 46 data points Close modal
+
+
+Figure 17View largeDownload slideData Visualization used to Analyze the Data based on AnomalyFigure 17View largeDownload slideData Visualization used to Analyze the Data based on Anomaly Close modal
+
+
+CONCLUSION
+
+
+There is need to further promote data analytics skills in the Energy industry in which the Oil and Gas industry is a part of, since information can be uncovered in a shorter time.Application of data analytics tools in solving problems will help us optimize systems and proceduresThe practical results obtained from collation of 65 data sets show that more can be done in less time when data science tools are applied.While the 5 different methods used were able to generate the basic results, it was clear that Data analytic tools combine speed with accuracy. While the speed in faster order is highlighted below:Python : 21 minutesR : 24 minutesPower Query : 30 minutesVBA : 45 minutesExcel (Manual): 110 minutesAfter consolidation, the data was cleaned up and duplicated data were removed.Analytical and automated solutions should be generated for routine or re-occurring activities.Inference from the chart showed that gross production, water production had suddenly increased even on the same choke at some point in time. Upon inspection, the choke had been corroded and therefore required that the bean box be changed and subsequently resulted in preserving the facilities in general.In a later review, which is not covered in this paper, the Well, also showed that it is a candidate for future WSO and perforation extension and therefore more value will be derived from this Well even in the future. This meant that the Well has future Well bore utility.The codes that were generated can actually be used in other similar situations but will just need to be uniquely tailored depending on the storage paths, number of data points, etc.pr
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+ABBREVIATIONS
+
+
+ABBREVIATIONSABBREVIATIONEXPANSION BSWBasic Sediments and Water BLPDBarrels of Liquid Per Day BOPDBarrels of Oil Per Day COCarbon Oxygen FTHPFlowing Tubing Head Pressure GORGas Oil Ratio VBAVisual Basic Application WSOWater Shut Off
+
+
+VBA CODE FOR THE COLLATION OF DATA
+
+
+Private Sub CommandButton1_Click()
+
+
+Dim i As Long
+
+
+Dim lcurrow As Long
+
+
+Dim lrow As Long
+
+
+Dim wb As Workbook
+
+
+For i = 1 To 65 Step 1
+
+
+Set wb = Workbooks.Open("C:\Users\OneDrive\Desktop\New folder" & "\Well-001_File " & i & ".xlsx")
+
+
+With wb.Sheets("Sheet1")
+
+
+If i = 1 Then lrow = 1
+
+
+Else lrow = 2
+
+
+End If
+
+
+Do Until. Range("A" & lrow).Value = vbNullString lcurrow = lcurrow + 1
+
+
+For n = 0 To 9 Step 1
+
+
+Me.Range("A" & lcurrow).Offset(columnoffset:=n).Value = . Range("A" & lrow).Offset(columnoffset:=n).Value
+
+
+Next n lrow = lrow + 1
+
+
+Loop
+
+
+End With wb.Close True
+
+
+Next i
+
+
+Set wb = Nothing
+
+
+End Sub
+
+
+PLOTING EXCEL USING VBA
+
+
+Sub Macro1()
+
+
+Macro1 Macro
+
+
+' Keyboard Shortcut: Ctrl+Shift+A
+
+
+'ActiveSheet.Shapes.AddChart2(332, xlLineMarkers).Select
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+Application.CutCopyMode = False
+
+
+ActiveChart.SeriesCollection.NewSeries
+
+
+ActiveChart.FullSeriesCollection(1).Name = "=Sheet1!$E$1"
+
+
+ActiveChart.FullSeriesCollection(1).Values = "=Sheet1!$E$2:$E$66"
+
+
+ActiveChart.FullSeriesCollection(1).XValues = "=Sheet1!$B$2:$B$66"
+
+
+ActiveChart.SeriesCollection.NewSeries
+
+
+ActiveChart.FullSeriesCollection(2).Name = "=Sheet1!$F$1"
+
+
+ActiveChart.FullSeriesCollection(2).Values = "=Sheet1!$F$2:$F$66"
+
+
+ActiveChart.FullSeriesCollection(2).XValues = "=Sheet1!$B$2:$B$66"
+
+
+ActiveChart.SeriesCollection.NewSeries
+
+
+ActiveChart.FullSeriesCollection(3).Name = "=Sheet1!$D$2:$D$66"
+
+
+ActiveChart.FullSeriesCollection(3).Name = "=Sheet1!$D$2:$D$66"
+
+
+ActiveChart.FullSeriesCollection(3).Name = "=Sheet1!$D$1"
+
+
+ActiveChart.FullSeriesCollection(3).Values = "=Sheet1!$D$2:$D$66"
+
+
+ActiveChart.FullSeriesCollection(3).XValues = "=Sheet1!$B$2:$B$66"
+
+
+ActiveChart.SeriesCollection.NewSeries
+
+
+ActiveChart.FullSeriesCollection(4).Name = "=Sheet1!$H$1"
+
+
+ActiveChart.FullSeriesCollection(4).Values = "=Sheet1!$H$2:$H$66"
+
+
+ActiveChart.FullSeriesCollection(4).XValues = "=Sheet1!$B$2:$B$66"
+
+
+ActiveChart.SeriesCollection.NewSeries
+
+
+ActiveChart.FullSeriesCollection(5).Name = "=Sheet1!$C$1"
+
+
+ActiveChart.FullSeriesCollection(5).Values = "=Sheet1!$C$2:$C$66"
+
+
+ActiveChart.FullSeriesCollection(5).XValues = "=Sheet1!$B$2:$B$66"
+
+
+ActiveChart.SeriesCollection.NewSeries
+
+
+ActiveChart.FullSeriesCollection(6).Name = "=Sheet1!$G$1"
+
+
+ActiveChart.FullSeriesCollection(6).Values = "=Sheet1!$G$2:$G$66"
+
+
+ActiveChart.FullSeriesCollection(6).XValues = "=Sheet1!$B$2:$B$66"
+
+
+ActiveWindow.SmallScroll Down:=-72
+
+
+Range("V21").Select
+
+
+ActiveSheet.ChartObjects("Chart 1").Activate
+
+
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+References
+
+
+SPE-205689-MS: How to Land Modern Data Science in Petroleum Engineering. HongbaoZhang; YijinZeng; LuluLiao; RuiyaoWang; XutianHou; JiangpengFeng; AmolMulunjkar.SPE-200781-MS: Data Science Use Case for Brownfield Optimization- A Case Study. ManishKumar; Tae HyungKim; DarrinSingleton M..IPTC-22172-MS: Data Science Adoption and Operationalization in O & G Industry: Challenges and Solutions. Mariem ZouchEasiest way to COMBINE Multiple Excel Files into ONE (Append data from Folder)By Leila Gharani (YouTube Video)How to Append multiple workbooks into one worksheet with VBA (Hindi/Urdu) By Mr. Kaash (YouTube Video)Wikipedia.orgGithub.com
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212031-MS
+
+
+

files/2022/Comparative Analysis of Gas Condensate Recovery by Carbon Dioxide Huff-N-Puff Carbon Dioxide Alternating Nitrogen and Nitrogen Injection A Simulation Study.txt

@@ -0,0 +1,523 @@
+----- METADATA START -----
+Title: Comparative Analysis of Gas Condensate Recovery by Carbon Dioxide Huff-N-Puff; Carbon Dioxide Alternating Nitrogen and Nitrogen Injection: A Simulation Study
+Authors: Nchila Yuven, Temitope Fred Ogunkunle, Oluwasanmi Ayodele Olabode, Rachael Joseph, Eniola opeyemi Bolujo, Sonia Nkongho
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211988-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Conventional methods to mitigate condensate banking is to inject water or dry gas which raises the reservoir pressure above the dew point. Unfortunately, these methods are inadequate as they lead to late response in achieving low drawdown pressures. This study utilizes the compositional module of Eclipse to build a lean gas reservoir with heterogeneous properties having maximum liquid loading of 6.32% and simulate CO2 and N2 injection scenarios. Comparative analysis on condensate and gas production from five case studies of CO2 huff-n-puff, CO2 cyclic injection, CO2 and, N2 continuous injection and the Gas Alternating Gas (CO2 and N2) are considered for 9 years of production. Parametric studies on the effects of injection and production rates, cyclic time and injection fluid composition investigated. N2, CO2, Cyclic, GAG, and CO2 huff, and puff injection cases resulted in oil recovery factors of 3.83%, 3.81%, 2.9%, 1.85% and 6.1% respectively.
+
+
+
+
+Keywords:
+sagd,
+machine learning,
+reservoir,
+artificial intelligence,
+upstream oil & gas,
+modeling & simulation,
+composition,
+stb,
+injection,
+condensate recovery
+
+
+Subjects: 
+Improved and Enhanced Recovery,
+Thermal methods
+
+
+
+
+Introduction
+
+
+The use of global energy has evolved significantly over the past 5 decades from coal through oil to being dominated using natural gas in the next 30 years. When the complete switch from crude oil to natural gas will occur, is still a debatable topic but some commenters suggest the age of natural gas has arrived (Hwang, 2011). Natural gas at present provides about 25% of the global energy supply and its desire is significantly increasing. From an environmental point of view, natural gas has also become a more desirable energy source as it is the cleanest of all the fossil fuels, relatively cheaper and abundant and proving stability of supply. Rapid increase in global demand for natural gas has led to growth of international gas trade, encouraging longer term contracts for its sales (Hwang, 2011).
+
+
+Natural gas resources are classified into wet, dry, condensate, and retrogrades based on hydrocarbon behavior and composition (Hasan, Mahmoud, & Al-majed, 2018). Condensates are low density with high API gravity liquid hydrocarbons phase that occur generally in association with natural gas. Gas condensate reservoirs initially are gas systems with initial temperature between the critical temperature and the cricondentherm. The development of gas condensate reservoirs is similar to the development of dry gas reservoirs. However, the significant differences are the flow of condensates in the reservoir occurs near the wellbore and, significant production of liquid over the life of the reservoir. Gas condensate reservoirs are known for their intricate thermodynamic behaviors and their highly complicated flow regimes (Gringarten & Al-Lamki, 2000). Three mobility zones can be identified in a condensate reservoir as shown in figure 1. The outer region far away from the wellbore with initial saturation and reservoir pressure high above the saturation pressure. The middle region where the reservoir pressure is below the saturation pressure, here the liquid will not flow since the saturation pressure is below the critical saturation pressure, and an inner region closer to the wellbore with higher condensate saturation with little gas mobility
+
+
+Figure 1View largeDownload slideRegions of condensate and gas flow behavior in a typical gas condensate reservoir (Zendehboudi, 2012).Figure 1View largeDownload slideRegions of condensate and gas flow behavior in a typical gas condensate reservoir (Zendehboudi, 2012). Close modal
+
+
+Generally, pressure reduction in a well should result into re-vaporizing of its liquid phase to become gas. However, with condensate reservoirs upon pressure reduction below the dew point, liquid condensates in the reservoir down to the well rather than vaporizing. Retrograde gas condensates can become a problem when the reservoir pressure is lower than the dew point value. Some of the many oil and gas fields with significant condensate blocking reported in literature include Arun field in North Sumatra (Afidick et al., 1994), Shell Oman experience a 67% loss in productivity for wells in two 12 fields (Rahimzadeh et al., 2016), The Cal Canal Field in California (Engineer, 1985).
+
+
+There are several widely applicable techniques that have been applied in producing from a gas condensate reservoir as shown in figure 2. Several authors have done works on various techniques as cited below:
+
+
+Pressure maintenance techniques such as: Huff-n-Puff (Sanaei et al., 2018; Wan and Mu, 2018), Gas cycling (Siddiqui et al., 2014; Adel et al., 2006; Nasiri et al., 2015), CO2 injection (Su et al., 2017; Fath and Dashtaki, 2016); Eremin & Fields, 2016; Wan and Mu, 2018; Yang et al., 2019), N2 injection (Mogensen and Xu, 2020); Linderman et al., (2008); Canchucaja & Sueiro (2018); Davarpanah et al., 2019).Productivity improvement: Horizontal (Dehane et al., 2000; Miller et al. 2010), Hydraulic fracturing (Kerunwa et al., 2020),Chemical injection: wettability alteration (Franco-Aguirre et al., 2018; Sheydaeemehr et al., 2014; Ali et at., 2019), solvent injection (Correa et al., 2008; Kumar et al., 2006).
+
+
+Figure 2View largeDownload slideCondensate reservoir development Techniques (Nchila et al., 2022)Figure 2View largeDownload slideCondensate reservoir development Techniques (Nchila et al., 2022) Close modal
+
+
+Due to pressure sensitivity producing these gas condensate reservoirs can become complicated. The conventional method of producing condensate reservoirs is to maintain the reservoir pressure and/or buttonhole pressure above the dew point pressure through water and/or gas injection or huff-n-puff however, some of the inherent problems with conventional approach are; late response to gas injection by the well, low achievable drawdown pressure and steady or slow decline in oil saturation around the wellbore by evaporation (Sheng, 2015). In the case huff-n-puff technique, a quick response is expected from gas injection. This is because the injected gas will increase the pressure around the producer well boosting the drive energy. The increased pressure may help in vaporizing the condensates around the producer well. However, there was a concern that the in gas injected during the huff period will be re-produced during the puff period (Sheng, 2015). According to Sheng (2015), the injected gas can be fully miscible with the in-situ oil, if the flowing bottom hole pressure is maintained above the minimum miscibility pressure; the viscosity of the oil will be decreased to a minimum and allowing maximum swelling of the oil.
+
+
+Several schemes of nitrogen and CO2 injection have been tested such as miscible N2 WAG in oil reservoirs (Greewalt et al, 1982), CO2 WAG (Jarrel et al, 2002), immiscible N2 WAG in oil reservoirs (Slack and Ehlich, 1981). In general, the studies on combined N2 and CO2 injection are rare. To solve the problems associated with the available developmental options, that is increasing the production drawdown and increasing liquid oil offtake, a Gas alternating gas (GAG) injection was applied proposed. In this study, a simulation approach was used to evaluate the proposed GAG injection, the performance of which compared with CO2 injection, huff-n-puff, N2 injection, CO2 cyclic injection then parametric analysis of the important operational variables will be carried out.
+
+
+Methodology
+
+
+In this study Schlumberger ECLIPSE 300 compositional simulator was used to model a gas condensate reservoir. The reservoir properties used for the simulation are listed table 1 are from a gas condensate reservoir in the Niger Delta, Nigeria.
+
+
+Table 1Reservoir and fluid properties Properties
+            . Values
+            . Units
+            . Initial reservoir pressure 5225 psia Reservoir temperature 212.7 F Fluid compressibility 9.08E-05 1/psia Dew point pressure 5225 psia Average porosity 24 % Average vertical permeability 456 md Average horizontal permeability 4566 md GOR 11274 scf/bbl. Density 0.3067 g/cm3 API gravity 47.3 API Fluid Molecular Weight 25.52 g/mol C7+ (%) 4.43 % Fluid Molecular Weight 25.52 g/mol Properties
+            . Values
+            . Units
+            . Initial reservoir pressure 5225 psia Reservoir temperature 212.7 F Fluid compressibility 9.08E-05 1/psia Dew point pressure 5225 psia Average porosity 24 % Average vertical permeability 456 md Average horizontal permeability 4566 md GOR 11274 scf/bbl. Density 0.3067 g/cm3 API gravity 47.3 API Fluid Molecular Weight 25.52 g/mol C7+ (%) 4.43 % Fluid Molecular Weight 25.52 g/mol View Large
+
+
+Reservoir grid description
+
+
+The cartesian grid is set into 195X63X81 grid cells in the X, Y and Z direction respectively making a total of 995085 grid cells. Each cell dimension in the grid model is given by 243ft, 252ft and 1.19ft which represents DX, DY and DZ respectively. The petrophysical properties such porosity (PORO), permeability (PERMX, PERMZ), active grids (ACTNUM) and net to gross (NTG) ratio, were also assigned to the reservoir grid model using inbuilt processors. The model is a heterogeneous reservoir model with varying reservoir porosity and permeability. The reservoir porosity ranges from 12% to 36% with an average of 24%. The average vertical and horizontal permeabilities are taken as 456md and 4566md respectively. The computational model simulator was designed based on the reservoir field units. The reservoir depth was not provided in the PVT report obtain and therefore a good estimate datum depth of 5490ft was used to initialize the model was used for this simulation. Seven gas producing wells P1, P2, P3, P4, P5, P6 and P7 located in the I J coordinates of (97, 21), (113, 15) (124, 30) (140, 23) (156, 31), (143, 38) and (170, 30) respectively and two injector wells G1 and G2 located in the I, J coordinates (107, 22) and (131,22) respectively on the reservoir grid model as shown in figure 3. All wells penetrate the grid blocks in the Z-direction. Table 1 and Table 2 shows the reservoir properties and the reservoir fluid composition.
+
+
+Figure 3View largeDownload slideReservoir model and well locationsFigure 3View largeDownload slideReservoir model and well locations Close modal
+
+
+Table 2Composition of reservoir fluid No
+            . Component
+            . Weight Fraction
+            .   Mol % 1 N2 0.07 2 CO2 1.81 3 C1 81.52 4 C2 6.18 5 C3 2.79 6 i-C4 0.62 7 n-C4 0.89 8 i-C5 0.43 9 n-C5 0.34 10 C6 0.93 11 C7 1.33 12 C8 0.89 13 C9 0.72 14 C10 0.34 15 C11 0.23 16 C12 0.15 17 C13 0.11 18 C14 0.11 19 C15 0.12 20 C16 0.07 21 C17 0.05 22 C18 0.05 23 C19 0.02 24 C20+ 0.23  Total 100 No
+            . Component
+            . Weight Fraction
+            .   Mol % 1 N2 0.07 2 CO2 1.81 3 C1 81.52 4 C2 6.18 5 C3 2.79 6 i-C4 0.62 7 n-C4 0.89 8 i-C5 0.43 9 n-C5 0.34 10 C6 0.93 11 C7 1.33 12 C8 0.89 13 C9 0.72 14 C10 0.34 15 C11 0.23 16 C12 0.15 17 C13 0.11 18 C14 0.11 19 C15 0.12 20 C16 0.07 21 C17 0.05 22 C18 0.05 23 C19 0.02 24 C20+ 0.23  Total 100 View Large
+
+
+Formation core Analysis
+
+
+SCAL processor contains correlations which are used to generate the relative permeabilities. SCAL enables a systematic application of the special core analysis available to one which will improve the results of the simulation. The Corey oil water saturation function is one of the 41 functions supplied in the SCAL processor which can be used to fit the gas-oil relative permeabilities. The Corey (1954) correlations (gas-oil-water) were used to generate relative permeabilities and saturation values for the gas-oil-water systems in the sand as shown in figures 4a and 4b.
+
+
+Figure 4View largeDownload slidesaturation functions vs Water saturation (a. Water/Oil b. Gas/Oil)Figure 4View largeDownload slidesaturation functions vs Water saturation (a. Water/Oil b. Gas/Oil) Close modal
+
+
+PVT Modelling
+
+
+The PVT data used for this study was based on recombined surface separator samples. ECLIPSE pre-processor PVTi was used for phase behavior calculation and to develop the fluid characteristics which were then exported into the ECLIPSE compositional model. PVTi contains facilities to allow for importing experimental data to an Equation of State (EOS), and finally generating the PVT tables for reservoir simulation studies. The PVT analysis obtained from the report was used as the basis for the synthetic reservoir fluid composition. The binary interaction coefficients and the critical parameters were turned to match the PVT analysis. The reservoir fluid components and weight fractions obtained from the laboratory was imported into the PVTi simulator. Fluid composition and weight fractions, saturation pressure, temperature, and weight fraction of the C20+ are all that was needed to fit the equation of state and to generate the Constant Composition Expansion (CCE), Constant Volume depletion and the optimized separator. Three-Parameter Peng-Robinson Equation of states and Lohrenz Bray and Clark viscosity correlations built into the reservoir simulator were used to generate PVT properties. Figure 5 shows the phase diagram for the reservoir fluid. Specific gas gravity, API gravity and reservoir temperature are the main input variables for these correlations. PVTi pre-processor was used to generate phase behavior and to develop the fluid characteristics and the results were exported into the ECLIPSE compositional model. The model is then initialized to calculate the initial conditions in the reservoir on the basis of hydrostatic equilibration.
+
+
+Figure 5View largeDownload slidePhase Envelop of the gas condensate fluid.Figure 5View largeDownload slidePhase Envelop of the gas condensate fluid. Close modal
+
+
+Simulation runs
+
+
+To be able to evaluate condensate blocking remediation, several numerical simulation models were built to describe the saturations and flow behavior of each phase and to model performance in terms of productivity. Five simulation runs were carried out for a period of 10 years.
+
+
+Case 1
+
+
+Natural Depletion
+
+
+In the base case, the reservoir model was initially allowed to produce under natural depletion using Eclipse 300 compositional simulator for 10 years. In the numerical simulation model, the producer wells were subjected to a minimum bottom hole flowing pressure. They result were then analyzed and further simulations carried out.
+
+
+After successful simulation of the base, several gas flooding scenarios were simulated. Two gas injector wells were introduced after the first year of gas production and various gas injection scenarios then modelled.
+
+
+Case 2
+
+
+Continuous injection of Carbon dioxide gas
+
+
+In this model CO2 was continuous injected into the reservoir to enhance condensate recovery. CO2 was continuously injected through the injection wells and production occurred simultaneously through the production wells. CO2 was used for this model to enhance condensate recovery due to its composition and miscibility ability requiring minimum miscibility pressure. The composition of CO2 is varied and the best composition that gives the highest recovery rate was chosen for further studies. The advantage of using CO2 is because it helps to extract heavier hydrocarbon components, its presence results in the expansion of the condensates and helps reduce the interfacial forces between the gas and the condensates. Hence, increasing the condensate mobility.
+
+
+Case 3
+
+
+Continuous injection of Nitrogen gas. Nitrogen was continuously injected into the reservoir purely without any other gas mixed with it. Nitrogen does not react due to its inert nature but when the injection is done at high pressures, it has the tendency to mix slightly with the oil resulting in a decrease in the oil viscosity.
+
+
+Case 4
+
+
+Carbon dioxide Huff-n-Puff
+
+
+The huff-n-puff technique is a multicyclic injection and production process. In this model CO2 gas was periodically injected into the reservoir to recover the condensates and enhance the gas productivity. During the huff period, one well was used for injection during the huff period and for production during the puff period. Each cycle consists of 1 year of injection and the followed by 1 year of soaking time and the 1 year of production. The huff-n-puff started at the end of the natural depletion.
+
+
+Case 5
+
+
+Gas Alternating Gas injection (GAG)
+
+
+The Gas Alternating Gas process is a new method developed and applied in this study using the ECLIPSE 300 compositional simulator to enhance condensate recovery for gas condensate reservoir. This technique involves the cycling process of injecting gas in alternating cycles followed by gas and repeating. The chosen gas for this proposed technique CO2 and N2. This method is based on the principle that due to gas segregation between the two gases, it will prevent the heavier gas to go to the top layers hence improving condensate recovery. One injector and one producer were used to test the Gas Alternating Gas technique to enhance condensate recovery. The Gas Alternating Gas cycle was fixed for 5 years.
+
+
+Operational procedures for each simulation case
+
+
+For various simulation cases describe above, the following simulation processes were performed.
+
+
+For the base case, the reservoir was allowed to produce under natural depletion for 10 years. A sensitivity analysis on three production rates of 6000Msc/day, 5000Mscf/day and 4000Mscf/day was carried out. The results were then exported on excel and plotted for graphical representation. After primary depletion of the well, two injector wells were then drilled, and gas injection carried out for various injection scenarios.Secondly effect of injection gas composition was investigated to determine the optimum injection gas composition to enhance the condensate recovery. Three different gas composition scenarios were investigated: 50% CO2 and 50% N2, 70% CO2 and 30% N2 and 30% CO2 and 70% N2. Results exported to excel spreadsheet for graphical representation.In the third case, three gas injection rates were tested to see the effect of CO2 and N2 injection rate on the cumulative condensate recovered, and gas produced. Three different injection rates, 5000Mscf/day, 4500Mscf/day, and 4000Mscf/day were tested for the N2 and CO2 gas and the optimum injection rate for each case taken and used subsequently for the huff-n-puff and the gas alternating gas Case. The production rate was set at 6000Mscf/day. The results were exported to excel spreadsheet and plotted for graphical representationThe fourth case. The effect of cyclic time was investigated. This involved cyclic injection of CO2. Two cyclic time intervals were investigated: one-year cyclic injection and two years of cyclic injection repeatedly for a period of 9 years in each case.Finally, the proposed Gas Alternating Gas techniques for CO2 and N2 was simulated. This made used of the optimum recovery values of composition, flowrates, earlier obtained. Production rate was set at 6000Mscf/day. The GAG was done for a period of 9years. Results of the simulation are presented in the next chapter.
+
+
+Results and discussion
+
+
+Constant Composition Expansion (CCE) and Constant Volume Depletion results
+
+
+The saturation pressure value was determined to be 5225 psia at the reservoir temperature of 212.7 F. The results showed that the reservoir is lean gas condensate reservoir with a maximum liquid dropout of 6.32% at 1515 psia. The Figure 6.1 through 6.4 below shows the variation of retrograde liquid, relative volume, gas density and deviation factor respectively with pressure.
+
+
+Figure 6. 1View largeDownload slideRetrograde Liquid Volume Versus pressure at 212.7 FFigure 6. 1View largeDownload slideRetrograde Liquid Volume Versus pressure at 212.7 F Close modal
+
+
+Figure 6. 2View largeDownload slidePlot of relative volume Versus pressure at 212.7 FFigure 6. 2View largeDownload slidePlot of relative volume Versus pressure at 212.7 F Close modal
+
+
+Figure 6. 3View largeDownload slidePlot of Gas density Versus pressure at 212.7 FFigure 6. 3View largeDownload slidePlot of Gas density Versus pressure at 212.7 F Close modal
+
+
+Figure 6. 4View largeDownload slidePlot of Z-factor Versus pressure at 212.7 FFigure 6. 4View largeDownload slidePlot of Z-factor Versus pressure at 212.7 F Close modal
+
+
+After the initialization of the reservoir model using the eclipse compositional simulator, the results gave the initial volume of gas, oil, and water in place in the reservoir. The volume of gas in the reservoir is given as 39,692,535 rb, and that of the initial water is 110029840 rb. Since the reservoir is initially at/above the reservoir pressure of 5225 psia, there is no oil avail at this point. 56,405,517Mscf of gas can be produced from the reservoir. The results at the initial state of the reservoir are presented in the table 3 below. At the initial reservoir conditions, the gas saturation values range from 0 in some areas of the reservoir to a maximum value of 0.609. This represents areas of high gas saturations
+
+
+Table 3Initial reservoir fluid in place. RESERVOIR CONDITION
+            . (SURFACE CONDITION)
+            . Region
+            . Oil (Res Vol) (rb)
+            . Water (Res Vol) (rb)
+            . Gas (Res Vol) (rb)
+            . Water (Sur Vol) (stb)
+            . Oil (wrt-Separator) (stb)
+            . Gas (wrt separator) (Mscf)
+            . Field 0 110029840 39692535 108311020 5310437.6 56405517 RESERVOIR CONDITION
+            . (SURFACE CONDITION)
+            . Region
+            . Oil (Res Vol) (rb)
+            . Water (Res Vol) (rb)
+            . Gas (Res Vol) (rb)
+            . Water (Sur Vol) (stb)
+            . Oil (wrt-Separator) (stb)
+            . Gas (wrt separator) (Mscf)
+            . Field 0 110029840 39692535 108311020 5310437.6 56405517 View Large
+
+
+Natural depletion
+
+
+In the base case the reservoir was allowed to produce using the natural energy of the reservoir. No gas injection was carried out. Seven gas producer wells each with an ID of 0.5ft were drilled into the reservoir. The field was simulated to produce for 10years. The production rate was set at 6000Mscf/day. The results showed that 21,954,718Mscf of gas and 3,294,832.3 stb of oil was recovered during the natural depletion with a recovery factor of 61.99%. Figure 7.1, 7.2 and 7.3 represents the FGPT, FOPT, RF, respectively for the base case.
+
+
+Figure 7.1View largeDownload slideField gas production TotalFigure 7.1View largeDownload slideField gas production Total Close modal
+
+
+Figure 7.2View largeDownload slideField oil Production TotalFigure 7.2View largeDownload slideField oil Production Total Close modal
+
+
+Figure 7.3View largeDownload slideRecovery factorFigure 7.3View largeDownload slideRecovery factor Close modal
+
+
+Nitrogen injection
+
+
+Nitrogen was injected continuously into the reservoir at a rate of 5000Mscf/day for a period of 9 years while production was carried out simultaneously. The gas production rate was set at 6000Mscf/day. The results showed that 46,913,476 Mscf and 3,512821.3 stb of gas and oil were produced respectively. The oil recovery factor for this case was 65.82 %. The injection of nitrogen results into an additional 217,989 stb of oil produced. That is a 3.81% incremental oil was produced compared to the base case. Figure 8.1, Figure 8.2 and 8.3 represents the FGPT, FOPT, RF, respectively for nitrogen injection.
+
+
+Figure 8.1View largeDownload slideField gas production otalFigure 8.1View largeDownload slideField gas production otal Close modal
+
+
+Figure 8.2View largeDownload slideField oil production totalFigure 8.2View largeDownload slideField oil production total Close modal
+
+
+Figure 8.3View largeDownload slideRecovery factorFigure 8.3View largeDownload slideRecovery factor Close modal
+
+
+Injection of carbon dioxide
+
+
+After a successful simulation of nitrogen injection. Carbon dioxide was injected into the reservoir at an injection rate of 5000Mscf/day and the gas production rate of 6000Mscf/day for 9 years. Production was carried out simultaneously with the injection process. Results showed that 46,913,476 Mscf of gas and 3,512,821.3 stb of oil were produced respectively with a recovery factor of 65.8%. A 3.81% incremental oil (condensate) was produced which is about 217,989 stb compared to the base case. Figure 9.1, figure 9.2 and figure 9.3 below represents the FGPT, FOPT, and RF respectively for CO2 injection.
+
+
+Figure 9.1View largeDownload slideField gas production totalFigure 9.1View largeDownload slideField gas production total Close modal
+
+
+Figure 9.2View largeDownload slideField oil production totalFigure 9.2View largeDownload slideField oil production total Close modal
+
+
+Figure 9.3View largeDownload slideRecovery factorFigure 9.3View largeDownload slideRecovery factor Close modal
+
+
+Carbon dioxide Huff-n-Puff
+
+
+After the primarily depleting the reservoir, it was the subjected to a huff and puff period of 9 years. Huff-n-puff refers to injection of gas during the huff period followed by shutting in the well to allow for soaking and then followed by a production period in which the producer well is opened for production. CO2 was injected at a rate of 5000Mscf/day and gas production carried out at 6000Mscf/day. Three cycles of huff-n-puff were carried out. Each cycle consisted of a one-year huff period, one-year soaking period and one-year puff period. The results from the simulation showed that 26,087,856 Mscf of gas and 3,669,426.5 stb of oil were produced respectively with a recovery factor of 68.09 %. A 6.1 % incremental volume of condensate was produced which is about 374,594.5 stb compared to the base case. Figure 10.1, 10.2, 10.3, 10.4, 10.5 and 10.6 below represents the FGPT, FOPT, RF, FOPR, FGPR and FGIT respectively for CO2 huff-n-puff model.
+
+
+Figure 10.1View largeDownload slideField gas production totalFigure 10.1View largeDownload slideField gas production total Close modal
+
+
+Figure 10.2View largeDownload slideField oil production totalFigure 10.2View largeDownload slideField oil production total Close modal
+
+
+Figure 10.3View largeDownload slideRecovery factorFigure 10.3View largeDownload slideRecovery factor Close modal
+
+
+Figure 10.4View largeDownload slideField gas production rateFigure 10.4View largeDownload slideField gas production rate Close modal
+
+
+Figure 10.5View largeDownload slideField oil production rateFigure 10.5View largeDownload slideField oil production rate Close modal
+
+
+Figure 10.6View largeDownload slideField gas injection totalFigure 10.6View largeDownload slideField gas injection total Close modal
+
+
+Gas Alternating Gas injection
+
+
+The Gas Alternating Gas (GAG) involved the cycling process of injecting gas in alternating cycles followed by gas and repeating. In this simulation case, CO2 and N2 were injected in alternating cycles of year each repeatedly. The injection started after one year of natural production. The GAG model was carried out for 9 years. The results showed that 63.84 % of the residual oil can be recovered using this technique. The gas production total obtained is 33,127,802 Mscf. The volume of gas injected is 14,607,243 Mscf. This technique of injection gives a 1.85% increment in the volume of oil recovered when compared to the base. It the volume of gas injected is about half the volume of gas injected in the other cases. This indicates that GAG is economical when compared to the other models of injection. Figure 11.1, 11.2, 11.3, 11. 4, 11.5 and 11.6 below represents the FGPT, FOPT, RF, FGPR, FOPR, and FGIT respectively for the GAG model.
+
+
+Figure 11.1View largeDownload slideField gas production totalFigure 11.1View largeDownload slideField gas production total Close modal
+
+
+Figure 11.2View largeDownload slideField oil production totalFigure 11.2View largeDownload slideField oil production total Close modal
+
+
+Figure 11.3View largeDownload slideRecovery factorFigure 11.3View largeDownload slideRecovery factor Close modal
+
+
+Figure 11.4View largeDownload slideField Gas production rateFigure 11.4View largeDownload slideField Gas production rate Close modal
+
+
+Figure 11.5View largeDownload slideField oil production rateFigure 11.5View largeDownload slideField oil production rate Close modal
+
+
+Figure 11.6View largeDownload slideField gas injection totalFigure 11.6View largeDownload slideField gas injection total Close modal
+
+
+Sensitivity analysis
+
+
+Effect of Production rate
+
+
+Three different production rates of 6000Mscf/day, 5000Mscf/day and 4000Mscf/day were simulated to see the variation in volume of oil and gas produced. Results showed that increasing the production rate increase the volume of oil and gas produced. However, the difference in the recovery factor is very small compared to the volume of oil recovered. The recovery factors for the three production rates are 61.99%, 61.87% and 61.49% respectively with an average difference of 0.0033%. The was however significant difference in the volume of water produced. The volume of water produced for the 6000Mscf/day, 5000Mscf/day and 4000Mscf/day are 1202977 stb, 1185884 stb and 1152954 stb respectively. Increasing the production rate let to an increase in volume of water being produced. Figure 12.1, 12.2, 12.3 and 12.4, below represents the FGPT, FOPT, RF, FWPT and FGIT for the three production rates.
+
+
+Figure 12.1View largeDownload slideField oil production totalFigure 12.1View largeDownload slideField oil production total Close modal
+
+
+Figure 12.2View largeDownload slideField gas production totalFigure 12.2View largeDownload slideField gas production total Close modal
+
+
+Figure 12.3View largeDownload slideField oil production rateFigure 12.3View largeDownload slideField oil production rate Close modal
+
+
+Figure 12.4View largeDownload slideRecovery factorFigure 12.4View largeDownload slideRecovery factor Close modal
+
+
+Effect of injection rate
+
+
+Sensitivity analysis of three different injection rates was carried out for CO2 injection and N2 injection. The gas injection rates into the wells were done at 5000Mscf, 4500Mscf and 4000Mscf. Results for the CO2 injection case, injecting at a rate of 4500 Mscf gave the highest oil recovery. About 3515240 stb of condensates was recovered giving a 3.84 % of incremental volume of condensates recovered compared to the 3.81% and 3.34% obtained from the 5000Mscf/day and 4000Mscf/day injection rates. For the N2 injection case, injecting at a rate of 5000Mscf gave the highest oil recovery. About 3512821.3 stb of condensates was recovered giving a 3.82 % of incremental volume of condensates recovered compared to the 3.65% and 3.34% obtained from the 4500Mscf/day and 4000Mscf/day injection rates as summarized in table 4.
+
+
+Table 4Comparison of injection rates 
+            . Nitrogen
+            . Carbon dioxide
+            . Injection rate
+            . FGPT
+            . FOPT
+            . RF
+            . FGPT
+            . FOPT
+            . RF
+            . Mscf/day
+            . Mscf
+            . rb
+            . %
+            . Mscf
+            . rb
+            . %
+            . 5000 46913476 3512821.3 65.81 46913476 3512821.3 65.8 4500 44608596 3502411.8 65.64 44966848 3515240 65.83 4000 42518348 3486887.9 65.33 42518348 3486879 61.99 
+            . Nitrogen
+            . Carbon dioxide
+            . Injection rate
+            . FGPT
+            . FOPT
+            . RF
+            . FGPT
+            . FOPT
+            . RF
+            . Mscf/day
+            . Mscf
+            . rb
+            . %
+            . Mscf
+            . rb
+            . %
+            . 5000 46913476 3512821.3 65.81 46913476 3512821.3 65.8 4500 44608596 3502411.8 65.64 44966848 3515240 65.83 4000 42518348 3486887.9 65.33 42518348 3486879 61.99 View Large
+
+
+Effect of injection gas composition
+
+
+A sensitivity analysis of the injection gas composition was carried out for the CO2 and N2 injection. Various injection gas composition was investigated to in order to evaluate the impact on the volume of condensate recovered. Table below shows the various injection gas compositions investigated. Results show that there was no significant increase in the volume of oil and gas produce as a result of changing the composition of each injection gas. It is however the volume of the injected gas that produces significant change in the volume of oil and gas recovered. Table 5 below represents the FGPT, FOPT for the three injection gas compositions.
+
+
+Table 5Comparison of different injection gas composition rates. 
+            . Composition
+            . FOPT
+            . FGPT
+            . Oil Incremental
+            . Run
+            . CO2
+            . N2
+            . stb
+            . Mscf
+            . %
+            . 1 0.7 0.3 3467340 33127802 1.85 2 0.5 0.5 3467340 33127802 1.85 3 0.3 0.7 3467340 33127802 1.85 
+            . Composition
+            . FOPT
+            . FGPT
+            . Oil Incremental
+            . Run
+            . CO2
+            . N2
+            . stb
+            . Mscf
+            . %
+            . 1 0.7 0.3 3467340 33127802 1.85 2 0.5 0.5 3467340 33127802 1.85 3 0.3 0.7 3467340 33127802 1.85 View Large
+
+
+Effect of cyclic time
+
+
+A sensitivity analysis was carried out on the CO2 injection cycle time. The injection rate was set at 5000Mscf/day and the production rate set at 6000Mscf/day for the two cases. Two injection cyclic times as shown in table 6 for CO2 injection were investigated. First, a one-year injection repeatedly and the in another case two years of cyclic injection repeatedly each for a period of 9 years. A 2.9 % increment in the volume of oil produced was observed for the 2 years repeated cyclic injection and a 2.31% oil increment obtained for a 1year repeated cyclic injection when compared to the base case. Results from the simulation indicates that increasing the cyclic time increases the volume of oil and gas produced. Figure 13.1, 13.2, 13.3, and 13.4 below represents the FGPT, FOPT, Field water cut and RF respectively for the two cyclic time of injection.397
+
+
+Table 6Comparison of two cyclic time for injection. Cyclic (Time)
+            . FOPT (stb)
+            . FGPT (Mscf)
+            . RF
+            . Incremental oil (%)
+            . 1yr 3483906 34310692 64.3 2.31 2yrs 3484829 34874156 64.89 2.9 Cyclic (Time)
+            . FOPT (stb)
+            . FGPT (Mscf)
+            . RF
+            . Incremental oil (%)
+            . 1yr 3483906 34310692 64.3 2.31 2yrs 3484829 34874156 64.89 2.9 View Large
+
+
+Figure 13.1View largeDownload slideField oil production totalFigure 13.1View largeDownload slideField oil production total Close modal
+
+
+Figure 13.2View largeDownload slideField gas production totalFigure 13.2View largeDownload slideField gas production total Close modal
+
+
+Figure 13.3View largeDownload slideField water cutFigure 13.3View largeDownload slideField water cut Close modal
+
+
+Figure 13.4View largeDownload slideRecovery factorFigure 13.4View largeDownload slideRecovery factor Close modal
+
+
+Figure 14View largeDownload slideResults comparison of various development options.Figure 14View largeDownload slideResults comparison of various development options. Close modal
+
+
+Table 7Summary of sensitivity analysis. No
+            . Case
+            . Production rate (Mscf/day)
+            . Injection rate (Mscf/day)
+            . FOPT (stb)
+            . FGPT (Mscf)
+            . FOE (%)
+            . FWPT (stb0
+            . FGIT (stb)
+            . Oil Increment (stb)
+            . Percentage Increase (%)
+            . Barrels of oil equivalence BOE
+            .   6000 - 3294832.3 21954718 61.99 1202977 - - - 6953951.967 1 Base Case 5000 - 3288432.5 21966472 61.87 1185884 - - - 6949511.167   4000 - 3267462.5 21960190 61.49 1152954 - - - 6927494.167    5000 3512821.3 46913476 65.81 1470030 29220000 217989 3.82 11331733.97 2 N2 injection  4500 3502411.8 44608596 65.64 1403297 25865772 207579.5 3.65 10937177.8    4000 3486887.9 42518348 65.33 1408819 23376000 192055.6 3.34 10573279.23    5000 3512821.3 46913476 65.8 1470030 29220000 217989 3.81 11331733.97 3 CO2 injection 6000 4500 3515240 44966848 65.83 1429196 26298000 220407.7 3.84 11009714.67    4000 3486879 42518348 65.33 1408819 23376000 192046.7 3.34 10573270.33 4 CO2 1yr cyclic   3483905.5 34310692 64.3 1551781 14596690 189073.2 2.31 9202354.167 5 CO2 2yr cyclic  5000 3484828.5 34874156 64.89 1576836 14598451 189996.2 2.9 9297187.833 6 GAG (CO2 @ N2)   3467339.8 33127802 63.84 1575943 14607243 172507.5 1.85 8988640.133 7 CO2 huff n puff   3669426.8 26087856 68.09 1798887 5480000 374594.5 6.1 8017402.8 No
+            . Case
+            . Production rate (Mscf/day)
+            . Injection rate (Mscf/day)
+            . FOPT (stb)
+            . FGPT (Mscf)
+            . FOE (%)
+            . FWPT (stb0
+            . FGIT (stb)
+            . Oil Increment (stb)
+            . Percentage Increase (%)
+            . Barrels of oil equivalence BOE
+            .   6000 - 3294832.3 21954718 61.99 1202977 - - - 6953951.967 1 Base Case 5000 - 3288432.5 21966472 61.87 1185884 - - - 6949511.167   4000 - 3267462.5 21960190 61.49 1152954 - - - 6927494.167    5000 3512821.3 46913476 65.81 1470030 29220000 217989 3.82 11331733.97 2 N2 injection  4500 3502411.8 44608596 65.64 1403297 25865772 207579.5 3.65 10937177.8    4000 3486887.9 42518348 65.33 1408819 23376000 192055.6 3.34 10573279.23    5000 3512821.3 46913476 65.8 1470030 29220000 217989 3.81 11331733.97 3 CO2 injection 6000 4500 3515240 44966848 65.83 1429196 26298000 220407.7 3.84 11009714.67    4000 3486879 42518348 65.33 1408819 23376000 192046.7 3.34 10573270.33 4 CO2 1yr cyclic   3483905.5 34310692 64.3 1551781 14596690 189073.2 2.31 9202354.167 5 CO2 2yr cyclic  5000 3484828.5 34874156 64.89 1576836 14598451 189996.2 2.9 9297187.833 6 GAG (CO2 @ N2)   3467339.8 33127802 63.84 1575943 14607243 172507.5 1.85 8988640.133 7 CO2 huff n puff   3669426.8 26087856 68.09 1798887 5480000 374594.5 6.1 8017402.8 View Large
+
+
+Figure 4.60 below shows the tornado plot for the highest influencing parameter for increasing the volume of fluid produced. Results from the plot indicates that, it is preferable to optimize the production rate to increase the volume of gas produced. In the case of improved oil recovery, optimization of the injection rate is preferable in producing more of the condensates.
+
+
+Figure 15.aView largeDownload slideTornado diagram versus FGPT (Mscf)Figure 15.aView largeDownload slideTornado diagram versus FGPT (Mscf) Close modal
+
+
+Figure 15bView largeDownload slideTornado diagram versus FOPT (stb)Figure 15bView largeDownload slideTornado diagram versus FOPT (stb) Close modal
+
+
+Conclusion
+
+
+Various gas injection techniques for gas condensate reservoir development were simulated for a heterogeneous reservoir model using the ECLIPSE 300 compositional processor. Five gas injection cases were simulated: N2 continuous injection, CO2 continuous injection, CO2 huff-n-puff and Gas Alternating Gas injection (CO2 alternating N2). Seven producer wells were drilled, and 2 injectors used for the simulation. A study of some parameters that affect gas productivity and condensate recovery carried out. These parameters include the production rate, gas injection rate, cyclic time, and composition of the injection gas.
+
+
+The PVTi results confirms a lean gas condensate reservoir with a maximum liquid loading of 6.32% and saturation pressure of 5225 F.The base case general productivities without any injection carried out were compared by the cumulative field gas production total as a percentage of the gas initially in place (GIIP) under the same reservoir dimensions. The results showed a cumulative recovery of 61.99% of the gas initially in place (GIIP) for a 6000Mscf/day of production rate.Comparing the recoveries for different production rates, the results showed that the higher the production rate the higher the volume of oil and gas produced. For production rates for 6000Mscf/day, 5000Mscf/day, and 4000Mscf/day the cumulative recoveries respectively are 61.99%, 61.87% and 61.49% of GIIP. However, increasing the production rates leads to an increase in the volume of water produced.Continuous injection of nitrogen resulted in the enhancement of both the condensate and gas recovery. The improvement of both the gas and condensate recovery range from 3.34% to 3.82%.The continuous injection of CO2, model resulted in gas and condensate recoveries in the range of 3.34% to 3.81% of incremental volumes produced. It was observed that equal volume of CO2 and N2 in the continuous injection model produced equal volumes of fluid. The recoveries were more sensitive when increasing the injection rates.The Gas Alternating Gas approach showed comparable results with a 1.85 % incremental volume of gas and condensates produced. This model resulted in the least volume of water produced and a small volume of gas injected when compared to other cases.It was found that the injection gas composition does not affect the volume of gas and condensate produced very much. This indicates that the miscibility of the injection gas with the reservoir fluid does not depend on the composition. The miscibility could be dependent on the reservoir temperature and not the composition.The huff-n-puff model resulted in both incremental gas and condensate produced. This model produced the highest volume of condensates compared to all the cases. A 6.1% increment in the volume of condensate produced was obtained. However, in resulted in the small volumes of gas produced. This is because for the huff-n-puff model, there is enough soaking time for the injected gas to mixed with the condensates there by reducing its viscosity allowing it to the easily lifted to the surface.Th price of oil and gas in the market could be the determining factor in which of the recovery technique to adopt. When the choice of fluid to be produce is gas, then continuous injection of N2 and continuous injection of CO2 is preferable due to higher gas production total. Likewise, if the case where oil production is the choice of fluid to be produced due to increasing oil prices, CO2 huff-n-puff, Gas alternating Gas, and cyclic injection of CO2 is recommended.
+
+
+Area of further research
+
+
+Proper characterization of reservoir and fluid parameters as well as the operational environments is essential. This will improve the equation of state and tuning methods for lean gas condensate fluids. Hence reducing the uncertainty in describing the phase behavior of lean gas condensate fluids and injection fluids.
+
+
+Given the size of the reservoir in this study, it is recommended to simulate for longer time of depletion of the reservoir, this will provide more time to properly deplete the reservoir and so higher CPU power is necessary for faster and proper calculations of the reservoir grid fluid and rock properties for better results.
+
+
+For the GAG technique, it is also recommended to carry out the GAG starting with nitrogen injection first and the compare the results with those obtained in this study. This will permit proper optimization of the gas productivity.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion FOE: Field oil efficiency FOPT: Field oil production total FGPT: Field gas production total FWPT: Field water production total FWCT: Field water cut
+
+
+References
+
+
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G., Putney, K., Lawrence, J., Gallat, M., & Hohensee, K. (2008, November). Feasibility study of substituting nitrogen for hydrocarbon in a gas recycle condensate reservoir. In Abu Dhabi International Petroleum Exhibition and Conference. OnePetro.Google Scholar Miller, N., Nasrabadi, H., & Zhu, D. (2010, June). Application of horizontal wells to reduce condensate blockage in gas condensate reservoirs. In International Oil and Gas Conference and Exhibition in China. OnePetro.Google Scholar Mogensen, K., & Xu, S. (2020). Comparison of three miscible injectants for a high-temperature, volatile oil reservoir-With particular emphasis on nitrogen injection. Journal of Petroleum Science and Engineering, 195, 107616.Google ScholarCrossrefSearch ADS  NasiriGhiri, M., Nasriani, H. R., Sinaei, M., Najibi, S. H., Nasriani, E., & Parchami, H. (2015). Gas injection for enhancement of condensate recovery in a gas condensate reservoir. 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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211988-MS
+
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+

files/2022/Comparative Study of Predictive Models for Permeability from Vertical wells using Sequential Gaussian Simulation and Artificial Neural Networks.txt

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+----- METADATA START -----
+Title: Comparative Study of Predictive Models for Permeability from Vertical wells using Sequential Gaussian Simulation and Artificial Neural Networks
+Authors: Oluwatosin John Rotimi, Ayodeji Michael Akande, Betty Ihekona, Oseremen Iyamah, Somto Chukwuka, Yao Liang, Wang Zhenli, Oluwatoyin Ologe
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211987-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+This study attempts to estimate permeability from well logs data and also predict values from existing rock sections to points that are missing using Artificial Neural Network (ANN) and Sequential Gaussian Simulation (SGS). Potentially, exploration data is prone to trends that are initiated by the sedimentation process, but a detrending method using Semi-variogram (vertical) algorithm was applied to remove this from the interpreted wells which are all vertical. Permeability modeled for ANN gave an estimated root mean square error (RMSE) of 0.0449, while SGS gave RMSE of 0.1789, both giving a ‘K’ range of 100 – 1000 mD. Although the spatial geology of the area was relegated and not considered, making a spatial prediction influenced from the temporal reference point un-assessable. However, the independent prediction on the overall result shows a better prediction from the ANN, perhaps due to the optimization algorithm used.
+
+
+
+
+Keywords:
+neural network,
+prediction,
+permeability,
+flow in porous media,
+upstream oil & gas,
+machine learning,
+sequential gaussian simulation,
+dataset,
+predictive model,
+algorithm
+
+
+Subjects: 
+Reservoir Fluid Dynamics,
+Information Management and Systems,
+Flow in porous media,
+Neural networks
+
+
+
+
+INTRODUCTION
+
+
+The petrophysical properties which are of great importance to oil and gas exploration and production (e.g. permeability. Saturation, resistivity etc.) are as a result of complex physical and chemical processes (Zhao et al., 2014). These petrophysical properties are essential to understanding the behavior of the reservoir rock and help to ascertain the volume of reservoir fluids present. The physical and chemical processes that occur during the deposition of sediments, result in significant spatial signatures on the properties of the reservoir rock and are used to define how the reservoir properties are spatially correlated for efficient reservoir characterization and hydrocarbon production.
+
+
+Spatial correlation helps to quantify the variability of reservoir rock properties over a great distance and at particular directions. Determining spatial information involves comparing a sample data value at one location with values of the same attribute at other locations. According to Chaki et al., (2018), a process used to quantitatively describe reservoir properties as they vary spatially using available field data is termed reservoir characterization. It has been identified that things closer together tend to have similar properties than things that are farther apart. This is a fundamental geophysical principle (Zhang, 2009).
+
+
+The petrophysical properties to be studied are obtained from geophysical surveys or well logs acquired during drilling. Due to the spatial component, the unknown petrophysical properties can be estimated using geostatistical techniques. The application of geostatistical tool is used to estimate and populate the spatial distribution of petrophysical properties at unsampled locations at different wells (Korjani et al., 2016; Rotimi et al., 2016).
+
+
+The aim of the study dwells on populating petrophysical parameters using geostatistical techniques and implementing a machine learning algorithm for better reservoir characterization.
+
+
+ARTIFICIAL NEURAL NETWORK (ANN)
+
+
+A neural network is a model built after the workings of the brain's architecture. It is composed of a large number of highly coupled processing components resembling neurons, which are connected via weighted connections resembling synapses. A neural network is used to predict values using chosen well log data via a nonlinear regression algorithm. Because neural networks are capable of adapting to severely non-linear issues, they have been used to examine and evaluate reservoirs over time (Fegh et al., 2013). As a result, neural networks are increasingly being utilized to forecast reservoir characteristics based on well log data.
+
+
+The artificial neural network (ANN) model for forecasting petrophysical properties such as shale volume, porosity, and permeability was developed by using well log dataset. The neural network toolbox in MATLAB is used to build, construct, visualize, and simulate the models.
+
+
+The spontaneous potential log readings and resistivity log readings serve as input variable while petrophysical property, porosity and permeability are the target (output) data in the ANN network. The model was built using Levenberg-Marquardt (LM) optimization algorithm in MATLAB, the reason being that it reduces the sum of the error function (Okon et al., 2020). The ANN model used a controlled learning process, it takes the input data which is spontaneous potential (SP) and resistivity log readings to predict the target porosity and permeability data. The workflow is presented in Figure 1.
+
+
+METHODOLGY
+
+
+The Geostatistical techniques of this study employed Variography which resolves directional variance in property significance. According to Gringarten & Deutsch, (1999), the most extensively used tool for modeling porosity, lithofacies, and other petrophysical parameters in terms of spatial variability is the variogram. Semivariograms are used to measure the similarity or dissimilarity of sample values as a function of sample location distances (Hosseini et al., 2019). The mathematical expression for semivariogram is expressed as:
+
+
+γ(h)=12E[Z(Xi)−Z(Xi+h)]2(1)
+
+
+Where Z(Xi) and Z(Xi + h) are referred as values of the sampled variable at location Xi and Xi + h separated by a vector h.
+
+
+Figure 1View largeDownload slideWorkflow used for designing ANN modelFigure 1View largeDownload slideWorkflow used for designing ANN model Close modal
+
+
+Variogram analysis accounts for stationarity of the property (removing trends) and ascertains the heterogeneous nature of the reservoir. Before variogram modeling and geostatistical simulation, any data that demonstrates a systematic redundancy must be removed. Accurate estimates of variogram are needed for reliable prediction by any of the geostatistical techniques to be employed. Accurate variogram analysis depends on the size of the sample, the number of lags at which it is estimated, lag distance, anisotropy and trend (El Khadragy et al., 2017; Rotimi et al., 2014b).
+
+
+There are features of variogram that must be considered for reliable prediction output. This includes the range, the sill and nugget. The sill, which is also known as the amplitude of a certain component of the variogram, is the total variance at which the empirical variogram appears to level off. The amount of spatial correlation reduces as separation distance increases, until there is no spatial correlation at all; this is referred to as the correlation range. Nugget refers to the intersection on the vertical axis, it represents the variability at distances smaller than the typical sample spacing.
+
+
+Using PETREL software, the variogram plots semi-variance against lag distance. When modelling the semivariogram, the variogram features sill, nugget and range are calculated in the two horizontal directions (major & minor) of each variable.
+
+
+Geostatistical Simulation
+
+
+Spatial variation parameters listed above and recovered from variogram analysis are plugged into the geostatistical modelling algorithm to conduct the spatial simulation of the selected petrophysical properties. The goal of geostatistical modelling is to create a three dimensional model that preserves reservoir heterogeneity and does not truncate intrinsic petrophysical parameters (Zhao et al., 2014; Rotimi et al., 2014a).
+
+
+These properties are populated within the boundaries of the wells that have previously been specified. The data to be used for geostatistical modeling must typically fulfill two essential requirements; the variables must generally be stationary (i.e. they must be adjusted for any trend and eliminated if there is any), and the data must generally be regularly distributed (Hosseini et al., 2019). Geostatistical simulation algorithms such as sequential gaussian simulation (SGS) and co-simulation were utilized to populate the petrophysical parameter in this study.
+
+
+Sequential Gaussian Simualtion (SGS)
+
+
+This results in a subsurface model of rock and fluid properties, using stochastic techniques which samples random field data with continuous qualities assuming a framework of spatial correlation (Grana & Azevedo, 2021). It is called sequential because the procedure is continually repeated for each grid point, where a simulated value from a distribution function derived from previously simulated values in the neighborhood of this site is generated at each unsampled location. (Fegh et al., 2013; Rotimi et al., 2014b). SGS demands that the input data (i.e. the upscaled data) have a mean of zero and a standard deviation of one; this suggests a normal distribution transformation. In general, the technique generates a property with a conventional normal distribution, and if the input data are not normally distributed, the outcomes will differ from the input. The semivariogram's output parameters (sill, range, and nugget) are used as input for SGS modeling, and it is critical that the data is normally distributed to prevent producing erroneous results. For conditional sequential gaussian simulation a secondary variable is introduced. When modelling with gaussian co-simulation, the primary variable and the secondary variable must exhibit linear relationship. This means that the primary and secondary variable must have relatable properties before it could be considered to use for co-simulation. Prior to this study, co-simulation was implemented to populate porosity and permeability across the entire reservoir, Vshale serves as the primary data input used for petrophysical modelling of porosity and permeability across the reservoir.
+
+
+ARTIFICIAL NEURAL NETWORK MODEL PERFORMANCE
+
+
+The model was trained multiple times to guarantee consistency. So, a three-layer feed-forward ANN model with 13 neurons in the hidden layer was created. Figure 2 shows the network architecture with Inputs, hidden layer neurons, and target output. Table 1 presents the model's training parameters. The Levenberg-Marquardt training optimization algorithm yielded a mean square error (MSE) of 0.002 at 106 epochs (iterations).
+
+
+Figure 2View largeDownload slideArchitecture of the modelFigure 2View largeDownload slideArchitecture of the model Close modal
+
+
+Table 1Developed parameters of the ANN model STRUCTURE OF THE DATASET
+            . VALUES
+            . Training dataset 6945 (70 % of datasets) Validation dataset 2976 (30% of datasets) Number of input neurons 2 (Spontaneous potential, Resistivity values) Hidden layer used 1 Neurons in hidden layer 13 Number of output 2 (porosity, φ and permeability, k) Learning algorithm (trainlm) Levenberg-Marquardt STRUCTURE OF THE DATASET
+            . VALUES
+            . Training dataset 6945 (70 % of datasets) Validation dataset 2976 (30% of datasets) Number of input neurons 2 (Spontaneous potential, Resistivity values) Hidden layer used 1 Neurons in hidden layer 13 Number of output 2 (porosity, φ and permeability, k) Learning algorithm (trainlm) Levenberg-Marquardt View Large
+
+
+PETROPHYSICAL PROPERTY PREDICTION
+
+
+The most dependable model can then be used to populate the petrophysical properties of different wells after it has been determined and carefully evaluated. Considering the model was chosen based on a variety of statistical analyses. This stage is to generalize the findings of the study by using the ANN model to predict porosity and permeability in unsampled wells throughout the study area. Unlike geostatistical modelling, property prediction using ANN model does not assume stationarity on reservoir properties, but captures linear and non-linear relationship between the input and output variables. For prediction of petrophysical properties for this research, back propagation network was implemented. The process flow is two directional whereby, the training process begins from the input to the hidden layer, where the weight and biases alter the input and gives an output. When the predicted result is not equal to the desired output, the neural network takes note of the feedback error and adds it to adjust its weight and bias.
+
+
+RESULT AND DISCUSSION
+
+
+Permeability Modelling
+
+
+Using log data alone for the prediction of permeability across the surface of the wells gave erroneous results since the log data available for permeability simulation are insufficient and thus led to adopting co-simulation algorithm. The petrophysical modelling used volume of shale as a secondary variable which is densely sampled, and exhibit a good correlation with the primary well-log data. Correlation coefficient – minimal to fair, obtained from the crossplot of volume of shale against porosity as presented in Figure 3 was used as an input variable since porosity and permeability data exhibit similar trends, alongside with a modelled surface for which the property would be simulated. Figure 4 depicts a pictorial representation of generated permeability using co-simulation algorithm.
+
+
+Figure 3View largeDownload slidePorosity and Volume of shale log property crossplot used in the porosity model simulationFigure 3View largeDownload slidePorosity and Volume of shale log property crossplot used in the porosity model simulation Close modal
+
+
+Figure 4View largeDownload slideView of simulated realization of permeability modelling using co-simulationFigure 4View largeDownload slideView of simulated realization of permeability modelling using co-simulation Close modal
+
+
+The distribution of permeability property ranges from 100 md to 1000md, the values are represented by color code, where streaks of red and orange indicates high permeability values. The property increases towards the northeast direction reflecting good flow and has high hydrocarbon prospect. The values of porosity and permeability shown were as a result of sequence of deposition and the differential responses resulting from the various diagenetic regimes of the identified reservoir strata.
+
+
+ARTIFICIAL NEURAL NETWORK MODEL PERFORMANCE
+
+
+Figures 5 & 6 are the performance graphs of the constructed ANN model's training, validation, and overall predictions on real datasets. The ANN model predicted reservoir petrophysical properties that were quite close to the field datasets in the figure. The training dataset had a correlation coefficient (R) of 0.87511, whereas the validation dataset had a correlation coefficient (R) of 0.86554. In addition, when the created ANN model's overall projected reservoir petrophysical parameters were compared to actual reservoir data, the R value was 0.87228. The R value indicated that the ANN model predictions were near to the field's porosity (φ), and permeability (K), datasets.
+
+
+Figure 5View largeDownload slideTraining and validation performance of the output and target dataFigure 5View largeDownload slideTraining and validation performance of the output and target data Close modal
+
+
+Figure 6View largeDownload slideAll performance of the output and target dataFigure 6View largeDownload slideAll performance of the output and target data Close modal
+
+
+As seen in Figure 7, the projected reservoir model petrophysical characteristics and the actual well data are quite comparable depicting a horizontal line relationship. This indicates a 0.3 field permeability for any value of predicted permeability. Statistically, the MSE and RMSE values for permeability are 0.021 and 0.0449, respectively. These findings imply that model predictions match real field data pretty well.
+
+
+Figure 7View largeDownload slideComparison between the ANN predicted value and actual field data for permeabilityFigure 7View largeDownload slideComparison between the ANN predicted value and actual field data for permeability Close modal
+
+
+COMPARATIVE ANALYSIS OF GEOSTATISTICAL TECHNIQUE AND ANN MODEL
+
+
+In employing both geostatistical techniques and artificial neural network for the simulation and prediction of petrophysical properties there is a need to check for the accuracy of the methods used. This is done by estimation of errors from prediction of the petrophysical properties using the developed ANN model and also from the simulation of the reservoir properties using geostatistical techniques.
+
+
+Permeability distribution before (upscaled data) and after modelling (predicted properties) is presented in Figure 8 depicting a fair similarity between the simulated permeability data and upscaled data. The major disparity is found in the lowest permeability ranges while the predicted permeability has about 20 % mismatch error margin on the average across the data. The metrics which are utilized for the prediction errors for this study are RMSE (root mean squared error) and MSE (mean squared error) as this are already obtained from the developed ANN model and based on the data available from the simulated petrophysical property, the metrics are also employed to determine the methods employed, which provided the least error estimate.
+
+
+Figure 8View largeDownload slidePlot of simulated permeability data in comparison with upscaled data onlyFigure 8View largeDownload slidePlot of simulated permeability data in comparison with upscaled data only Close modal
+
+
+CONCLUSION
+
+
+The study has shown how geostatistics alongside machine learning algorithms could be used for population of reservoir properties especially for field at remote locations. The result shows that co-simulation was a better technique than sequential gaussian simulation as it relies on the influence of the secondary variable to populate primary variable. From the simulated realization, it was observed that more heterogeneities were captured when permeability property was populated using co-simulation. Regarding machine learning algorithm, the study indicated that artificial neural network (ANN) has been developed based on multiple-inputs and multiple-outputs (MIMO) for the prediction of reservoir petrophysical property, permeability. The developed ANN model is a back propagation network and Levenberg-Marquardt optimization algorithm was established to be the best learning algorithm.
+
+
+Based on the predictions performance of the ANN model, the conclusion can be drawn that the developed ANN model predicted reservoir petrophysical properties were in good agreement with the corresponding field data, as the obtained RMSE values of the model prediction performance was 0.0449. It could be concluded that both techniques (geostatistics and ANN model) could be better off used as a collaborative method.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+ACKNOWLEDGEMENT
+
+
+Key laboratory of Petroleum Resources Research, IGGCAS and Covenant University and CUCRID are appreciated for supporting this study. The operator of the field of study in Niger Delta is also appreciated for the release of data for this study and permission to publish.
+
+
+References
+
+
+Al, J. M., Al, M. S., Iop, J., Ser, C., Sci, M., Musawi, J. M. Al, & Jawad, M. S. Al. (2019). Study of different geostatistical methods to model formation porosity (Cast study of Zubair formation in Luhais oil field) Study of different geostatistical methods to model formation porosity (Cast study of Zubair formation in Luhais oil field). 0–14. https://doi.org/10.1088/1757-899X/579/1/012031Google Scholar Bjørlykke, K. (2015). Petroleum geoscience: From sedimentary environments to rock physics, second edition. In Petroleum Geoscience: From Sedimentary Environments to Rock Physics, 2nd Edition. https://doi.org/10.1007/978-3-642-34132-8Google Scholar Caumon, G. (2010). Towards stochastic time-varying geological modeling. Mathematical Geosciences, 42(5), 555–569. https://doi.org/10.1007/s11004-010-9280-yGoogle ScholarCrossrefSearch ADS  Chaki, S., Routray, A., & Mohanty, W. K. (2018). Well-Log and Seismic Data Integration for Reservoir Characterization: A Signal Processing and Machine-Learning Perspective. IEEE Signal Processing Magazine, 35(2), 72–81. https://doi.org/10.1109/MSP.2017.2776602Google ScholarCrossrefSearch ADS  El Khadragy, A. A., Eysa, E. A., Hashim, A., & Abd El Kader, A. (2017). Reservoir characteristics and 3D static modelling of the Late Miocene Abu Madi Formation, onshore Nile Delta, Egypt. Journal of African Earth Sciences, 132, 99–108. https://doi.org/10.1016/j.jafrearsci.2017.04.032Google ScholarCrossrefSearch ADS  Korjani, M. M., Popa, A. S., Grijalva, E., Cassidy, S., & Ershaghi, I. (2016). Reservoir characterization using fuzzy kriging and deep learning neural networks. Proceedings - SPE Annual Technical Conference and Exhibition, 2016-Janua. https://doi.org/10.2118/181578-msGoogle Scholar Rotimi, O. J., Zhenli, W., & AfolabiR. O. (2016). Characterizing Geometrical Anisotropy of Petrophysical properties in the middle Shahejie formation, Liaohe Depression, China. International Journal of Applied Environmental Sciences, 11(1), 89–109Google Scholar Zhang, Y. (2009). Introduction to Geostatistics - Course Notes. InCourseNotes.papers2://publication/uuid/20990432-8B19-4169-AC3A-F73E130F2D04Google Scholar Zhao, S., Zhou, Y., Wang, M., Xin, X., & Chen, F. (2014). Thickness, porosity, and permeability prediction: comparative studies and application of the geostatistical modeling in an Oil field. Environmental Systems Research, 3(1), 7. https://doi.org/10.1186/2193-2697-3-7Google ScholarCrossrefSearch ADS  Okon, A. N., Adewole, S. E., & Uguma, E. M. (2020). Artificial neural network model for reservoir petrophysical properties : porosity, permeability and water saturation prediction. Modeling Earth Systems and Environment, 0123456789. https://doi.org/10.1007/s40808-020-01012-4Google ScholarCrossrefSearch ADS  Rotimi, O. J., Ako, B. D., & Zhenli, W. (2014a). Application of rock and seismic properties for prediction of hydrocarbon potential. Petroleum and Coal, 56(1), 41–53Google Scholar Rotimi, O. J., Ako, B. D., & Zhenli, W. (2014b). Reservoir characterization and modeling of lateral heterogeneity using multivariate analysis. Energy, Exploration an Exploitation, 32(3), 527–552Google ScholarCrossrefSearch ADS  
+
+
+
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211987-MS
+
+
+

files/2022/Computer-Aided Design for a Multilateral Well Completion in a Stacked Reservoir.txt

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+----- METADATA START -----
+Title: Computer-Aided Design for a Multilateral Well Completion in a Stacked Reservoir
+Authors: Faith A. Bamgboye, Promise O. Longe, Boniface A. Oriji
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211980-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Over the years, multilateral well technology has been one of the most rapidly evolving and widely utilized production technologies for new and maturing reservoirs. Multilateral wells have the potential for reservoir productivity improvement. The characteristics used to evaluate multilateral well completion are connectivity, isolation, and accessibility. All these focus on the completion design of the main bore, lateral bores, and junctions that connect the lateral and main bores. Hence, one of the factors to consider in designing multilateral wells is the junction type, which depends on the required degree of mechanical integrity and pressure integrity at each lateral. Previous studies establish that the lateral junctions are a critical element of multilateral completions and can fail under formation stresses, temperature-induced forces, and differential pressures during production. Thus, the reliability of a multilateral completion design is the ability to construct and complete the multilateral junction successfully. The Technology Advancement of Multilaterals (TAML) has categorized the distinct types of multilateral junctions based on support and hydraulic integrity provided at the junction. The objectives of this paper are: (1) to provide a detailed discussion on each classification level and the conditions in which they are applicable, (2) to present a conceptually digitized application of a multilateral well on a stacked reservoir XXXX in a Niger Delta field using SEPAL software. To achieve the latter goal, after a preliminary and detailed casing design, we applied the SEPAL software to design and digitize the proposed multilateral well schematics for the stacked reservoir. From the analysis, a multilateral level 5 junction was selected to overcome specific problems (e.g., wellbore collapse) due to the unconsolidated sands of the reservoir in the field of interest.
+
+
+
+
+Keywords:
+directional drilling,
+reservoir,
+api casing collapse,
+drilling operation,
+junction,
+integrity,
+upstream oil & gas,
+lateral,
+requirement,
+multilateral well completion
+
+
+Subjects: 
+Drilling Operations,
+Directional drilling
+
+
+
+
+Introduction
+
+
+A multilateral well is a single well with one or more wellbore branches radiating from the main borehole. It may be an exploration well, an infill development well, or re-entry into an existing well. Multilateral completion systems allow the drilling and completion of multiple lateral boreholes within a single main bore. It describes the assembly and installation of a downhole pipe and related equipment so that oil and gas can be efficiently and safely extracted from various target zones of the reservoir. Multilateral well completions can be carried out in new and existing wells. Between 1980 and 1995, only 45 multilaterals well completions were reported; however, since 1995, hundreds of multilateral wells have been completed. This increased number of multilateral wells is related to a rapid sequence of advances in drilling multilateral wells—directional and horizontal drilling techniques, advanced drilling equipment, and coiled tubing drilling, Bosworth et al., (1998). Well completions for multilateral wells are very different from vertical wells or even single-leg horizontal wells. The critical distinguishing component in a multilateral well completion is the junction construction, which provides communication and conduction between the laterals and the main borehole.
+
+
+Cooper and Dowell (1988) explained the many circumstances to consider before deciding to drill horizontal wells, and these must also be considered in the final completion design. Some of these circumstances include (1) Thin reservoirs: the productivity index (PI) for a horizontal well reflects the increased area of contact of the well with the reservoir. (2) Vertical permeability: The productivity obtained by drilling a horizontal well partially depends on the magnitude of the vertical permeability and the length of the drain hole. The ratio of vertical permeability to horizontal permeability is high; a horizontal well may produce more cost-effectiveness than a vertical well. (3) Heterogeneous reservoirs: where horizontal drain-hole may provide several advantages when reservoir heterogeneity exists in the horizontal plane. This type of horizontal wellbore in the reservoir provides potential for far more information about the reservoir than would typically be available. As logging and completion techniques become more sophisticated, this aspect of horizontal wells is likely to be very advantageous. Other circumstances include recovery from water flooding and field development plans.
+
+
+Economides et al. (2001) discussed the three major categories of multilateral well designs, reservoir consideration, and their application. The 3 categories are open-hole multilateral wells, limited-isolation/access multilateral systems, and complete multilateral systems. Garrouch et al. (2004) presented the various lateral completion types that must be designed to fit the production constraints and the reservoir characteristics; for consolidated formations: (a) Open-hole, (b) pre-drilled or slotted-liner. (c) Pre-drilled or slotted liner with external casing packers. (d) Cased, cemented, and perforated, while for unconsolidated formations: (a) Open-hole with pre-drilled liner and stand-alone screen, (b) Open-hole with stand-alone screen, and (c) Open-hole gravel pack.
+
+
+Safare (2004) noted that multilateral well systems had developed rapidly based on technological advancements in the oil industry since their introduction. In the upstream sector, which involves exploration and production, the aim is to produce hydrocarbons optimally and efficiently. In carrying out this task, the following are some challenges faced:
+
+
+Difficult reservoir conditions such as tight formations or laminated reservoirs,Complex geologic conditions such as compartmentalized or stacked reservoirs,Efficient and effective reservoir management and development plans.
+
+
+Multilateral well technology provides innovative solutions to the above areas, and positive results have been obtained regarding production and finances. Figure 1 shows the geometries of multilateral wells ranging from a single drain-hole to multiple well branches in horizontal-fanned, vertical stacked or dual-opposed arrangements. These geometries are determined by the number of targets, depths/pressures, well construction parameters, and risk analysis.
+
+
+Figure 1View largeDownload slideCommon geometries of multilateral wells (Fraija et al., 2002).Figure 1View largeDownload slideCommon geometries of multilateral wells (Fraija et al., 2002). Close modal
+
+
+Many issues are involved in completion selection, and design of a multilateral well. The main concerns include borehole stability at the junctions and in the main wellbore and laterals, production/injection control, and re-entry for workover or stimulation. Hill, Zhu & Economides (2008) stated some factors to consider when designing a multilateral well completion. These factors are reservoir structure, junction formation characteristics, the differential pressure at the junction, production and injection management, and re-entry capacity.
+
+
+In pursuing optimum production, low-cost benefit, and maximum recovery of reserves, multilateral completions can provide a great advantage. The following failures include equipment-use failure, cementing issues of the junction or lateral, excessive drawdown causing junction collapse occurs when well completion is not sufficiently designed or reviewed when well plans changed. Computer-aided design of multilateral well completions helps mitigate these failures. It allows flexibility in selecting, designing, and optimizing appropriate multilateral well-completion design, thereby increasing reliability. Thus, this paper describes the conceptual design and analysis for the optimum multilateral well design using a computer-aided design approach. This approach will guide the effective selection of completion design, optimum casing design, effective equipment design, and easy modification of the multilateral well completion design, if necessary.
+
+
+Multilateral Well Completion Levels
+
+
+The Technology Advancement of Multilaterals (TAML) system for multilateral junction classification is based on support and hydraulic integrity provided at the junction. Wells were categorized according to the type of junction used to join the main bore to the lateral and produced standards that were designated TAML Levels 1 through 6. The ascending order of these levels reflects the junction's increasing mechanical and pressure capability. Consequently, cost, complexity, and risk also increase at the higher TAML levels (Flatern, 2016). They are as follows:
+
+
+Level 1: It's an open-hole lateral drilled from an open-hole mother-bore. There is no mechanical support or hydraulic isolation at the junction. Level 1 completions have been selected for many multilateral wells from the early stage of multilateral well development because of their simplicity and low cost. The requirements are consolidated, highly competent formations because of the lack of junction and lateral support. (Hill et al., 2008). Production control and zonal isolation generally are not available in commingled production. In Level 1 wells, re-entry is also not guaranteed. These disadvantages limit the applications of Level 1 multilateral completion.Level 2: Here, the main borehole is cased and cemented, but the laterals have simple completions such as an open hole, a slotted liner laying in the lateral from the main bore, or a prepacked screen laying in the lateral from the main bore. There are two ways to create the lateral at the junction, either by pre-milled windows or by milling through the casing. Consequently, the complexity is increased. But since the casing supports the junction, it achieves more borehole stability than Level 1 completion. After drilling the lateral, a standard Level 2 completion is to set a sliding sleeve at the junction between two packers and leave the lateral open hole. With the sliding sleeve open, both laterals produce commingled. For zonal isolation in this completion, a plug can be set in the lower packer to shut off the lower lateral for water coning and other production problems. To shut off the upper lateral section by closing the sliding sleeve. Notice that commingled production from both laterals cannot be separated once inside the tubing. Re-entry is also limited in this completion because of the sliding sleeve (Hogg, 1997).Level 3: The main wellbore is cased and cemented, and the lateral is cased but not cemented. The main advantage of Level 3 completion is that the mechanical integrity at the junction is improved compared with Level 2 completions. A Level 3 junction provides sand control means for unconsolidated formations and provides limited junction support for heavy-oil production. Since hydraulic integrity is not available in Level 3 completions without cement at the junction, junction failure is still a problem when pressure drawdown is substantial after a production period.Level 4: The main borehole and the lateral are cased and cemented at the junction for this type. Level 4 provides better mechanical integrity and hydraulic isolation by cementing at the junction compared to Level 3. It can withstand higher-pressure differential and prevent sand problems at the junction. The completion procedure is more complicated because it takes more trips and has more equipment. Level 4 completions can be created either by milling a window in the casing from the main wellbore or by using a pre-milled casing. After the lateral is drilled, a liner is placed and cemented in the lateral.Level 5: These completions were developed based on Level 4 systems with improvements in pressure integrity. The junction's full pressure integrity is achieved by running tubular and packers in the main wellbore and the lateral. In a typical Level 5 completion, there is a dual packer above the junction location and two more packers below the junction in the lower part of both the main wellbore and the lateral. Two tubing strings are run below the dual packer into the main wellbore and the lateral, and the additional packers seal them in the main wellbore and the lateral. Level 5 multilateral offers the best solution for multilateral wells in weak, incompetent environments that are susceptible to wellbore collapse.Level 6: This is the most advanced completion in multilateral well technology. Full pressure integrity and hydraulic isolation at the junction are achieved with casing strings, both in the main wellbore and in the lateral. A Level 6 junction has several significant advantages compared with other multilateral completions. It is a single component completion, the junction completion process is much simpler, and it eliminates the debris from downhole milling or wash-over processes. The resulting completion offers maximum flexibility while minimizing risk and complexity. The current primary limitations of Level 6 junctions are the larger hole size required and the high cost (Hill et al., 2008). It is typically employed at the bottom of a casing string. After the casing and junction are cemented into place, the laterals are drilled and tied back to the junction with some cemented lateral liner and hanger assembly (Sarfare, 2004).
+
+
+Figure 2View largeDownload slideTAML Classification of Multilateral Completions (Butler et al., 2017)Figure 2View largeDownload slideTAML Classification of Multilateral Completions (Butler et al., 2017) Close modal
+
+
+Complete Multilateral Well System
+
+
+A complete multilateral system provides two to five laterals from one new or existing wellbore. The system must also be compatible with cementing operations for liners, slotted liners, and prepacked screens for sand control.
+
+
+Applications
+
+
+The lateral wellbore is cased back to the primary bore exit in this design. The liner casing string is mechanically connected to the primary bore casing; the lateral-to-main-wellbore junction must be hydraulically sealed. Any complete lateral bore or portions of any lateral can be isolated to control the production inflow profile. Each lateral must also be accessible for re-entry without rig intervention.
+
+
+Reservoir Considerations
+
+
+Proper reservoir modeling and target selection must occur during the project planning phase, and a stable, non-sloughing, impermeable shale or hard rock formation is desirable at the exit point. However, if the target selection requires exit in unconsolidated sands or in the producing interval itself, the unconsolidated sand can be stabilized with cement or plasticized material. Since low to medium build rates will be used to simplify casing installation, engineers must select targets and plan drill paths with such considerations in mind.
+
+
+Installation Considerations
+
+
+The primary bore is drilled, and the primary production casing string is cemented in place across all anticipated lateral-bore exit points. The primary bore is typically drilled into a producing zone and completed for final production.
+
+
+Figure 3View largeDownload slideComplete (Advanced) Multilateral Well System (Economides et al., 2001)Figure 3View largeDownload slideComplete (Advanced) Multilateral Well System (Economides et al., 2001) Close modal
+
+
+Advantages of Multilateral Wells
+
+
+Multilateral wells leverage the existing advantages of horizontal wells with further improvements to produce multilayer pay zones [Oluwadairo, (2018), Rivera, et al., (2003)] compared the economics of levels 3 and 6 multilateral junction configurations to a horizontal well for a natural flow of oil of gravity between 20 – 29° API in a homogeneous reservoir, and permeability ranging from 10 mD to 1250 mD. Results showed that the two-branched multilateral produced 13% more oil than the horizontal well in a high permeability reservoir. The multilateral produced 80% more oil than the horizontal well in a low permeability reservoir with low viscosity oil and 10 to 15% less water than the horizontal well. Horizontal wells have the following benefits over vertical wells: better sweep efficiencies, decreased water, and gas coning tendencies, increased exposure to natural fractures in the formation, and increased efficiency of draining relatively thin formation layers, ultimately leading to higher productivity indices from the well. In summary, multilateral provide the following advantages:
+
+
+Greater contact area to reservoir ratio and increased exposure to natural fracture systems via multiple laterals.Low cost-benefit ratio: reduced capital spent on drilling the well, wellhead installation, platform risers, and completion equipment compared to multiple wells. An operator in the Arabian Gulf reported 35% savings per well, despite 44% extra cost compared to a single horizontal (Al-Umair, 2000).Access to multiple pay zones, including thin layers or older and formerly depleted reservoirs from a single location.Well slot optimization and minimized environmental impact or footprint in offshore and harsh or remote locations.Extend the life of an existing field development by drilling laterals out of the existing wellbores and tapping into reserves that were not recovered during the earlier production stage.Accelerated production, i.e., higher flow rates at lower pressure drops than single-bore wells.
+
+
+Methodology
+
+
+This section includes the procedures involved in this study, including the theoretical assumptions. The usage of SEPAL (Structured Engineering Presentation and Analytics Leverage) in this study comprises the design, digitization, and management of well schematics on a single platform. This SEPAL module is used to generate conceptual well-completion schematics (initial), store completion schematic as deployed (final), and manage changes to well configurations because of intervention over time. All these data can be queried and retrieved from the database when required. The SEPAL Well Schematic Management module output is a digital well status diagram that can be queried for various engineering analyses and easily updated to capture the evolution of schematics over the life of the well.
+
+
+Figure 4View largeDownload slideWorkflow of studyFigure 4View largeDownload slideWorkflow of study Close modal
+
+
+Data
+
+
+Table 1PVT Data for Reservoir XXXX Reservoir
+            . Pi(psia)
+            . Saturation Pressure (psia)
+            . Rsi (SCF/STB))
+            . Temp (°F)
+            . Bo (RB/STB)
+            . API
+            . Oil Viscosity (cP)
+            . XXXX 4409 4159 982 172 1.578 33.04 0.35 Reservoir
+            . Pi(psia)
+            . Saturation Pressure (psia)
+            . Rsi (SCF/STB))
+            . Temp (°F)
+            . Bo (RB/STB)
+            . API
+            . Oil Viscosity (cP)
+            . XXXX 4409 4159 982 172 1.578 33.04 0.35 View Large
+
+
+Table 2Structural Data for Reservoir XXXX Sand A  Top = 10113 (ft) Vertical Permeability = 468.52 (mD) Base = 10167 (ft) Horizontal Permeability = 113.19 (mD) Contact = 10153 (ft) Swi = 0.18 Thickness = 64 (ft) Porosity = 0.23 Sand B  Top = 10169 (ft) Vertical Permeability = 368.52 Base = 10190 (ft) Horizontal Permeability = 93.34 (mD) Contact = 10173 (ft) Swi = 0.16 Thickness = 21 (ft) Porosity = 0.24 Sand C  Top = 10208 (ft) Vertical Permeability = 568.52 Base = 10243 (ft) Horizontal Permeability = 123.19 (mD) Contact = 10216 (ft) Swi = 0.22 Thickness = 35 (ft) Porosity = 0.23 Sand A  Top = 10113 (ft) Vertical Permeability = 468.52 (mD) Base = 10167 (ft) Horizontal Permeability = 113.19 (mD) Contact = 10153 (ft) Swi = 0.18 Thickness = 64 (ft) Porosity = 0.23 Sand B  Top = 10169 (ft) Vertical Permeability = 368.52 Base = 10190 (ft) Horizontal Permeability = 93.34 (mD) Contact = 10173 (ft) Swi = 0.16 Thickness = 21 (ft) Porosity = 0.24 Sand C  Top = 10208 (ft)��Vertical Permeability = 568.52 Base = 10243 (ft) Horizontal Permeability = 123.19 (mD) Contact = 10216 (ft) Swi = 0.22 Thickness = 35 (ft) Porosity = 0.23 View Large
+
+
+Preliminary Analysis
+
+
+Determination of the Casing Setting Depth
+
+
+The principle of selecting the casing shoe setting depths starts with the knowledge of pore pressure, fracture gradient, and mud density. These can be obtained using the following equations:
+
+
+Pore Pressure (P. P) Gradient=Pore PressureDepthpsi/ftEquation 1
+
+
+Fracture Gradient (F. G)=(SD−PD)*(y1−y)+(PD) psi/ftEquation 2
+
+
+Where SD = Overburden Pressure in psi/ft
+
+
+PD = pressure gradient at depth of interest
+
+
+y = Poisson's ratio = 0.35
+
+
+P. P Gradient in ppg=P. P Gradient0.052Equation 3
+
+
+F. G in ppg=F. G0.052Equation 4
+
+
+Design F. G =F.G in ppg−0.52Equation 5
+
+
+Mud Weigth =P. P Gradient in ppg+0.52Equation 6
+
+
+Table 3Casing Setting Depth Data Depth (ft)
+            . Pore Pressure (ft)
+            . P.P Gradient (psi/ft)
+            . Fracture Gradient (psi/ft)
+            . P.P Gradient (ppg)
+            . Mud Density (ppg)
+            . Design F.G (ppg)
+            . 0 — — — — — — 1000 195 0.195 7.74 3.75 4.25 7.24 2000 390 0.195 7.74 3.75 4.25 7.24 3000 625 0.208 7.86 4.01 4.51 7.36 4000 950 0.238 8.11 4.57 5.07 7.61 5000 1350 0.270 8.40 5.19 5.69 7.90 6000 1800 0.300 8.67 5.77 6.27 8.17 7000 2400 0.343 8.74 6.59 7.09 8.24 8000 3160 0.395 9.82 7.60 8.10 9.32 9000 3610 0.401 9.88 7.71 8.21 9.38 10000 4120 0.412 9.97 7.92 8.42 9.47 11000 5100 0.464 10.43 8.92 9.42 9.93 Depth (ft)
+            . Pore Pressure (ft)
+            . P.P Gradient (psi/ft)
+            . Fracture Gradient (psi/ft)
+            . P.P Gradient (ppg)
+            . Mud Density (ppg)
+            . Design F.G (ppg)
+            . 0 — — — — — — 1000 195 0.195 7.74 3.75 4.25 7.24 2000 390 0.195 7.74 3.75 4.25 7.24 3000 625 0.208 7.86 4.01 4.51 7.36 4000 950 0.238 8.11 4.57 5.07 7.61 5000 1350 0.270 8.40 5.19 5.69 7.90 6000 1800 0.300 8.67 5.77 6.27 8.17 7000 2400 0.343 8.74 6.59 7.09 8.24 8000 3160 0.395 9.82 7.60 8.10 9.32 9000 3610 0.401 9.88 7.71 8.21 9.38 10000 4120 0.412 9.97 7.92 8.42 9.47 11000 5100 0.464 10.43 8.92 9.42 9.93 View Large
+
+
+Figure 5 below shows a plot pore pressure gradient in ppg, the mud weight in ppg, the design fracture gradient, and the fracture gradient against depth. This graphical representation is done to obtain the setting depth for each of the proposed casing strings:
+
+
+Figure 5View largeDownload slideCasing Settling SelectionFigure 5View largeDownload slideCasing Settling Selection Close modal
+
+
+Detailed Design
+
+
+The detailed design includes the determination of casing specifications: the grades, the connection type, and the weight of each casing string. The selection process compares the pipe ratings with the design loads such as bursts and collapse and applies minimum acceptable safety standards (i.e., design factors). The API design factors are essential "safety factors" to design safe, reliable casing strings. The following will be used in this study:
+
+
+Collapse (From External Pressure): 1.125
+
+
+Burst (From Internal Pressure): 1.1
+
+
+Collapse and Burst Requirements
+
+
+Collapse Pressure, Pc=0.052×Mud Weight×Depth×Design FactorEquation 7
+
+
+Burst Pressure, PB=P. P Gradient×Depth×Design FactorEquation 8
+
+
+Table 5Input Data, Obtained from Figure 5  Setting Depth
+            . P.P Gradient
+            . Mud Weight
+            . Main Wellbore 0—100ft 0.2382 psi/ft 5.08 ppg 0—4,000ft 0.2382 psi/ft 5.08 ppg 0—7,500ft 0.3702 psi/ft 7.62 ppg 0—10,400ft 0.4295 psi/ft 8.76 ppg Lateral Wellbores 10,000—10,133ft 0.4295 psi/ft 8.76 ppg 10,140—10,170ft 0.4295 psi/ft 8.76 ppg 10,180—10,212ft 0.4295 psi/ft 8.76 ppg Setting Depth
+            . P.P Gradient
+            . Mud Weight
+            . Main Wellbore 0—100ft 0.2382 psi/ft 5.08 ppg 0—4,000ft 0.2382 psi/ft 5.08 ppg 0—7,500ft 0.3702 psi/ft 7.62 ppg 0—10,400ft 0.4295 psi/ft 8.76 ppg Lateral Wellbores 10,000—10,133ft 0.4295 psi/ft 8.76 ppg 10,140—10,170ft 0.4295 psi/ft 8.76 ppg 10,180—10,212ft 0.4295 psi/ft 8.76 ppg View Large
+
+
+Conductor Pipe (20 inch)
+
+
+Collapse
+
+
+PC = 0.052 × 5.08 × 100 × 1.125 = 29.72 psi
+
+
+Burst
+
+
+PB = 0.2328 × 100 × 1.1 = 25.61 psi
+
+
+Figure 6View largeDownload slideSelection of Conductor Casing Based on API Casing Collapse, Burst, and Tensile PropertiesFigure 6View largeDownload slideSelection of Conductor Casing Based on API Casing Collapse, Burst, and Tensile Properties Close modal
+
+
+From the API specification table, casing grade H-40 satisfies the conductor casing's collapse and burst pressures requirements.
+
+
+Surface Casing (16 inch)
+
+
+Collapse
+
+
+PC = 0.052 �� 5.08 × 4000 × 1.125 = 1188.72 psi
+
+
+Burst
+
+
+PB = 0.2328 × 4000 × 1.1 = 1024.32 psi
+
+
+Figure 7View largeDownload slideSelection of Surface Casing Based on API Casing Collapse, Burst, and Tensile PropertiesFigure 7View largeDownload slideSelection of Surface Casing Based on API Casing Collapse, Burst, and Tensile Properties Close modal
+
+
+From the API specification table, casing grade K-55 satisfies the surface casing's collapse and burst pressures requirements.
+
+
+Intermediate Casing (13⅜ inch)
+
+
+Collapse
+
+
+PC = 0.052 × 7.62 × 7500 × 1.125 = 3343.28 psi
+
+
+Burst
+
+
+PB = 0.3702 × 7500 × 1.1 = 3054.15 psi
+
+
+Figure 8View largeDownload slideSelection of Intermediate Casing Based on API Casing Collapse, Burst, and Tensile PropertiesFigure 8View largeDownload slideSelection of Intermediate Casing Based on API Casing Collapse, Burst, and Tensile Properties Close modal
+
+
+From the API specification table, casing grade P-110 satisfies the collapse and burst pressures requirements of the Intermediate Casing.
+
+
+Production Casing (9⅝ inch)
+
+
+Collapse
+
+
+PC = 0.052 × 8.76 × 10400 × 1.125 = 5329.58 psi
+
+
+Burst
+
+
+PB = 0.4295 × 10400 × 1.1 = 4913.48 psi
+
+
+Figure 9View largeDownload slideSelection of Production Casing Based on API Casing Collapse, Burst, and Tensile PropertiesFigure 9View largeDownload slideSelection of Production Casing Based on API Casing Collapse, Burst, and Tensile Properties Close modal
+
+
+From the API specification table, casing grade N-80 satisfies the production casing's collapse and burst pressures requirements.
+
+
+Production Liner I (7 inch)
+
+
+Collapse
+
+
+PC = 0.052 × 8.76 × 10133 × 1.125 = 5192.76 psi
+
+
+Burst
+
+
+PB = 0.4295 × 10133 × 1.1 = 4787.34 psi
+
+
+Production Liner II (7 inch)
+
+
+Collapse
+
+
+PC = 0.052 × 8.76 × 10170 × 1.125 = 5211.72 psi
+
+
+Burst
+
+
+PB = 0.4295 × 10170 × 1.1 = 4804.82 psi
+
+
+Production Liner III (7 inch)
+
+
+Collapse
+
+
+PC = 0.052 × 8.76 × 10212 × 1.125 = 5233.24 psi
+
+
+Burst
+
+
+PB = 0.4295 × 10212 × 1.1 = 4824.66 psi
+
+
+Figure 10View largeDownload slideSelection of Production Liners Based on API Casing Collapse, Burst, and Tensile PropertiesFigure 10View largeDownload slideSelection of Production Liners Based on API Casing Collapse, Burst, and Tensile Properties Close modal
+
+
+From the API specification table, casing grade C-90 satisfies the collapse and burst pressures requirements of the production liners of the three layers.
+
+
+Table 6Proposed Casing Information Casing Name
+            . Casing Setting Depth Interval (ft)
+            . Casing Setting Depth (ft)
+            . Casing Size (in)
+            . Casing Grade
+            . Nominal weight (lbs/ft)
+            . From
+            . To
+            . TVD
+            . MD
+            . Conductor 0 100 100 100 20 H-40 94 Surface 0 4,000 4,000 4,000 16 K-55 84 Intermediate 0 7,500 7,500 7,500 13⅜ P-110 80.7 Production 0 10,400 10,400 10,400 9⅝ N-80 53.5 Production Liner I 10,000 10,133 10,133 900 7 C-90 29 Production Liner II 10,140 10,170 10,170 900 7 C-90 29 Production Liner III 10,180 10,212 10,212 900 7 C-90 29 Casing Name
+            . Casing Setting Depth Interval (ft)
+            . Casing Setting Depth (ft)
+            . Casing Size (in)
+            . Casing Grade
+            . Nominal weight (lbs/ft)
+            . From
+            . To
+            . TVD
+            . MD
+            . Conductor 0 100 100 100 20 H-40 94 Surface 0 4,000 4,000 4,000 16 K-55 84 Intermediate 0 7,500 7,500 7,500 13⅜ P-110 80.7 Production 0 10,400 10,400 10,400 9⅝ N-80 53.5 Production Liner I 10,000 10,133 10,133 900 7 C-90 29 Production Liner II 10,140 10,170 10,170 900 7 C-90 29 Production Liner III 10,180 10,212 10,212 900 7 C-90 29 For the vertical section of the wellbore, the True Vertical Depth (TVD) = Measured Depth (MD)View Large
+
+
+Output from the SEPAL Software
+
+
+The Figure 11 above depicts the optimum multilateral well schematic showing various downhole equipment for the Reservoir XXXX. The components of the completion system are discussed further:
+
+
+Figure 11View largeDownload slideOptimum Multilateral Well Completion SchematicFigure 11View largeDownload slideOptimum Multilateral Well Completion Schematic Close modal
+
+
+Surface Controlled Subsurface Safety Valve (SCSSV)
+
+
+The SCSSV acts as a fail-safe to isolate the wellbore in case of mechanical system failure or damage to the surface production-control facilities. The control system operates with hydraulic control pressure used to hold open a ball or flapper assembly that will close if the control pressure is lost.
+
+
+Gas-lift Mandrel
+
+
+The gas-lift mandrel is assembled with the production tubing string to provide a means of locating gas-lift valves. At a later time, when the well cannot produce naturally because of a reduction in pressure, the gas-lift system will become necessary
+
+
+Packer
+
+
+The packers in the producing zones act as a sealing device that isolates and contains produced fluids and pressures within the tubing string. The dual-access and the single-access packer in the vertical section of the well support some of the tubing weight and prevent downhole movement of the tubing strings.
+
+
+Intervention Discriminator
+
+
+This tool helps access the laterals 2 and 3 selectively; also, the production from each lateral is commingled through the 3½ inch.
+
+
+Inflow Control Valve
+
+
+The presence of surface-controlled, variable inflow control valves to control the commingled production from laterals 2 and 3 and prevent crossflow. This device provides an overall capability to effectively manage the reservoir and production over the life of the well, which prolongs the field life, thus improving overall economic performance and field economics.
+
+
+Autonomous Inflow Control Device (AICD)
+
+
+The AICD in the lateral zones can help balance hydrocarbon flow as it enters the production string. The significant function here is to delay gas and water influx and reduce gas and water influx on breakthrough
+
+
+Downhole Flowmeter
+
+
+The downhole flow meter enables a less intrusive flow monitoring of the producing zones. It may be equipped with at least a sensor designed to sense a parameter (e.g., pressure and temperature) related to fluid flow.
+
+
+Frac-Pack
+
+
+Due to the unconsolidated sands of the Reservoir XXXX, a frac-pack sand control method is used to improve the productivity of the producing zones over the life of the well. Frac-packing merges two distinct processes—hydraulic fracturing and gravel packing.
+
+
+Conclusion
+
+
+The optimum multilateral well completion proposed in this work satisfies the conceptual design requirements. These requirements include the casing preliminary and detailed designs and selecting the appropriate equipment needed to ensure efficient operation of the well. Hence, taking a digital approach in designing multilateral wells provides solutions to the problems earlier stated. The schematics obtained is a crucial parameter during the planning phase of constructing the well and can be followed through to the completion of the well, production, and re-entry operations. The Level 5 lateral junction type is selected for this well completion design to overcome wellbore collapse due to the unconsolidated sands of the reservoir.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgment
+
+
+We want to thank the management of CypherCrescent Limited for the support for this paper; granting the license to use the SEPAL and the permission to publish this paper. Finally, we also wish to thank Dolapo O., Lowell U., Diepreye Y., and Paul (IDSL) for their contribution, invaluable support, encouragement, corrections, and gentle guidance in the successful completion of this work.
+
+
+References
+
+
+Cooper, R. E., & Dowell, P. T. (1988). "An Overview of Horizontal Well Completion Technology." Paper presented at the International Meeting on Petroleum Engineering, Tianjin, China. doi:https://doi.org/10.2118/17582-MSGoogle Scholar Hogg, C. (1997). "Comparison of Multilateral Completion Scenarios and their Application". Paper presented at the SPE Offshore Europe, Aberdeen, United Kingdom. doi:https://doi.org/10.2118/38493-MSGoogle Scholar Bosworth, S., El-Sayad, H. S., & Ismail, G. (1998). Key Issues in Multilateral Technology. Schlumberger Oilfield Review.Google Scholar Umair, N. A. (2000). "The First Multilateral/Dual lateral well completion in Saudi Arabia". Paper presented at the IADC/SPE Asia Pacific Drilling Technology, Kuala Lumpur, Malaysia. doi:https://doi.org/10.2118/62771-MSGoogle Scholar Economides, M. J., Collins, D. R., HottmanW. E., & LongbottomJ. R. (2001). Horizontal, Multilateral, and Multibranch Wells in Petroleum Production Engineering.Google Scholar FraijaJ., OhmerH., & PulickT. (2002). New Aspect of Multilateral Well Construction. Schlumberger Oilfield ReviewGoogle Scholar Hill, A. D., Ding, Z., & Economides, M. J. (2008). Multilateral well. Society of Petroleum Engineers.Google Scholar Flatern, R. (2016). Multilateral Wells. Schlumberger Oilfield Review.Google Scholar Butler, B., Grossmann, A., Parlin, J., & SekhonC. (2017). Study of Multilateral-Well-Construction Reliability. SPE Drilling and Completion32(01): 42–50. doi:
+              https://doi.org/https://doi.org/10.2118/175437-PAGoogle ScholarCrossrefSearch ADS  Oluwadairo, K. (2018). Multilateral Well Modeling from Compartmentalized Reservoirs. Memorial University of Newfoundland St John's, Newfoundland & Labrador Canada. Faculty of Engineering and Applied Science. Department of Oil and Gas Engineering.Google Scholar Rivera, N., Spivey, J. P., & Sehbi, S. B. (2003). Multilateral, intelligent well completion benefits explored. Retrieved fromhttp://www.ogj.com/articles/print/volume-101/issue-15/drilling-production/multilateral-intelligent-well-completion-benefits-explored.htmlGoogle Scholar Garrouch, A. A., Lababidi, M. S., & EbrahimA. S. (2004). An Integrated Approach for The Planning and Completion of Horizontal and Multilateral Wells. Journal of Petroleum Science and Engineering44 (2004) 283–301Google ScholarCrossrefSearch ADS  ManojSarfare. (2004). Reservoir Studies of New Multilateral Well Architecture. Texas A&M University, Department of Petroleum Engineering.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211980-MS
+
+
+

files/2022/Condensate Well Production Optimisation in Oredo Field Using Simulated Surface Proportional Integral Derivative Controller Downhole Transmitter and WellheadBottomhole Chokes.txt

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+----- METADATA START -----
+Title: Condensate Well Production Optimisation in Oredo Field Using Simulated Surface Proportional Integral Derivative Controller, Downhole Transmitter and Wellhead/Bottomhole Chokes
+Authors: Joshua Dala, Lateef Akanji, Kelani Bello, Olalekan Olafuyi, Prashant Jadhawar
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212047-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+A method of optimising gas production from condensate well in Oredo field by simulating surface proportional integral derivative controller, downhole transmitter, wellhead and bottomhole chokes is presented. This method overcomes the potential risk of high backpressure imposed on the production tubing by manual choking or other control solutions using wellhead valve. Firstly, a model of Oredo well O7 is constructed with a closed node constituting the reservoir unit and a surface pressure node on the wellhead. An automated pressure integral derivative controller that senses and controls the bottomhole flowing pressure by actuating the wellhead choke is then installed at the wellhead. Measurement input to the auto-controller is delivered via an insitu transmitter. This design approach is successfully applied to the well O7 model through a commercial multiphase simulator on well models and provides a condensate banking monitoring mechanisms with improved production output.
+
+
+
+
+Keywords:
+drillstem/well testing,
+upstream oil & gas,
+drillstem testing,
+artificial intelligence,
+complex reservoir,
+controller,
+production logging,
+production control,
+bottomhole,
+surface proportional integral derivative controller
+
+
+Subjects: 
+Well & Reservoir Surveillance and Monitoring,
+Reservoir Simulation,
+Formation Evaluation & Management,
+Unconventional and Complex Reservoirs,
+Production logging,
+Drillstem/well testing,
+Completion Selection and Design,
+Completion Installation and Operations,
+Completion equipment,
+Gas-condensate reservoirs
+
+
+
+
+Introduction
+
+
+Gas condensate reservoirs are characterised by an instant or progressive decrease in well production. As reservoir pressure declines below the dew point, a high condensate saturation of fluid develops near the well bore region. This process brings about reduction in gas deliverability due to reduction in effective gas permeability. Kapuni Apportioned Optimisation Spreadsheet (KAOS) has been used by Claire (2001) to optimise production from condensate wells through an examination of both the thermodynamic conditions and the obtained KAOS results.
+
+
+In the Arun gas field, development of wells and facilities stemmed from from four clusters (Pathak et. al., 2004). Gas condensate dropout and associated water from the wells are treated at the points of production. Cluster control program was utilised in operating the wells and cluster process. In the Kuparik River reservoir at Alaskan North slope, the main objective of the optimisation process as applied in this field was to produce maximum oil through gas lift gas allocation to the producing wells (Stoisits et. al., 1994).
+
+
+Supervisory Control and Data Acquisition (SCADA) system has been used in remote monitoring of condensate producing fields (e.g., Mclean and Goranson, 1997) such that an automatic switch prevents liquids accumulation. Further, permeability curves have been used by Coskuner, 1999 to enhance condensate reservoir simulation by providing an understanding of the formation’s behaviour and assisting in the optimisation of the field development. Mendik 2005, presented a gas condensate production optimisation technique by using reservoir modelling and simulation to define the number of wells, location of wells and performance option analysis.
+
+
+Seah et al., 2014, used compositional simulation in gas condensate production optimisation by analysing horizontal well configuration and fluid compositions. Optimal injection and production rates in condensate systems have been identified by using nature-inspired algorithms in simulation (e.g. Janiga, et al., 2018). In temperature transient analysis (TTA) of remote gauges located at a distance from the sandface Dada, et. al. (2018) reconstructed the sandface temperature using both numerical transient thermal simulators and operational practices that tend to minimise attenuation effects.
+
+
+Liang et. al., 2020 proposed an improved genetic algorithm optimisation fuzzy controller for managed pressure drilling by establishing the wellhead back pressure control model and calculating the transfer function. The proposed controller performs better in terms of speed, stability, and robustness. Proportional–integral–derivative (PID) controller PID is widely used because of its simple structure and low maintenance cost (Feng et. al., 2018). Application of combined PID at the wellhead and downhole transmitter in proactive monitoring of condensate banking in producing wells is sparse.
+
+
+Dala et. al. 2021 developed pseudo-pressure functions for describing the distribution of pressure within each region in a condensate reservoir. Within the proposed region 4, which is nearest to the wellbore, the quantities of the gas-to-oil (GOR) and condensate-to-gas (CGR) ratios will reverse trend at the end of region 3. They concluded that the pseudo-pressure values in regions 3 and 4 will depend on the GOR and CGR in the system and will evolve over the life of the well.
+
+
+Methodology
+
+
+A proportional–integral–derivative (PID) controller combines proportional control with integral and derivative corrections to automatically compensate for changes in the system. In essence, a PID controller can be used to force feedback on the condensate wellhead pressure to match the bottomhole dewpoint pressure (setpoint). This is the subject of the current work as described in this section. The overall system control function can be expressed as:
+
+
+u (t)=Kpe(t)+Ki∫e(t)dt+Kpdedt,
+
+
+where, Kp, Ki and Kp, are non-negative coefficients for the proportional, integral, and derivative terms respectively (P, I, and D). Figure 1 is a schematic representation of a PID controller installed at the wellhead in a feedback loop for condensate producing well. A transmitter installed at the bottomhole provides an input for the controller at the wellhead based on the desired bottomhole pressure value or setpoint r(t). The transmitted r(t) is obtained from the analysis of a complementary work reported by Dala et. al. 2021, such that surface choking is trigerred as soon as gas condensate banking threshold pressure is reached. y(t) is the measured process value (PV). Figure 2 is a system representation of the Oredo well 07 showing the reservoir, wellbore, production tubing and tubing head. The locations of the PID (auto-C) and transmitter (TM-1) are also indicated on the system diagram. The design is implemented in a commercial simulator Olga software package.
+
+
+Figure 1View largeDownload slideA schematic diagram of a proportional–integral–derivative (PID) controller in a feedback loop for condensate producing well. r(t) is the desired bottomhole pressure value or setpoint (SP) as provided by the transmitter, and y(t) is the measured process value (PV).Figure 1View largeDownload slideA schematic diagram of a proportional–integral–derivative (PID) controller in a feedback loop for condensate producing well. r(t) is the desired bottomhole pressure value or setpoint (SP) as provided by the transmitter, and y(t) is the measured process value (PV). Close modal
+
+
+Figure 2View largeDownload slideA schematic representation of the well design for a) dual-string completion model and b) single string completion model. The implementation of the surface and downhole choking was carried out on the single-string completion design model only.Figure 2View largeDownload slideA schematic representation of the well design for a) dual-string completion model and b) single string completion model. The implementation of the surface and downhole choking was carried out on the single-string completion design model only. Close modal
+
+
+The primary objective of this investigation is to carry out sensitivity analysis on the application of surface and downhole chokes in numerical simulation models of condensate wells. It is desired to maintain bottomhole pressure above dew point and avoid condensation at the wellbore. The two chokes have been designated as Oredo-7 bottomhole choke valve (O7- BHV) and Oredo-7 wellhead choke valve (O7-WHV) respectively.
+
+
+Mass and momentum conservation equations
+
+
+ρρφ∂∂t(xgρg)=−1A∂∂z(xgA ρgUg)±φ±Sg,
+
+
+where, φ represents the gas condensation rate, ρ is the density, A is the cross-sectional area, U is gas velocity, S is saturation and z is the vertical flow direction. The momentum equation for gas can be written as:
+
+
+ρρρøρ∂∂t(∑xgρgUg)=−(∑xg)∂p∂z−1A∂∂z(∑xgAρgUg2)−∑n12DnfnUn|Un|ρg±g cos ø∑xgρg+ Σ Mp+Me−Md+Σ Mb,
+
+
+and for liquid condensate component
+
+
+ρρρρøø∂∂t(∑xoρoUo)=−xg∂p∂z−1A∂∂z(∑xoAρoUo2)−∑n12DnfnUn|Un|ρg±xogρo cos ø−∑Mp−Me+Md+∑Mb−Cg sin ø∂h∂z
+
+
+and the energy conservation equation within a discretised wellbore for a mass field mi within the whole system can be expressed as:
+
+
+∂∂t(∑gmgEg)+1A∂∂z(A∑gmgUgHg)=S+Q+Hg,(9)
+
+
+where,
+
+
+Hg=hf+gY+12⋅Ug2
+
+
+is the field enthalpy,
+
+
+Eg=ef+gY+12⋅Ug2
+
+
+is the field energy and S is the enthalpy source/sink, and Q is the heat flux through the tubing wall. Other constitutive correlations govern interfacial mass transfer, entrainment / deposition of droplets between liquid and gas, entrainment of gas bubbles in oil and vice-versa, friction at the pipe wall surface and fluid/fluid interface and
+
+
+∑ivi=1
+
+
+i = gas, hydrocarbon bulk, hydrocarbon droplets, water bulk, water droplets. In OLGA numerical application, a finite volume with a staggerred grid technique is used for the discretisation, forward approximation to time derivatives, backward approximation to space derivatives, semi-implicit method and integration time-step is limited by Courant-Friedrich- Levy (CFL) criterion based on fluid transport velocity. Primary variables include five mass fractions, three velocity fields, one pressure field and one temperature field. Secondary variables required by the system include volume fractions, velocities of droplet, flowrates, other temperature and pressure dependent fluid properties, etc.
+
+
+Numerical simulation in OLGA™
+
+
+The OLGA™ transient wellbore numerical simulation pacakage (Schlumberger 2012) was used for the purpose of this investigation. Parameters measured directly from Oredo field were used as input for the numerical simulation study. The reservoir is modelled separately, and the parameters obtained therefrom are used as the lower (reservoir) boundary condition via the closed "source" term definition. A PID controller installed at the wellhead will match the bottom-hole pressure (BHP) or dew point pressure and subsequently activate the downhole and wellhead valves accordingly. Installation of a transmitter feeding the PID controller allows for an adequate reduction in condendensate banking and hence gas production optimisation.
+
+
+Additional parameters required for setting up the OLGA models include: wall and tubing characteristics to replicate the thermal conductivity and temperature gain/loss behaviour across the length of the production tubing. Linear and non-linear opening types of choking are adopted and analysed. Assigned boundary and initial conditions of pressure and temperature include 4030 psia and 189°F. Other essential conditions are imposed as required by the model setup. Flow of fluid from the reservoir to the wellbore is modelled using Fetkovich equations. Fluid PVT was modelled in Multifash and phase envelope of reservoir fluid is shown in Figure 3. Other condensate input data is shown in Table 1.
+
+
+Figure 3View largeDownload slidePhase envelope diagram for reservoir condensate fluid sampled from well O7 in Oredo field.Figure 3View largeDownload slidePhase envelope diagram for reservoir condensate fluid sampled from well O7 in Oredo field. Close modal
+
+
+Table 1Oredo field condesate model input data. Pi (psia) 4,030 T (°F) 189 Pd (psi) 4,030 ɸ (-) 0.25 Pwf (psi) 4,000 h (ft) 6 µo (cp) @ 189.3 °F 0.098 k (mD) 706 ρo (kg/m3) 561 rw (ft) 0.33 Pi (psia) 4,030 T (°F) 189 Pd (psi) 4,030 ɸ (-) 0.25 Pwf (psi) 4,000 h (ft) 6 µo (cp) @ 189.3 °F 0.098 k (mD) 706 ρo (kg/m3) 561 rw (ft) 0.33 View Large
+
+
+Results and analysis
+
+
+Fig. 3. shows the phase envelope associatesd with the Oredo O7 reservoir. The conceptual application of choking in the condensate buildup around the wellbore involves four scenarios viz: choking at the wellhead with varying tubing head pressures, bottom of the tubing constrained by the bottomhole dewpoint pressure, fixed at 4000 psia for all simulation purpose. The design implementation is shown in Figure 4. The bias of the PID controller output signal is 0.2 (i.e. an initial opening of the valves). This was varied up to a value of 0.8, the value used in the reported numerical simulation outputs. The amplification factor is set to 0.02 and the integral time was set to 1E10s which indicates no integral effect. The stroke time, opening time and closing time are all set to 10 sec.
+
+
+Figure 4View largeDownload slideProcess design implementation of the long string completion model of Oredo-7 well. A dual choking strategy involving surface and downhole choking was implemented.Figure 4View largeDownload slideProcess design implementation of the long string completion model of Oredo-7 well. A dual choking strategy involving surface and downhole choking was implemented. Close modal
+
+
+The well performance was monitored using profile graphical plots such as Figure 5 showing the total liquid volume flow (m3/d) along the tubing length and trend plots. In Figure 5, the total liquid volume flow gradually increases at the bottom of the well as the reservoir fluid gets closer to the dewpoint pressure. As the PID controller gets activated, the liquid volume flow reduces paving way for production of gas to be optimised. Figure 6 is a trend plot showing gas volume flow (sm3/d) at standard conditions. As the system tends to hit the dewpoint, the PID controller gets activated allowing flow thereby providing an optimum condensate well performance.
+
+
+Figure 5View largeDownload slideTotal liquid volume flow (m3/d) along the tubing length at steady-state.Figure 5View largeDownload slideTotal liquid volume flow (m3/d) along the tubing length at steady-state. Close modal
+
+
+Figure 6View largeDownload slideA trend plot showing standard gas volume flow, QGST (sm3/d) at the wellhead (an observation point). Imposed tubing head pressure constraint is 200 psia.Figure 6View largeDownload slideA trend plot showing standard gas volume flow, QGST (sm3/d) at the wellhead (an observation point). Imposed tubing head pressure constraint is 200 psia. Close modal
+
+
+Figure 7 is a trend plot showing standard gas volume flow, QGST (sm3/d), pressure profiles at the wellhead and bottomhole, liquid volume fraction at the wellbore and total liquid content along the tubing branch with imposed tubing head pressure of 180 psia. An early time gas flow fluctuation manifests as a result of the activation of the PID controller minimising the effects of condensate banking whilst stabilising the bottomhole flowing pressure and optimising gas production.
+
+
+Figure 7View largeDownload slideA trend plot showing standard gas volume flow, QGST (sm3/d), pressure profiles at the wellhead and bottomhole, liquid volume fraction at the wellbore and total liquid content along the tubing branch. Imposed tubing head pressure constraint is 180 psia.Figure 7View largeDownload slideA trend plot showing standard gas volume flow, QGST (sm3/d), pressure profiles at the wellhead and bottomhole, liquid volume fraction at the wellbore and total liquid content along the tubing branch. Imposed tubing head pressure constraint is 180 psia. Close modal
+
+
+Figure 8 shows a trend plot of standard gas volume flow, QGST (sm3/d), pressure profiles at the wellhead and bottomhole, liquid volume fraction at the wellbore and total liquid content along the tubing branch with an imposed tubing head pressure constraint is 120 psia. The gas production optimisation persists over a much longer period of time while the liquid condensate content in branch decreases at a slightly steeper rate compared to previous case described by Figure 7.
+
+
+Figure 8View largeDownload slideA trend plot showing standard gas volume flow, QGST (sm3/d), pressure profiles at the wellhead and bottomhole, liquid volume fraction at the wellbore and total liquid content along the tubing branch. Imposed tubing head pressure constraint is 120 psia.Figure 8View largeDownload slideA trend plot showing standard gas volume flow, QGST (sm3/d), pressure profiles at the wellhead and bottomhole, liquid volume fraction at the wellbore and total liquid content along the tubing branch. Imposed tubing head pressure constraint is 120 psia. Close modal
+
+
+This analysis allows for a condensate system to be produced at varying flow regimes for a longer period with minimum intervention. A typical flow regime along the length of the production tubing at time t = 11 hours is shown in Figure 9. Stratified, annular and bubble flow regimes can be observed. The automation control system consisting of transmitter, PID controller and valves are collectively capable of prolonging production operation necessary to sustain the system pressure above dew point. A much longer gas production from the condensate reservoir can also be achieved within the scope of this application. Implementation of dual choke at the surface and subsurface brings about a stabilsed flow system indicating a possible prolonged period of production. Data generated from this investigation can be validated using experimentally obtained data under the same setup and consideration. The coupling of the lower boundary conditions to a dynamic simulator or a pseudo-radial model will enhance the simulation set up.
+
+
+Figure 9View largeDownload slideA trend plot showing flow regime along the length of the production tubing at 11 hours.Figure 9View largeDownload slideA trend plot showing flow regime along the length of the production tubing at 11 hours. Close modal
+
+
+Conclusions
+
+
+A simulation approach to optimising gas production from condensate well in Oredo field using PID controller, downhole transmitter, wellhead and bottomhole chokes is developed in this work. This method overcomes the potential risk of high backpressure imposed on the production tubing by manual choking or other control solutions using wellhead valve. A much longer gas production from the condensate reservoir can also be achieved within the scope of this application. Implementation of dual choke at the surface and subsurface brings about a stabilsed flow system indicating a possible prolonged period of production.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion ϕPorosity (-) ρFluid density (kg/m3) μFluid viscosity (cp) θWell inclination angle from horizontal (°) gAcceleration due to gravity (m/s2) f(t)Time function hFormation thickness (ft.) KFormation Permeability (mD) rwWellbore radius (ft.) TTemperature (°F) PwfBottom-hole flowing pressure (psia) PdDew-point pressure (psia) ACross-sectional area, (in2) UGas velocity (ft/s) SFluid saturation (-)
+
+
+Abbreviations
+
+
+AbbreviationsAbbreviationExpansion BHPBottom hole pressure PIDProportional-integral-derivative CFLCourant-Friedrich-Levy WHVWellhead valve BHVBottomhole valve SCADASupervisory Control and Data Acquisition
+
+
+Acknowledgment
+
+
+The authors wish to express their gratitudes to the Nigerian Petroleum Development Company (NPDC) for providing access to the data used in this project. The authors are also grateful to Schlumberger for providing academic licence of transient multiphase dynamic wellbore simulator Olga™ used in this work.
+
+
+References
+
+
+Dada, A., Muradov, K., Wang, H., and Nikjoo, E., VillarrealE., Davies, D. (2018). Mitigation of the Remote Gauge Problem in Temperature Transient Analysis. SPE paper 190863 presented at the 80th EAGE Conference and Exhibition held in Copenhagen, Denmark, 11-14 June 2018.Google Scholar Dala, J., Akanji, L., Bello, K., Olafuyi, O., and Jadhawar, P. (2021). A Pseudo-Radial Pressure Model for Near-Wellbore Condensate Banking Prediction. Paper presented at the SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, August 2021. doi: https://doi.org/10.2118/208449-MS.Google Scholar Feng, H., Yin, C.B., Weng, W.W., Ma, W., Zhou, J., Jia, W.H., and Zhang, Z. Li. (2018). Robotic excavator trajectory control using an improved GA based PID controller, Mech. Syst. Signal Process. 105 (153–168). doi:10.1016/j.ymssp.2017.12.014.Google ScholarCrossrefSearch ADS  Janiga, D., Czarnota, R., Stopa, J., WojnarowskiP., and KosowskiP. (2018). Utilization of nature-inspired algorithms for gas condensate reservoir optimization. Soft Computing, 3May, Volume 23, p. 5619–5631Google ScholarCrossrefSearch ADS  Liang, H., Zou, J., Zuo, K., and Khan, M.J. (2020). An improved genetic algorithm optimization fuzzy controller applied to the wellhead back pressure control system, Mechanical Systems and Signal Processing, Volume 142, 106708.Google ScholarCrossrefSearch ADS  McLean, D. and Goranson, H. (1997) Gas Well Production Optimization Using Expert Systems Technology" SPE paper 38807 presented at the Annular Technical Conference and Exhibition held in San Antonio, Texas, 1997.Google Scholar Seah, Y.H., Gringarten, A.C., Giddins, M.A., and Burton, K. (2014). Optimising Recovery in Gas Condensate Reservoirs. Adelaide, Society of Petroleum Engineers.Google ScholarCrossrefSearch ADS  Stoisits, R.F., Scherer, P.W., and Schmidt, S.E. (1994). Gas Optimization at the Kuparuk River Field. SPE paper 28467 presented at the Annual Technical Conference and Exhibition held in New Orleans, U.S.A, 1994.Google Scholar Risan, R.M., AbdullahS., and Hidayet, Z. (1988). Condensate Production Optimization in the Arun Gas Field. OSEA paper 88200 presented at the Southeast Asia Conference held in Singapore, 1988.Google Scholar Schlumberger (2012). OLGA Dynamic Multiphase Flow Simulator.Scott, C.E. (2001). Computer Applications to Enhance Production Optimization and Process Efficiency. SPE paper 72168 presented at the Asia Pacific Improved Oil Recovery Conference held in Kuala Lumpur, Malaysia, 2001.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212047-MS
+
+
+

files/2022/Corporate Social responsibility A Paneacea for sustainable Development in Niger Delta Region of Nigeria.txt

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+Title: Corporate Social responsibility: A Paneacea for sustainable Development in Niger Delta Region of Nigeria
+Authors: Humphrey Otombosoba Oruwari
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211934-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The objective of this study is to investigate the extent to which corporate social responsibility programme of oil and gas companies contribute to the social economic development in Niger Delta region that host oil and gas operations. Several stakeholders, namely Government leaders, community leaders and other members of oil and gas operating communities in Niger Delta are clamouring for a bigger share of revenue deriving from oil and gas operations in their areas in an effort to achieve a level of socio-economic development that is commensurate with the level of petroleum extraction in their areas of operations to reduce resource curse or the paradox of poverty that host oil and gas companies. Meanwhile, the oil companies believe that through their Corporate Social Responsibility programmes CSR, they are significantly contributing to sustainable socio-economic development of the rural communities that host their oil and gas operations. This scenario presents a gap and a conflict which necessitated an investigation into the study. The methodological framework employed in the study is that of literature review and multiple case study of some corporate social responsibility programe of some oil and gas companies. The study finding indicates that there are CSR programme like road construction, borehole water infrastructure, building of school and health facilities and award of scholarships. This entrench the believe in the oil and gas companies that their corporate social investment programme is more than adequate, however, the indigenous people in the host communities feel that these social investments are inadequate. To solve this problem, foster unity and harmony between key stakeholders in the oil and gas communities, the study concluded that a minimum corporate social investment threshold based on percentage of revenue be set and applied uniformly across the petroleum industry in Nigeria. In addition, each oil and gas company should make an annual CSR report to show the attainment of minimum annual corporate social investment. This will remove opacity and enhance transparency, uniformity and predictability in the CSR programme of oil and gas companies in Niger Delta, with the level of socio-economic development reflecting the level of oil and gas endowment and extraction in the petroleum bearing communities.
+
+
+
+
+Keywords:
+responsibility,
+niger delta,
+corporate social responsibility,
+social responsibility,
+programme,
+community,
+development,
+upstream oil & gas,
+csr,
+nigeria
+
+
+Subjects: 
+Sustainability/Social Responsibility,
+Social responsibility and development
+
+
+
+
+Introduction
+
+
+Historically, the petroleum industry has been perceived to be focused on profit maximization, paying little or no attention to its negative impact on communities and the environment. This is inline with Ibibia (2002) observation that the underlying objectives of the principal actors in petroleum investment, (though as divergent as they are varied) can be summed up essentially as the maximization of their respective investment at the least possible cost risk. Environmental protection and sustainable development were hardly within the contemplation of these principal actors. Environmental costs were not internalized into the overall operational and capital cost.
+
+
+The only contribution to society and the environment has been largely discretionary, philanthropic and mainly about reputation management. It is therefore imperative that Corporate Social Responsibility/Investment (CSR/I) projects should be employed in ways that enhance socio-economic development of communities. This is in line with the principles of social contract which postulates that a business organization is supposed to give to the environment where it is operating. This points to the need to employ CSI strategies that do not compromise the profit maximization objectives of petroleum companies, also known as strategic CSR (Olirtzky et al., 2011). However, the challenge is on how to conceptualize and implement such strategies that are mutually profitable to oil and gas companies and host communities.
+
+
+Several oil and gas companies operating in Niger Delta have been performing corporate social investment over the years. However, the level of social investment varies from company to company over the years dues to different approach and level of commitment to socio economic development to the host community. Consequent upon the disparate CSR programmes of the different oil and gas companies, the impact on the socio-economic development of the various oil and gas communities is also varied as there are no standardized approach to implementing CSR. Also, since CSR is used as tool for socio economic development, it results in significant shift in the fortune of Niger Delta. Hence it is necessary to unlock the capability of CSR and leverage on the existing oil and gas companies to address the various socio-economic challenges facing the troubled Niger Delta.
+
+
+In Niger Delta there is disagreement between host communities (communities that host the oil and gas companies) and Federal government on one hand and oil and gas companies on the other, pertaining to the level of effectiveness of CSI by the oil and gas operator in the socio-economic development of host community. The oil and gas companies believed very strongly that they are adequately investing in host community through CSR program. Globally, however, contradictory interpretation and thought on CSR also exist. The growing importance of CSR has made it necessary for every firm to use international benchmark as standard to device their CSR practice accordingly. Dijken (2007) suggests that CSR works well if it creates a "win-win" situation where a company performs well by virtue of operating in a socially responsible way. Baker (2001) argues that CSR is not simply philanthropy, suggesting that CSR initiatives should be tactical and strategic. Thus, CSR requires a number of enabling factors in order to generate sustainable development impact as part of overall long-term strategy. However, it is complex to formulate a broad development plan based on CSR (Frynas, 2005). Against all these complex nuances, the fundamental task of this study is to assess the extent to which CSR has been a vehicle of sustainable socio-economic development.
+
+
+Literature review
+
+
+Corporate social responsibility
+
+
+CSR is a drive towards sustainability this is in line with Emma et al (2010) submission that Corporate social responsibility (CSR) is one of the critical success factors in oil and gas production. It is also the philosophy of sustainable development that many argue is the only way forward for the world economy. In the view of Chikwe (2012), CSR is a set of standards by which organizations can positively impact their operating environment with the innovation potential and creating sustainable economic growth and developments. Social responsibility refers to the obligation of business organisation to adopt policies and plans of action that are desirable in terms of expectations, values and interest of the society. It ensures that the interests of different groups of the public are not adversely affected by the decisions and policies of the business. The idea of CSR essentially means that business organisations have responsibilities that go beyond mere profit making and encompasses voluntary activities and actions that affect people, their community and natural environment.
+
+
+Adeola (2002) opined that in order to bring about peace which is necessary for economic development and also the successful operations of oil field, the empowerment of the Niger Delta people is important for various reasons. It is a win-win situation and would lead to security of lives and properties. Also, the UN Global compact initiative, launch in 2000, underline efforts aimed at encouraging business participants to improve on their corporate social and environmental behaviour. The global compact places emphasis on policy dialogues as a means of achieving compact goals. The principal aim of the dialogue is to quicken and ensure "mutual understanding and joint efforts among business, labour and nongovernmental organisation (NGO). The objective is to influence policy-making and the behaviour of all stake holders. Also, recall the Millennium development goal 8 -developing a global partnership for development. By encouraging business to implement labour, environmental and human right standards the UN global compact intend to spread the benefits of globalization more widely so as to give capitalism a human face and thus counter the growing perception and disenchantment. Government only cannot ensure peace. Partnership for peace should involve government, international organisation, business communities and civil society.
+
+
+The benefits implementing CSR
+
+
+The level of CSR engagement is dependent on corporate objective or ambition, whether the companies regard CRS as a value, a source of competitive advantages or risk management, all of which are intangible asset of modern business. Rushton (2002) noted the benefit of CSR is linked to value and brand so they are intangible asset of company. Welford (2002) opined that CSR can result in enhanced image and value. CSR is linked to competitive advantages and several authors emphasized that taking a philanthropic view of CSR will bring true competitive advantages to companies and their supply chain partners; according to Porter, (2003: 28) the truly strategic way to CSR is to realize that philanthropy in the competitive context align with social and economic goals and improves the company's long term business prospect". In Harvard business review (HBS 2003), Smith emphasized that philanthropy and business units have joint forces to develop philanthropic strategies that give their company a powerful competitive edge.
+
+
+Draw back/ limitation of CSR
+
+
+It is worthy of note that lack of national macro-economic planning and management backed by an enabling environment have significant implication for the overall performance of CSR initiative by company especially oil and gas in developing country. In order words, if the macro economy is under performing due to government failure. There is likely wood that the contributions of oil and gas companies to the host communities could fail to achieve the desired outcome. Good governance is therefore an important component of CSR agenda (Ite, 2004).
+
+
+Sustainability -Corporate Social responsibility
+
+
+Sustainability demands that the behaviour of a corporation aligns with the broader goals of society, including both economic and non economic growth. CSR requires corporation to identify their sustainability responsibilities and to take voluntary steps to improve their social and environmental performance in harmony with economic/ financial benefits.
+
+
+Ibibia (2002) opined that considering the underlying aim of the petroleum investor does not necessarily always coincide with those of the host government, environment protection and sustainable development and corporate social responsibility obligations may be viewed with cynicism by the former. Nevertheless, it would be too presumptuous to say that this sceptism drives every petroleum investor to deliberately violate environmental obligations as a technique to boost the return on investments. Even then it may be too presumptuous to posit that, there is a general propensity to comply. However, between these two extremes, there must be a middle ground, where rationality tamed by current developments in environmental laws and policy should result into a new imperative for both government and business. If they are to achieve the much neede legitimacy in the pursuit of of their respective divergent interest. These new imperatives obviously question the mundane view that business and government exist exclusively to maximise the rate of return on their investments.
+
+
+Sustainable development by Total Energy Company
+
+
+The term sustainable development has been widely defined as development that meets the need of the present generation without compromising the ability of future generations to meet their own needs. Total energy has a adopted a working definition of sustainable development of "achieving a harmonious balance between people's need and earth resources between benefits and costs in both a short and long term"
+
+
+Total Energy has had a fair share in the demonstrations within the industry as described earlier. Much of its past effort to enhance relationship and deliver development have yielded only little benefits. The company has identified some basic parameters for the development of sustainable community development.
+
+
+Broadbased development (one not restricted to host communities)Community ownership of its development. This enhances loyalty, sense of belonging through self-development action identification, planning and implementing as well as reducing suspicionCommunity workforce enlightenment. Focusing on key and current issues, research findings and specialization raining particularly important for safe guarding of assetPeople and the environmentAccelerating partnership for growth of peaceful coexistentPoverty alleviation through greener habitsProject sustainable impact assessment- An integration of mitigative measures for adverse impact relating to economic, physical, social, health and developmental issues. This is established with the full participation of all stakeholders especially community representative as early as possibleContinued assessment and dialogue during all phases of project development.All sustainable development programmes project to be subjected to independent verification
+
+
+Selected action on sustainable community development
+
+
+Total energy is approaching this from angles of environment, economics and social delivery. From the perspective of economic delivery, capacity building through skill acquisition and enterprise development initiatives are being vigorously pursued. Community Development Foundation at variou levels, one can observe abandoned development project all over the federation from the coast to the fringes of Sahara, in hamlets as well as megacities. Collosal sum of money and other resources have been lost as a result.
+
+
+Methodology
+
+
+The study adopted a literature review and multiple case study of indigenous and international oil company and CSR they carry out during oil field development project. As succinctly opined by Baridan (2001), the nature and purpose of study should dictate the method to be adopted in any study. In relation to this assertion, we explored the existing and thematic related literature in order to achieve our set objectives of the study.
+
+
+Result and discussion
+
+
+Case studies of the management of local relationship in the development of oil fields the Umusedege, Ogebelle and Asuokpu/Umutu oil fields
+
+
+A community relation is the process of building and sustaining a good relationship between oil companies and its host Communities in order to provide a conducive environment for its operations. It provides a proactive guideline in ensuring that Environmental, Cultural, Social and Economic issues that bother on the welfare of the host Communities are adequately addressed in its area of operation. Wade (2012) posited that, historically, many oil companies have mismanaged relationships with the local communities and this has led to many difficulties in oilfield operations and the relationships upon which they are built. However, Mart resources undertook the following steps in the development of the Umusadege field:
+
+
+Build a strong partnership with a local Nigerian oil companyForm integrated field development teams from Mart and local partners (joint control) The Joint Venture operations maintain more than 85% Nigerian workforceEngage local communities in the operations, providing employment and educational opportunitiesIncreasing Local Content by using local service companies, laboratory services, and personnel
+
+
+According to Wade (2012), managing partner and local community relationships has proved to be a challenge for many oil field operators. Also, many oil companies have historically mismanaged relationships with the local communities, which have led to difficulties in oilfield operations. Mart's success has been a result of maintaining good working relationships with its partners and local communities. Wade (2012) posited that: "Midwestern Oil and Gas implements social projects and financially supports various social groups. These programs help to increase living standards for people as well provides opportunities for personal development".
+
+
+The host community is recognized as important partners. The willing active support of the communities as partners is indispensable to the business. Emphasis was placed on enhancing relationship with the community. Oil and gas companies should consider social projects and involvement in the development of local community as an important part of its activity, readily participating in the activities of local community and developing good relationships with it. Oil and gas companies should invest in building sustainable programme in the community.
+
+
+According to MPN SEPLAT (2012), Seplat leverages the successful onshore track record of platform (Asuokpu/Umutu marginal field-OML38) regarding community relationship management. Host communities were regarded as stakeholders and capacity building and empowerment a primary objective in the partnership. The host communities consist of four major stake holding communities and thirty-five others of smaller size. The company is committed to a programme of proactive engagement with the communities to implement sustainable development programmes. Community engagement forum is used to dialog with the communities and keep them abreast of the company's operation. There is the development of a 5-year general memorandum of understanding with the host communities. Also, there were formal courtesy visit to the traditional rulers within the SEPLAT's areas of operation.
+
+
+Table 1 shows the corporate social responsibility of some marginal oil field operators which were awarded license in 2003 to develop the field. A marginal oil field is one that the profitability may be low due to some unfavorable conditions.
+
+
+Table 1Corporate social responsibility initiative by marginal field development companies MidwesternAssisted flood victims during the flood disaster of 2012.Construction of 500m drains system within Umusadege communityConstruction of 1.5km interlock road in UmusadegeAwarded 42 scholarship to different categories of students worth 150,0te00 to students drawn from the community and across the stateProvided boreholes and modern toiletsProvision of 20 bed amenity ward at the kwale general hospital and fully equippedAlso as part of its Midwestern oil and gas in collaboration with Suntrust oil company Ltd. Built and donated blocks of 12 class to Utagba Ogbe- communities, Kwale, Delta state. Platform petroleum Provision of water scheme for Ogbe-uzu community completed in 2007 and in use. Running cost till date is on PPL/NP2.Street light project completed in 2007, in line with MOUBridge construction at Adoneshaka, across Ethiope, project completed in 2008 and now in useConstruction of welding shade for Adoneshaka communityConstruction of town hall project at Ogbe-Uza community, project completed in 2008 in line with MOUBuilt class room for host community.The company donated an Ultra-modern building to geology department of university of Nigeria (UNN) Universal energy resourcesExpand 50 M annually on CSR project and programme under with the existing Memorandum of understanding (MOU) with the communities.Scholarship worth N 3.1 m and micro credit scheme with N2 M were given to the communitites across universal energy resources limited/SIPEC operational areas.They are also involved in Human capacity development and the establishment facilities with the host communities Energia Petroleum/Oando Formal education and skill acquisition program for its host community.Empowering the youths of its host/Impacted communities thereby Sustained peace in Niger Delta regio Oriental EnergyFocus on capacity building initiatives such as training and improved education.Employment of 100 key Afren Commercial, Legal, Technical and operational staff Pillar oilObserves all health and safety regulations.Establish cordial relationship with relevant Federal and State government agencies remain cordial MidwesternAssisted flood victims during the flood disaster of 2012.Construction of 500m drains system within Umusadege communityConstruction of 1.5km interlock road in UmusadegeAwarded 42 scholarship to different categories of students worth 150,0te00 to students drawn from the community and across the stateProvided boreholes and modern toiletsProvision of 20 bed amenity ward at the kwale general hospital and fully equippedAlso as part of its Midwestern oil and gas in collaboration with Suntrust oil company Ltd. Built and donated blocks of 12 class to Utagba Ogbe- communities, Kwale, Delta state. Platform petroleum Provision of water scheme for Ogbe-uzu community completed in 2007 and in use. Running cost till date is on PPL/NP2.Street light project completed in 2007, in line with MOUBridge construction at Adoneshaka, across Ethiope, project completed in 2008 and now in useConstruction of welding shade for Adoneshaka communityConstruction of town hall project at Ogbe-Uza community, project completed in 2008 in line with MOUBuilt class room for host community.The company donated an Ultra-modern building to geology department of university of Nigeria (UNN) Universal energy resourcesExpand 50 M annually on CSR project and programme under with the existing Memorandum of understanding (MOU) with the communities.Scholarship worth N 3.1 m and micro credit scheme with N2 M were given to the communitites across universal energy resources limited/SIPEC operational areas.They are also involved in Human capacity development and the establishment facilities with the host communities Energia Petroleum/Oando Formal education and skill acquisition program for its host community.Empowering the youths of its host/Impacted communities thereby Sustained peace in Niger Delta regio Oriental EnergyFocus on capacity building initiatives such as training and improved education.Employment of 100 key Afren Commercial, Legal, Technical and operational staff Pillar oilObserves all health and safety regulations.Establish cordial relationship with relevant Federal and State government agencies remain cordial View Large
+
+
+Adapted from: Success factors for marginal oil field development in Niger Delta region (2018)
+
+
+In table 2 the CSR variable was coded as the dependent variable because of the broad questions about the nature of CSR policy in the various oil and gas activities. The elements which are listed in the column of independent variables were categorized as such because they reflect the various dimensions in which the study sought to determine the impact of CSR on. The volume of data was quite overwhelming and so for the purpose of the summary, only data which directly meet the requirements of the study objectives is presented.
+
+
+Table 2 Independent Variables
+            . Dependent Variables
+            . Employment Corporate Social responsibility (CSR) Ecological environment Social infrastructure empowerment communication Community participation Health and safety Levilihood support Sustainability Independent Variables
+            . Dependent Variables
+            . Employment Corporate Social responsibility (CSR) Ecological environment Social infrastructure empowerment communication Community participation Health and safety Levilihood support Sustainability Source: authors computationsView Large
+
+
+Niger Delta Exploration Company (NDEP) and the Host Community Relation. A Model of Sustainable Community Development
+
+
+NDEP (with the other companies to which it is affiliated) has a general philosophy concerning the management of host community relations that is being adopted for and will guide the implementation of the Ogbelle project a marginal field. The essence of NDEP's host community relations philosophy can be summarized as follows:
+
+
+An indigenous oil and gas group, NDEP uses its better understanding of the problems faced both by the operating companies and the host communities in approaching the development of the environment in which it operates as well as issue of compensation payments.
+
+
+For each of its project, NDEP sets up a joint venture. A condition of each of these ventures is that 5% (five percent) of the net pre-tax profits of the joint venture is ploughed back into an endowment fund of a Community Development Trust established by NDEP for the benefit of the host communities in which the projects’ development facilities are situated.
+
+
+In each case, the relative project joint venture will include NDEP, a public company in which the Host Communities will be invited to invest in the same way as everybody else. In this way, each Host Community, and each individual unit, can indirectly own equity in the project to the extent that it can afford.
+
+
+The aims and objects of the aforementioned community and Environment Development Trust fund include:
+
+
+Understanding and financing of host community projects for the advancement and sustainable development of host communitiesAdvancement and propagation of education and learning generally in the Host CommunitiesEnhancing and supporting local initiatives that deals with protection of the environment in the host CommunitiesFacilitating employment opportunities and capacity building in the Host CommunitiesDevelopment of projects of public utilities to the Host communitiesAdvancement of other purpose beneficial to the Host Communities in general
+
+
+NDEP or its associates structured the Community and environmental Development trust to operate ultimately as a distinct corporate body whose operations, though supported by NDEP and its associated companies is not managed by it. It has representation from the Host communities on its advisory board and is overseen by professionals in various discipline as appropriate. There are separate funds for each set of Host communities relative to each project.
+
+
+The community and Environmental Development Trust Endowment fund is invested to produce an income that is used to support Host Community development project. Trust consulted appropriate local stakeholders to identify such projects. Since the income from the Endowment Fund will continue to accrue even at the end of the project life, the trust may be able to take on continuing commitments.
+
+
+The trust at various time work in concert with relevant government agencies, local and international NGOs and private bodies whose interest are similar and which target the same beneficiaries. Proper execution of the model has greatly ameliorated most problems presently experiences regarding community relations in the development of oil and gas operations and resulted in the establishment of cordial relationship among all stakeholders in each project.
+
+
+For a couple of reasons, CSR is a prime issue in the development of oil/gas fields in Niger Delta where there is a very high sense of being marginalized and cheated out of the oil wealth the land produce and this situation has led to disharmony. In adequate corporate social responsibilities efforts and activities or inadequacy of it can result in many problems for the oil/ gas companies. These problem areas will include: Sabotage, Hostility, Arson, spreading of destructive rumours, fictitious petition writing company, Kidnapping, an outright attack on the one company's employee or installation, pilfering, stealing demonstration and destruction of oil/gas infrastructure. (Humphrey and Dosunmu 2017). The vulnerability of the infrastructure to sabotage is underscored by the poor property rights, enforcement regime and exclusion technology employed to protect it. The technology of excluding potential saboteurs and community collaborators from the complex oil and gas infrastructure in a sustainable manner is costly and implementing such technology may threaten the very profitability of investment in the infrastructure (Pedro and Joshua 2014). In Nigeria the people of Niger Delta region have staged several protests and demonstrations against the multinational oil companies operating in the region for violating air and land pollution and banning further exploration or drilling except where pollution standards are maintained.
+
+
+Conclusions and Recommendations
+
+
+Conclusions
+
+
+The findings of the data collected and analysed proved the following:
+
+
+That the community leaders, including chiefs, village heads, local authority officials and other administrators, believe that oil and gas companies must invest more in the host communities.Ordinary members of the oil and gas communities also believe that the CSI by oil and gas companies in infrastructure like roads, water, clinics, training, etc is inadequate.Both community members and their leaders believe that the host communities are entitled to a share of revenue from oil and gas activities in their area through CSI.Oil and gas companies, on the contrary, believe that they are investing significantly in the socio-economic development of their host communities through their CSR programmes.The levels of CSI differ from company to company as there is no set formula or amounts to be invested in CSR programmes.
+
+
+The problem identified in the beginning was therefore confirmed by the study findings which necessitates recommendations to mitigate the problem.
+
+
+Recommendations
+
+
+In pursuit of a durable solution to the problem that was confirmed by the findings of the research, the following recommendations are proffered by the study:
+
+
+That Government carries out a petroleum industry consultative process to come up with a CSR policy for all petroleum companies that stipulates a percentage of revenue that all oil and gas companies are required to invest in the host community annually. This is in line with the Petroleum industry act (PIA) which recommends 3 % of operationg expenditure (OPEX) to be used as CSR in the host communities.CSR must start at the top and be considered as a factor in strategic planning by oil and gas industry and create social or organizational structure that supports the social responsibility frame work and constantly communicate the result of the ongoing effort to all stake holders and publicly celebrate success. That all oil and gas companies produce an audited Corporate Social Investment report showing CSR projects undertaken, impacts and the cost which must meet the minimum percentage set in 1 above.There must be a community needs assessment and CSR projects prioritisation involving the oil companies, community leaders and community members annually.The researcher proposes that for Government policy to be effective and benchmarked to international best practice, the policy review must be based on relevant and applicable aspects of the following guidelines:The International Council on Sustainable Development Framework for the petroleum industry which was crafted to enhance transparency on CSR in host communities.The Extractive Industry Transparency Initiative (EITI)OECD Guidelines for Multinational Enterprises (1976)Global Reporting Initiative (GRI) (2000) – Sustainable Reporting GuidelinesUnited Nations Global Compact (2000)Equator Principles (2003)ISO26000 Guidelines on Social Responsibility (2010)
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+AdeolaAdekinju (2002): A paper delivered at the conference on sustainable development: resources ownership in Nigerian by the institution of public policy analysis on September 18, 2002. Federal palace hotel, Victoria Island, Lagos.Google Scholar Baridam, D.M. (2001). Research Methods in Administrative Sciences. Diobu, Port Harcourt: BELK PublishersGoogle Scholar Baker, J. (2001) OECD conference on corporate social responsibility. "Freedom of association and CSR": available at:http://www.oecd.org/pdf/M00003000/M00003662.pdf [Accessed 15 Oct 2018].Google Scholar Chikwe, J.E. (2012). Corporate social responsibility and organizational effectiveness of oil companies in Nigeria. An unpublished Ph. D Thesis, Department of Management, Rivers State University of Science and Technology,Port Harcourt, NigeriaGoogle Scholar Dijken, F. V. (2007). Corporate Social Responsibility: Market regulation and the evidence. Managerial Law (Emerald)49(4), 141– 184Google ScholarCrossrefSearch ADS  Emma IfeanyiOgueri, IkeNwachukwu b, RayUnamma (2010): Critical success factors affecting sustainability of oil and gas production in Niger Delta, Nigeria. Available athttp://www.ssrn.com/link/OIDA-Intl-Journal-Sustainable-Dev.html accessed on 24th December 2012Google Scholar Ite, U. E (2004): Multinational and corporate social responsibility in developing countries. A case study of Nigeria, corporate social responsibility and environmental management, Vol II no.1 Pp 1–11March2004Google Scholar MPN SEPLAT (2012): MP Nigeria management presentation October 2012.Niger Delta Exploration and Production (NDEP): The Ogbelle Fields Development Project and other Upstream Petroleum Industry Project. ProspectusJuly2001:Olirtzky, M., Seigel, D.S., Waldman, D.A. (2011) "Strategic Corporate Social Responsibility and Environmental Sustainability", Business and Society, Vol.50 No.6: Sage Publications.Google Scholar Oruwari, Humphrey O: AdewaleDosunmu (2017): Constraints in sustainable development of marginal oil field in Niger Delta: SPE paper no 189060: Nigeria Annual International conference and exhibition heldLagos, Nigeria, 31 July- 2 August 2017.Google Scholar Frynas, J.G. (2005). The false development promise of Corporate Social Responsibility: evidence from multinational oil companies. International Affairs, 81(3), 581–598Google ScholarCrossrefSearch ADS  Havard Business School (HBS) (2003b) Harvard Business Review on Corporate Responsibility, The New Corporate Philanthropy, Harvard Business School Press.Ibibia, L. W (2002): Environmental Law and policy of Petroleum Development. Strategies and Mechanisms for Sustainable Management in Africa. Pp 5-6and Pp 296. Published by Anpez Center for Environment and Development Port Harcourt.Google Scholar PedroEgbe and JoshuaGogo (2014): Comprehensive response to the threat of environmental degradation and oil and gas infrastructure integrity violation in Nigeria: Nigeria oil field technology review- June 2014, vol. 2 issue 2Google Scholar Porter, M. (2003:23) Harvard Business Review on Corporate Responsibility, The Competitive Advantage of Corporate Philanthropy, Harvard Business School Press.Google Scholar Rushton, K. (2002) "Business Ethics: A Sustainable Approach", Business Ethics: A European Review, April, Vol.11, No. 2, pp.137–139Google ScholarCrossrefSearch ADS  Wade, C: (2012): Mart resouces Inc: A Nigerian marginal field case study. A corporate presentation by Mart resources Inc, 9th October, 2012 at the North American Assembly, the Four Season Hotel. Houston Texas: available at www.martresources.com accessed on 15th December 2012.Google Scholar Welford, R. (2002) "Globalisation, Corporate Social Responsibility and Human Rights", Corporate Social Responsibility and Environmental Management, Vol.9, No.1, pp.1–8.Google ScholarCrossrefSearch ADS  
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211934-MS
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files/2022/Cost Optimization by Designing an Ultra-Slim Horizontal Well in the Niger Delta The Eremor Field Case Study.txt

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+----- METADATA START -----
+Title: Cost Optimization by Designing an Ultra-Slim Horizontal Well in the Niger Delta – The Eremor Field Case Study
+Authors: Ugo' Okoli, Hope Okwa, Segun Adebayo, Ifiok Mkpong
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212041-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Oil price volatility is one of the major drivers, which drive the decision of Operators to drill oil wells to further develop oil fields. A more significant constraint, which deals a huge blow on the Marginal Field Operators in the Niger Delta is the huge and ‘unavailable’ CAPEX associated to the delivery of these wells.This paper elucidates how ‘detailed’ well design and optimization were used to design and deliver two swamp wells for a Marginal Field operator in the Niger Delta. With the application of detailed engineering and optimization processes, the well costs were reduced by over 50%.The wells were initially designed, and to be delivered for circa $13MM per well, which is the P50 cost of drilling Swamp wells in the Niger Delta. However, post design optimization, the wells were designed and delivered for circa 6.5MM per Well.The paper also details the drilling execution methods put in place to ensure that the wells designed were delivered efficiently.
+
+
+
+
+Keywords:
+drilling operation,
+well planning,
+upstream oil & gas,
+asset and portfolio management,
+spe nigeria annual international conference,
+case study,
+eremor field case study,
+optimization,
+drilling engineer,
+exhibition
+
+
+Subjects: 
+Well Planning,
+Drilling Operations,
+Asset and Portfolio Management,
+Professionalism, Training, and Education,
+Information Management and Systems,
+Directional drilling,
+Communities of practice,
+Knowledge management
+
+
+
+
+Introduction and method
+
+
+The "Horizon A & B" project was planned and delivered at a time when the world was just trying to recover from the COVID-19 pandemic, as the world literarily came to a grinding halt. Airports were closed, flights were grounded, companies had to downsize prevent a total collapse. This downsizing extremely affected the oil producing companies, because oil and gas products were marginally used.
+
+
+At this time, all the metrices used for financial analysis estimated the project to be marginally profitable, thus it didn’t make sense to embark on a drilling campaign at this time.
+
+
+However, it was very important to the operator at this time to drill more wells and further develop the block, and it was imperative to deliver the project within the planned time and budget.
+
+
+Prior to the commencement of the detailed well design of the development wells, a detailed review of the offset information was carried out to ensure that all good practices were replicable or even improved upon This information was used to enhance the of all the planning and operational phases of the project. It was also required to adhere to the guidelines of the new variable ‘COVID-19’, put in place by regulatory bodies.
+
+
+Some other processes used to ensure optimization are made lucid below:
+
+
+Concept Select and Basis of Design Optimization
+
+
+The following optimization initiatives were identified and implemented during this phase:
+
+
+Well Architecture: The offset wells constituted of a 20" conductor (driven to refusal), 13-3/8" surface casing (post drilling 17-1/2" surface hole), 9-5/8in casing (post drilling 12-1/4" intermediate section). To reduce the capital expenditure (CAPEX), the well size was scaled down to 20’’ conductor, 7" surface casing, 6" landing & lateral’’ hole sections with a 4-1/2’’ lower completion assembly and 3-1/2" upper completions.By reducing the hole size and casing string, circa $2MM per well was saved, further savings were achieved from the reduced logistics, reduced drilling fluid and cement volumes, OCTG and waste management costs.Detailed Offset Review & Analogue Wells: A detailed review of analogue wells was conducted and analysis showed that this well could be drilled and completed in 10 days. Some key activities and drilling parameters were studied and included into the Horizon A & B project.Optimizing the Directional Plan and Well placement: The two planned wells were planned to be drilled in close proximity with existing wells, this raised some collision issues. Thus, an additional area had to be dredged to further optimize well placement and reduce/eliminate any collision risk. Certain processes were put in place to ensure the collision issues are annulled; they include: Early/shallow nudge of the well at circa 300-400ft (right below the conductor shoe), to increase the separation factor from other wells and minimize the collision risk.Keeping DLS at maximum of 3deg/100ft while drilling in all hole sections.Drill the surface holes directionally to place the wellbore as much as possible in the direction of the landing azimuth.It was also ensured that the planned trajectories were reviewed to ensure less tortuous well paths and low to moderate Directional Difficulty Index (DDI), while optimally placing the lateral sections away from the prognosed ratty sands.
+
+
+Figure 1View largeDownload slideFinal Well ArchitectureFigure 1View largeDownload slideFinal Well Architecture Close modal
+
+
+Figure 2View largeDownload slideOptimized Well profilesFigure 2View largeDownload slideOptimized Well profiles Close modal
+
+
+Rig Selection, certification and acceptance
+
+
+Owing to the supposedly "wrong timing" of the drilling campaign, getting a fit-for-purpose rig was a big task. Post identifying a rig technically suitable to deliver the planned wells, a robust rig inspection scope requiring expert surveyors (HSE, mechanical and electrical) was used to ensure that all critical equipment on the rig was fully functional and certified prior to accepting the rig. All critical and major deficiencies had to be rectified before the rig acceptance. The process steps used to certify the rig include;
+
+
+Equipment inspectionRepairing the defective equipment.Function testing / pressure testing of equipment.Certify equipmentRig up equipment.Valid certification of rig personnel
+
+
+On completion, a rig acceptance test was conducted for 5 days to ascertain that rig equipment was fully functional and ready for use.
+
+
+Contracting Strategy
+
+
+There was a decline in oil price, rig count was on a decline, and not cost effective to drill wells. Because of this constrain, the team came up with a lump sum strategy for all the contracts based on the low oil price at the time. This helped reduce significantly the spread rate during operations, thus the cost of the wells and the overall CAPEX of the project.
+
+
+Optimization of Drilling Operations (Operations and Logistics)
+
+
+The following optimization initiatives were identified and implemented during this phase: Driving the Conductor Pipes Offline: The initial plan was to drive the conductors with the rig. However, after a detailed analysis and evaluation it was confirmed that there was a huge saving potential, if the conductor pipes were driven offline. This was implemented successfully.Onsite Operations Supervision: During the execution phase of the project there was efficient and comprehensive monitoring of all the operations during the campaign. The field team constituted of the Drilling Supervisor and Night Drilling Supervisor, Completion Superintendent, Safety Officer, Wellsite Drilling Engineer and Logistics Coordinator.
+
+
+The onshore Operations Team constituted of the Drilling Engineer, Senior Drilling Engineer, and Chief Well Engineer. It was ensured that this team remained relatively constant throughout the drilling campaign, ensuring the continuity of personnel from well to well, and using the same operational standards and procedures.
+
+
+Communication Loop System
+
+
+To ensure that the detailed plan was efficiently executed, the planned well delivery meetings and comminications was adhered to. The following meetings were held as required to ensure that all pertinent information were communicated and understood.
+
+
+Pre-morning discussion with the Drilling Supervisor and rig team.Daily conference call in the morning by the Chief Well Engineer and rig crew.Evening operations update by 4.00pm, to bridge any possible gaps.
+
+
+The daily conference call hosted by the Chief Well Engineer commenced once the rig was on location. Participants included the Drilling Supervisor, Rig Manager, Logistics Coordinator, Safety officer Drilling Engineers and Completion Superintendent. Service Contractor staff were compelled to participate and contribute when necessary.
+
+
+Other Well delivery/pre-and post-operational meetings held which were used to closely monitor the operations include: Campaign Kick-off Meeting: The campaign kick-off meeting is the first meeting with the project team. It was held to introduce the "Horizon A & B" project to the Rig and service contractors. The meeting was also used to introduce all team members and discuss in detail the role of every team member.Drilling Well on Paper: Drilling the well on paper (DWOP) process involved working through each step of the operation in 15 – 30-minute intervals, assessing if each step could be optimized or improved and then agreeing on an empirical time to accomplish each task. The final timing from the review is the target time against which the rig is supposed to deliver. The COVID-19 protocols had to be observed and the DWOP was held virtually. It was ensured that the actual service contractor personnel who was going to do the job was available for the meeting. Comments and updates from the DWOP were then used to update the drilling program.Pre-spud Meeting: The pre-spud meeting was an overview of operation held at the rig site. It was a re-cap of everything that was discussed at the DWOP and was held prior to spudding the well. It was a more practical meeting where unclosed action items and processes were firmed up.Pre-Section Meetings: Pre-section meetings were held prior to every phase of the operation, just before the commencement of the phase. It was a "reminder" type of meeting that elaborates mostly on the required parameters (flowrates, mud weight, equivalent circulating density limits, Revolutions per minute (RPM) etc.). This meeting was usually a teleconference between Lagos office and the Rig site and was mandatory that all the rig and service contractors who would be part of the immediate operation be present for this meeting.Pre-Tour, JSAs, Toolbox: Pre-tour, JSAs and Toolbox meetings were held on the Rigsite immediately after the pre-section meeting and prior to the commencement of the operation. It served to further buttress all that was discussed at the pre-section meeting.Besides the meetings and workshops discussed above, there was regular communication with the Rigsite especially at nocturnal hours to ensure that jobs were executed as per plan. The drilling operations was also monitored "Real-Time" at the Lagos office, especially when it was required to land the well in the proposed reservoir and optimally geosteering the drain in the target reservoir sands.
+
+
+Safety
+
+
+Safety was ensured priority in every facet of the operation. Appropriate risk assessment of all operations had to be conducted before any operation commenced.
+
+
+To forestall possible significant safety incidents, "Stop work Authority", an initiative aimed at preventing incidents in operation was enacted. The process ensured the following: Full stop card participation, (100%) by everyone on the rig. This was used to track and curb leading indicators that could become incidents.That the rig and service contractors comply with the standard international HSE requirements.Detailed risk assessment was carried out prior to any operation; the risk assessment was further reviewed during the Drilling Well on paper to ensure all risks were captured and mitigated.With this in place, the drilling contractor, with the full support of management achieved a high creditable record and standard of safety. This included an effective safety hazard observation card (SHOC) system, that encouraged all members of the crew to recognize and record good and bad practices. No FAT, LTI or RWDC was recorded throughout the six (6) months of operation.The safety objective was made clear in all documentation as follows: "The principal objective was to deliver the planned work scope without any incidents, high potential near misses and uncontrolled discharges to the environment during the well construction process".
+
+
+"Horizon A & B" Operations Performance
+
+
+The first development well "Horizon"-A was drilled and completed in thirty-one (31) days. Being the first well of the project, there were a lot of improvement areas identified to help reduce the flat times. Some of these areas included (on-bottom times, ROPs, casing running speed etc.). This was also the first time this drilling crew was drilling an unconventional well. After a thorough review of the operational timings, the team were challenged to drill and complete the next well in twenty (20) days without falling short of the HSE targets. With continuous reviews and optimization plans, the second well was delivered in less than 29 days (This included additional scope of a Pilot hole, Plug back, & Logging operations).
+
+
+Figure 3View largeDownload slidePost Operations Composite Depth Time CurveFigure 3View largeDownload slidePost Operations Composite Depth Time Curve Close modal
+
+
+Rushmore reviews, an external database designed to gather operations data from all operating companies was used to perform performance benchmarking on the "Horizon" wells drilled. Both wells drilled on this development project were benchmarked against eight similar wells drilled by other Operators in Nigeria. Two performance metrices (ft/day and $/ft -see charts below) were used for this benchmarking exercise. Results from this review showed that the Horizon Wells are within a P10.
+
+
+Figure 4View largeDownload slideWell Performance Comparison plotFigure 4View largeDownload slideWell Performance Comparison plot Close modal
+
+
+Figure 5View largeDownload slideWell Performance Comparison plotFigure 5View largeDownload slideWell Performance Comparison plot Close modal
+
+
+Conclusion
+
+
+The re-engineering of the wells, dedication of the team to the project, incentive strategy and optimization processes put in place helped in the delivery of two (2) safe, reliable, and affordable well for the Nigerian Marginal Operator.These wells were successfully drilled and completed, and are currently producing.An average of twenty-three (23) days and six Million dollars ($6MM) was spent per well.The "Horizon" project can be termed a very successful operation due to the following highlights; Horizontal drains were effectively placed in the target sands meeting all reservoir and production engineering objectives, with a Productivity Index (PI) of circa 2000bbls/day/psi.All HSE KPI’s were met (FAT, LTI, RWDC).Work scope was delivered without any community issues.The project achieved an average NPT of 10%.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+OkoliUgo', NumbereOtokini, NtiwunkaGreg, EmmanuelIfediora, ChikeNwagu, PrinceAigbedion, EleanorOkubor, HassannahSalami (2021): Delivering Best in Class Wells: A Case Study (SPE-203705-MS).Google Scholar OkoliUgo', Numbere, O., Aigbedion, P. and others (2019): "Reducing Drilling Operations Cost Through Rental Inventory Management - A Case Study (SPE Paper 198804), SPENigeria Annual International Conference and Exhibition, 5th -7th August, Lagos, Nigeria.Google Scholar OkoliUgo' (2013): Drilling cost effective Wells by incorporating lessons learnt into drilling operations; OML 126 Wells (Phase 1 FDP) (SPE 167598), SPE Nigeria Annual International Conference and Exhibition, August 2013, Lagos, Nigeria.Google Scholar OkoliUgo', NumbereOtokini (2014): The Importance of Geomechanical Analysis for Well Design and Engineering, SPE Nigeria Annual International Conference and Exhibition, August 2013, Lagos, Nigeria.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212041-MS
+
+
+

files/2022/Cuttings Lifting Coefficient Model A Criteria for Cuttings Lifting and Hole Cleaning Quality of Mud in Drilling Optimization.txt

@@ -0,0 +1,516 @@
+----- METADATA START -----
+Title: Cuttings Lifting Coefficient Model: A Criteria for Cuttings Lifting and Hole Cleaning Quality of Mud in Drilling Optimization
+Authors: Dorcas Jimmy, Emenike Wami, Michael Ifeanyi Ogba
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212004-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+In this study, the hole cleaning qualities of mud samples formulated with tigernut derivatives – starch and fibre – as additives were determined by adding drill cuttings as impurities and evaluating the Carrying Capacity Index (CCI) as well as Transport Index (TI) of the muds. Results of the analysis conducted for the mud properties showed that all the different mud properties but the pH of the mud evaluated of mud samples B, C1, C2, and C3 were slightly higher (albeit within the recommended values) than those of the control (standard) mud sample A. Using the results obtained from mud properties analysis and drilling operations data for the evaluation of the hole cleaning qualities, the following new expressions for optimum cuttings lifting ability (β) and cuttings lifting coefficient (β1), which gives criteria for cutting lifting in a wellbore were developed: β1 = 0.11519 [(1 − Cf)]−1(dp)−2.014. The higher the value of β1 greater than one, the better the hole cleaning ability of the mud and the lower the mud flowrate needed to achieve better hole cleaning for a given cutting particle size.
+
+
+
+
+Keywords:
+equation,
+cuttings transport,
+expression,
+drilling fluids and materials,
+lifting coefficient,
+lifting,
+petrowiki,
+drilling fluid management & disposal,
+concentration,
+fraction
+
+
+Subjects: 
+Drilling Fluids and Materials,
+Drilling fluid management & disposal,
+Cuttings transport
+
+
+
+
+Introduction
+
+
+The key to a successful hole cleaning relies upon integrating optimum drilling fluid properties with best drilling practices. Good solids control begins with good hole cleaning and there are several factors that affect it. One of the primary functions of the drilling fluid is to bring drilled cuttings to the surface in a state that enables the drilling-fluid processing equipment to remove them with ease (Amoco, 2016; Baroid, 1998). To achieve this end, quick and efficient removal of cuttings is essential. In aqueous-based fluids, when drilled solids become too small to be removed by the solids-control equipment, they are re-circulated down-hole and dispersed further by a combination of high-pressure shear from the mud pumps, passing through the bit, and the additional exposure to the drilling fluid. The particles become so small that they must be removed via the centrifuge overflow (which discards mud too) and/or a combination of dilution and chemical treatment. Thus, to minimize mud losses, drilled solids must be removed as early as possible (ASME, 2005).
+
+
+In rotary drilling operations, both the fluid and the rock fragments are moving in the annulus. The situation is complicated by the fact that the fluid velocity varies from zero at the wall to a maximum at a point between the pipe outer wall and the wellbore wall. In addition, the rotation of the drill-pipe impacts centrifugal force on the rock fragments, which affects their relative location in the annulus. As a result of the extreme complexity of this flow behavior, drilling personnel have relied primarily on observation and experience for determining the lifting ability of the drilling fluids. In practice, either the flow rate or effective viscosity of the fluid is increased, if problems related to inefficient cuttings removal are encountered. The result is a natural tendency towards thick muds and high annular velocities (Abdul et al., 2002).
+
+
+However, increasing the mud viscosity or flow rate can be detrimental to the cleaning action beneath the bit, and cause a reduction in the penetration rate. Also there is a considerable economic penalty associated with the use of a higher flow rate or mud viscosity than necessary. Increasing the mud viscosity will not necessarily improve the cuttings transport efficiency in directional and horizontal sections as well. Transport is usually not a problem, if the well is near vertical. However, considerable difficulties can occur when the well is being drilled directionally, because cuttings may accumulate either as a stationary bed at hole angles above about 50° or in a moving, churning bed at lower hole angles. Drilling problems that may result are various and the severities are high (Petrowiki, 2018).
+
+
+One of the criteria for assessing the performance of drilling fluid is its hole cleaning ability, especially in the transportation of drill cuttings from down hole to the surface. This criterion is even much more difficult for highly deviated wells. The ease with which this can be done depends on certain properties the mud must possess. There are several critical elements that affect cutting lifting such as hole angle of the interval, the rheology of the drilling fluid to be used for the drilling operation, the cuttings size, shape, density, integrity, and sphericity, the rate of penetration, the drill string rotational rate, the drill string eccentricity for laminar flow and turbulent flow, the drilling fluid density, the flow rate or annular velocity (Ali et al., 2012; Unegbu, 2010; and AMSE, 2005).
+
+
+Generally, in near vertical and moderately inclined hole intervals, annular velocity (AV) has the largest impact upon whether a hole can be cleaned of cuttings (Gavignet and Sobey, 1989). The problem of cuttings transport in vertical wells has been studied for many years, with the earliest analysis of the problem being that of Pigott (1941).
+
+
+Since the early 1980s, cuttings transport studies have focused on inclined wellbores using different mechanisms, which dominate within different ranges of wellbore taking angleinto consideration (Petrowiki, 2018);
+
+
+Cuttings bed heights and annular cuttings concentrations as functions of operating parameters (flow rate and penetration rate)Wellbore configuration (depth, hole angle, hole size or casing/wellbore inside diameter (ID), and pipe size)Fluid properties (density and rheological properties)Cuttings characteristics (density, size, shape, bed porosity, and angle of repose)Pipe eccentricity and rotary speed.
+
+
+Laboratory experience indicates that the flow rate, if high enough, will remove the cuttings for any fluid, hole size, and hole angle. Unfortunately, flow rates high enough to transport cuttings up and out of the annulus effectively cannot be used in many wells, because of limited pump capacity and/or high surface or down-hole dynamic pressures. This is particularly true for high angles with hole sizes larger than 12¼ in.
+
+
+Some models for cutting transport employed either experimental, mechanistic, and or field application processes (Ford et al, 1990; Guild et al, 1995; Uduak & Pal, 2012) which includes Carrying Capacity Index (CCI=k×Av×ρm400000)⁠, Transport Ratio (TR=VfVAorTR=(99.50.01778Pr)Dhole2Qm(26.149+742.7Pr))⁠, Transport Index (TI=QdρRf834.5)⁠, Rheological Factor (RF=6K3585Aa[TICCI])⁠, and Angle Factor (Af=1AfQvertical=Qdeviated) (ASME, 2005; Robinson, 2000; Belavadi, 1994; Unegbu, 2010; and Lou et al, 1992 & 1994) but in literature there are no direct readily available expressions that easily correlates the optimum cuttings lifting criterion, which relates the ratio of mud annular velocity to cuttings slip velocity and cuttings fraction, without having to go through the cumbersome and rigorous calculations and chats and starting from first principle, etc.
+
+
+Averting of this bottleneck is highly needed as it will considerably facilitate the ease of rating drilling muds in terms of their hole cleaning performance even before going into the drilling operations. This research is aimed at developing a model equation for calculating mud carrying ability and other hole cleaning parameters that will reduce the cumbersomeness and rigors in the applications of the existing methods of calculations of drilling cuttings transport to the surface in both vertical, deviated and highly deviated (horizontal) wells.
+
+
+Methodology
+
+
+Materials
+
+
+The materials employed in this study are data from a standard drilling fluid physiochemical properties experimented and analyzed, Excel software for calculations and equation validation, flow s
+
+
+Methods
+
+
+This study employed a mixed research method of Quantitative research approach which employed secondary data collection method; and Analytical research approach which employed Numerical research method for the formulation and validation of the model equation for cuttings lifting coefficient and cutting lifting criterium.
+
+
+Quantitative Approach / Analysis
+
+
+The secondary data used in this study are presented in  Appendix A, Table 1 and 2 below.
+
+
+Numerical Approach
+
+
+Formulation of Model Equations for Cuttings Transport
+
+
+There are several forces acting on the particles in a flowing mud system. These forces include both natural forces (such as gravitational forces, etc.) and artificial forces (forces from the object or phase of flow). Other forces are forces based on direction of flow such as drag coefficient versus the particle Reynolds number, the particle settling in fluid, lift forces, etc (Ezekiel, 2012).
+
+
+Mixture Velocity
+
+
+A mixture of mud and drill cuttings flowing through a wellbore annulus, has a velocity given (Petrowiki, 2018) as:
+
+
+Vmix=Qc+QmA(1)
+
+
+Where Qm is the volumetric flow rate of the mud and Qc is the volumetric flow rate of the cuttings which depends on the bit size and the penetration rate. In addition, the mixture velocity can be calculated from the average plug and annulus velocities in terms of the equivalent pipe.
+
+
+Cutting Concentration
+
+
+The cutting concentration is defined (Petrowiki, 2018) as;
+
+
+Cf=QcQc+Qm(2)
+
+
+The average concentration, c, of cuttings accounts for the cuttings concentrations in the plug and annular regions. The cuttings concentrations in the plug and annular regions are assumed equal if the suspended cuttings are uniformly distributed across the area open to flow. Obviously, this assumption has a major impact, and the actual distribution is probably a function of wellbore geometry, mud properties, cuttings properties, and operating conditions. Thus, we obtain;
+
+
+Vmix=CVs(1−c)c−co(3)
+
+
+Where; Vs represents the average settling velocity in the axial direction.
+
+
+The value calculated using equation (3) is the minimum acceptable mixture velocity required for a cuttings concentration. Pigott (1941), recommended that the concentration of suspended cuttings be a value less than 5%. With this limit (c = 0.05), equation (3) becomes;
+
+
+Vmix=0.0475vs0.05−Co(4)
+
+
+Where; Co< 0.05. This implies that the penetration rate must be limited to a rate that satisfies this equality. For near-vertical cases, the critical mud-cuttings mixture velocity equals the value of Equation (4). If the circulation rate exceeds this value, the suspended cuttings concentration will remain less than 5%. However, if the mud circulation velocity is less than the cuttings’ settling velocity, the cuttings will eventually build up in the wellbore and plug it.
+
+
+From equation (2), Qc can be expressed in terms of Qm and cutting concentration, Cf, to give:
+
+
+Qc=(Cf1−Cf)Qm(5)
+
+
+Cutting Transport Criterion
+
+
+Cutting annular velocity, Vc is defined (Majeed et al, 2014) as
+
+
+Vc=Qc/Acf≈(Cf1−Cf)QmAcf(6)
+
+
+For cutting lifting, Vc must be greater than the cutting setting velocityVs, with a positive net upward velocity equal to (Vc - Vs).
+
+
+Therefore, condition for cutting lifting is that the ratio of the net upward velocity to slip velocity should be positive, i.e.
+
+
+Vc−VsVs≥0orVcVs−1≥0(7)
+
+
+βOrβ≥1(8)
+
+
+where ββ=VcVs is the velocity ratio of the cutting annular velocity to its slip velocity, which must be greater than 1 for cutting transport to be possible. β represents the condition for cutting lifting. Hence combining equations (6 and 8), we have
+
+
+β(11−Cf)×[QmAa×1Vs]=β(9)
+
+
+The value [QmA×1Vs]⁠, represents the ratio of the mud annular velocity to cutting slip velocity.
+
+
+Hence for cuttings lifting, we require;
+
+
+𝛃β≥Qm/Aa[(1−cf)Vs]−1(10)
+
+
+Where β is much greater than 1.The higher the β value, the better the particles lifting.
+
+
+For laminar flow conditions
+
+
+The cutting setting velocity Vs is given as [Petrowiki, 2018]:
+
+
+Vs=ds2g(ρs−ρf)18μ(11)
+
+
+For turbulent flow
+
+
+Equations (9) to (10) will be applicable here using the appropriate expression for particle slip velocity as (Petrowiki, 2018):
+
+
+Vsl=233gds(ρs−ρf)fρf(12)
+
+
+Where f = 24/Rep, is the frictional factor, which is a friction of the particle Reynolds number, and shape of the particle given by sphericity.
+
+
+Unfortunately, the volume of fluid required to reach critical velocity for turbulent flow is frequently outside the achievable flow rate for hole sizes larger than 8inch and is frequently limited by maximum allowable ECD and/or hole erosion concerns (Petrowiki, 2018), hence equation in this study will be confined to those for laminar flow conditions.
+
+
+The numerical value for β in equation (10) can be obtained solving equation below;
+
+
+β={QmAa}(11−Cf)X18μds2g(ρs−ρf)(13)
+
+
+Where;
+
+
+ρs = Density of solids (cuttings)
+
+
+ρf = Density of the Drilling Fluid without Drill Cuttings.
+
+
+If ρm is the density of the mud medium containing drill cuttings and is given as;
+
+
+ρm=Cfρs+(1−Cf)ρf(14a)
+
+
+When there are no drill cuttings, Cf = 0, ρm = ρf and for all cuttings only, Cf = 1, ρm = ρs.
+
+
+Thus for a mud of known rheological properties, by fixing Qm – mud flow rate for given annular hole area (Aa), we obtain β-value knowing the particle size and cutting fraction in the equation (11) for laminar flow for a vertical hole. In case of deviated well, we substitute Qm = AfQdev, where Af = angle factor.
+
+
+The optimum value of β can be computed for a given formulated mud at various annular flow velocities for both vertical and deviated wells using equation (13)
+
+
+Cutting Lifting Coefficient and Cutting Lifting Criteria
+
+
+A generalized expression for cutting lifting criterion can be developed for cuttings of different particle sizes, dp, which relates β to cutting concentrations, Cf, and the rheological properties of the mud in the form of a polynomial equation;
+
+
+𝛃𝛃𝛗β'=β/QmAa=φ[(1-Cf)]y(dp)z(14b)
+
+
+The numerical values of the constants, φ, y and z are evaluated graphically from various plots of β’ against Cf and dp. Positive values of β represent cutting lifting. β’ represents cutting lifting velocity ratio per unit annular flow velocity of the mud, which otherwise could be termed cutting lifting coefficient.
+
+
+When the cutting lifting coefficient is multiplied by the annular mud velocity, we can obtain the mud’s ability to lift cuttings. Experimental data were used for plot of β vs cuttings size (figure 1 below) at different mud flowrates.
+
+
+Figure 1View largeDownload slideGraph of Cuttings Lifting Coeffient Versus Particle Size (mm)Figure 1View largeDownload slideGraph of Cuttings Lifting Coeffient Versus Particle Size (mm) Close modal
+
+
+For Hole diameter DHole = 6.3inches and DPipe = 4inches
+
+
+Evaluating β1 using Tables 1 and 2 in  Appendix A, for a given particle size for a drilling mud of known density and viscosity at different cutting concentrations, gave a power expression of the form;
+
+
+β1=φ(1−Cf)yidsiz(15)
+
+
+Where φi is a constant at cutting fraction, Cfi for cutting particle size, dsi.
+
+
+Relating the values of φi for different cutting fractions, Cf, will yield an expression in form of equations (15) for different particle sizes give;
+
+
+β1=φ(1−Cf)y(ds)z(16)
+
+
+Equation (16) gives the generalized expression for cuttings lifting coefficient. Equation (16) was validated using experimental data for nine (9) mud samples with different cutting sizes and cutting fractions. The absolute deviation error, % of the experimental to the model were also obtained.
+
+
+Results and Discussion
+
+
+Boundary Cuttings Lifting Criteria (β) and Lifting Coefficient, (β1)
+
+
+The boundary cutting lifting condition, β, and lifting coefficient (β1), which is β per unit mud annular velocity, expressed as a function of cutting size and fraction are illustrated in Tables 1 in  Appendix A. The lifting coefficient, (β1), represents the Lifting Condition per Unit Annular Velocity (equation 16) while the condition for cuttings lifting, or lifting criteria is;
+
+
+β=φQmAa(1−Cf)y(ds)z(17)
+
+
+Calculations for cutting diameters of 0.5mm - 5mm with of cutting fraction, Cf, in the range of 0.005 - 0.050 gave expressions in terms of Cf which when normalized for particle sizes in figure 1 below, yielded the numerical values for the constants in equation (14a) as: φ = 0.11519; y = -1 and z = -2.014 for ds = dp (0.5mm – 5mm), thus giving equation (18) below as the lifting coefficient;
+
+
+β1=0.11519(1−Cf)−1(ds)−2.014(18)
+
+
+and
+
+
+Condition for cutting lifting, β=QmAa(β1)
+
+
+β1=0.11519QmAa(1−Cf)−1(ds)−(−2.014)(19)
+
+
+Where;
+
+
+β1 = Optimum Lifting Coefficient, β = Cutting Lifting Criteria, φ = Constant, Cf = Cutting Fraction y = -1, ds = Particle size (0.5mm – 5mm), z = -2.014, Qm = Mud Flowrate, Aa = Annular Area
+
+
+If the lifting coefficient is multiplied by the mud annular velocity (QmAa)⁠, then cutting lifting ability of a particular type of mud at different annular velocities can be easily calculated.
+
+
+Equation (18) represents the boundary condition for cutting lifting. Equation (19) shows that the ability of drilling mud to lift cuttings is directly proportional to the mud flow rate and inversely proportional to the square of the cutting particles size.
+
+
+Validation of Model Equation (18)
+
+
+The generalized model equation for cutting lifting coefficient β’=0.11519(1 − Cf)−1(ds)−2.014 was tested using experimental data from nine mud samples with particle sizes of 150µm, 3.35mm and 5.0mm at cutting fraction of 0.005 and prediction error evaluated as shown below (Table 3).
+
+
+Table 3Results for Experimental, Model and Error Values Obtained Using the Generalized Model Equation for Cutting Lifting Coefficient (β1) 
+            . A+P1
+            . A+P2
+            . A+P3
+            . B+P1
+            . B+P2
+            . B+P3
+            . C+P1
+            . C+P2
+            . C+P3
+            . Exp. 5.0890 0.0103 0.0046 5.1271 0.0113 0.0047 6.0573 0.0092 0.0042 Model 5.2837 0.0101 0.0042 5.2837 0.0102 0.0042 5.2837 0.0102 0.0043 Error (%) -3.8 1.3 9.0 -3.05 10 9.01 12.77 10.107 -1.635 
+            . A+P1
+            . A+P2
+            . A+P3
+            . B+P1
+            . B+P2
+            . B+P3
+            . C+P1
+            . C+P2
+            . C+P3
+            . Exp. 5.0890 0.0103 0.0046 5.1271 0.0113 0.0047 6.0573 0.0092 0.0042 Model 5.2837 0.0101 0.0042 5.2837 0.0102 0.0042 5.2837 0.0102 0.0043 Error (%) -3.8 1.3 9.0 -3.05 10 9.01 12.77 10.107 -1.635 View Large
+
+
+The result shows that the equation predicted cutting lifting coefficients of the various mud samples with absolute errors between 1.3% to 3.8% while the remaining samples had error values of 9 – 12%. This shows that the model is particularly suitable for estimating cutting lifting for smaller sized cuttings.
+
+
+Calculations of cutting lifting ability at mud flow rates between 140gpm and 2100gpm, illustrated in Figure 2, using Table 2 and 4 in  Appendix A, showed that good cuttings carrying was obtained for particles sizes less than 1mm at mud flow rates above 500gpm.
+
+
+Figure 2View largeDownload slidePlot of Cutting Lifting Criteria of Different Particle Sizes (mm) at Various Mud Flowrates for Cf = 0.5%Figure 2View largeDownload slidePlot of Cutting Lifting Criteria of Different Particle Sizes (mm) at Various Mud Flowrates for Cf = 0.5% Close modal
+
+
+Conclusions and Contributions to Knowledge
+
+
+From the research work and study carried out in this project, the aim and objectives of this research which is to develop a model equation for calculating mud carrying ability and other hole cleaning parameters that reduces the cumbersome and rigorous applications in the method of calculations of drilling cutting transport to the surface in both vertical, deviated and highly deviated (horizontal) wells, using quantitative and numerical research approach, employing secondary data collection method, already existing cutting transport models and principles.
+
+
+Conclusively, a New correlation expressions have been formulated and validated numerically using standard and field drilling operation data results to give expressions for Cuttings lifting Criteria (β) and Cutting Lifting Coefficient (β1) – which gives the criteria for cutting lifting in wellbore, the higher the value of β1 greater than 1, the better the hole cleaning ability of the mud, as well as the lower the mud flowrate needed to achieve hole cleaning for a given cutting particle size.
+
+
+Also this research work has contributed to knowledge in that from the equations derived in this work, the drilling Engineer can estimate using β and or β1 (equation 18 and or 19), the cutting lifting ability of any drilling mud of known properties if the cutting size and the cutting fraction in the mud are known / estimated based on the drill bit blades.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+PARAMETERS, DATA AND RESULTS USED FOR SOLVING BOUNDARY CUTTINGS LIFTING CONDITIONS (CRITERIA) (β) AND OPTIMUM LIFTING COEFFICIENT (β1)
+
+
+Table 1Values of Parameters for Lifting Condition (β) and Lifting Coefficient (β1) Cf
+            . 1 – Cf
+            . d = 0.5mm
+            . d = 1.0mm
+            . d = 1.5mm
+            . d = 2.0mm
+            . d = 2.5mm
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . 0.0 1 2.194 0.456 8.777 0.1139 19.749 0.0507 35.109 0.0285 54.859 0.0182 0.005 0.995 2.183 0.460 8.733 0.1151 19.650 0.0512 34.934 0.0288 54.584 0.0184 0.010 0.990 2.172 0.465 8.690 0.1162 19.550 0.0517 34.758 0.0291 54.310 0.0186 0.015 0.985 2.161 0.470 8.646 0.1174 19.450 0.0522 34.583 0.0294 54.035 0.0188 0.020 0.980 2.150 0.475 8.602 0.1186 19.350 0.0527 34.407 0.0297 53.761 0.0190 0.025 0.975 2.139 0.480 8.557 0.199 19.255 0.0533 34.231 0.0300 53.484 0.0192 0.030 0.970 2.129 0.484 8.514 0.1211 19.157 0.0538 34.056 0.0302 53.213 0.0194 0.035 0.965 2.117 0.490 8.470 0.1223 19.058 0.0544 33.881 0.0306 52.938 0.0196 0.040 0.960 2.106 0.494 8.426 0.1236 18.956 0.0550 33.704 0.0309 52.664 0.0198 0.045 0.955 2.096 0.500 8.382 0.1249 18.860 0.0555 33.529 0.0312 52.390 0.0200 0.050 0.950 2.085 0.504 8.338 0.1263 18.761 0.0561 33.354 0.0316 52.116 0.0201 Cf
+            . 1 – Cf
+            . d = 0.5mm
+            . d = 1.0mm
+            . d = 1.5mm
+            . d = 2.0mm
+            . d = 2.5mm
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1–Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . 0.0 1 2.194 0.456 8.777 0.1139 19.749 0.0507 35.109 0.0285 54.859 0.0182 0.005 0.995 2.183 0.460 8.733 0.1151 19.650 0.0512 34.934 0.0288 54.584 0.0184 0.010 0.990 2.172 0.465 8.690 0.1162 19.550 0.0517 34.758 0.0291 54.310 0.0186 0.015 0.985 2.161 0.470 8.646 0.1174 19.450 0.0522 34.583 0.0294 54.035 0.0188 0.020 0.980 2.150 0.475 8.602 0.1186 19.350 0.0527 34.407 0.0297 53.761 0.0190 0.025 0.975 2.139 0.480 8.557 0.199 19.255 0.0533 34.231 0.0300 53.484 0.0192 0.030 0.970 2.129 0.484 8.514 0.1211 19.157 0.0538 34.056 0.0302 53.213 0.0194 0.035 0.965 2.117 0.490 8.470 0.1223 19.058 0.0544 33.881 0.0306 52.938 0.0196 0.040 0.960 2.106 0.494 8.426 0.1236 18.956 0.0550 33.704 0.0309 52.664 0.0198 0.045 0.955 2.096 0.500 8.382 0.1249 18.860 0.0555 33.529 0.0312 52.390 0.0200 0.050 0.950 2.085 0.504 8.338 0.1263 18.761 0.0561 33.354 0.0316 52.116 0.0201 Cf
+            . 1–Cf
+            . d = 3.0mm
+            . d = 3.5mm
+            . d = 4.0mm
+            . d = 4.5mm
+            . d = 5.0mm
+            . 
+            . 
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . 1 1 78.996 0.0127 107.52 0.0093 140.44 0.0071 177.74 0.0056 219.43 0.0046 0.995 0.995 78.601 0.0128 106.98 0.0094 139.74 0.0072 176.85 0.0057 218.34 0.0046 0.990 0.990 78.206 0.0129 106.45 0.0095 139.03 0.0073 175.96 0.0057 217.24 0.0047 0.985 0.985 77.811 0.0130 105.91 0.0096 138.33 0.0073 175.08 0.0058 216.14 0.0047 0.980 0.980 77.416 0.0132 105.37 0.0097 137.63 0.0075 174.19 0.0059 215.05 0.0048 0.975 0.975 77.021 0.0133 104.83 0.0098 136.93 0.0075 173.30 0.0059 213.95 0.0048 0.970 0.970 76.626 0.0135 104.30 0.0099 136.23 0.0076 172.41 0.0060 212.85 0.0048 0.965 0.965 76.231 0.0136 103.76 0.0100 135.52 0.0077 171.52 0.0060 211.76 0.0049 0.960 0.960 75.836 0.0137 103.22 0.0101 134.82 0.0077 170.63 0.0061 210.66 0.0050 0.955 0.955 75.442 0.0138 102.68 0.0102 134.12 0.0078 169.74 0.0062 209.56 0.0050 0.950 0.950 75.046 0.0140 102.14 0.0103 133.42 0.0079 168.85 0.0062 208.46 0.0051 Cf
+            . 1–Cf
+            . d = 3.0mm
+            . d = 3.5mm
+            . d = 4.0mm
+            . d = 4.5mm
+            . d = 5.0mm
+            . 
+            . 
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . VS
+            . β1(1-Cf)VS
+            . 1 1 78.996 0.0127 107.52 0.0093 140.44 0.0071 177.74 0.0056 219.43 0.0046 0.995 0.995 78.601 0.0128 106.98 0.0094 139.74 0.0072 176.85 0.0057 218.34 0.0046 0.990 0.990 78.206 0.0129 106.45 0.0095 139.03 0.0073 175.96 0.0057 217.24 0.0047 0.985 0.985 77.811 0.0130 105.91 0.0096 138.33 0.0073 175.08 0.0058 216.14 0.0047 0.980 0.980 77.416 0.0132 105.37 0.0097 137.63 0.0075 174.19 0.0059 215.05 0.0048 0.975 0.975 77.021 0.0133 104.83 0.0098 136.93 0.0075 173.30 0.0059 213.95 0.0048 0.970 0.970 76.626 0.0135 104.30 0.0099 136.23 0.0076 172.41 0.0060 212.85 0.0048 0.965 0.965 76.231 0.0136 103.76 0.0100 135.52 0.0077 171.52 0.0060 211.76 0.0049 0.960 0.960 75.836 0.0137 103.22 0.0101 134.82 0.0077 170.63 0.0061 210.66 0.0050 0.955 0.955 75.442 0.0138 102.68 0.0102 134.12 0.0078 169.74 0.0062 209.56 0.0050 0.950 0.950 75.046 0.0140 102.14 0.0103 133.42 0.0079 168.85 0.0062 208.46 0.0051 View Large
+
+
+Table 2Cutting Lifting Criteria at Different Mud Annular Velocities @ Cf = 0.5% Qm
+            . Av
+            . Dp @ 0.5
+            . Dp @ 1
+            . Dp @ 1.5
+            . Dp @ 2
+            . Dp @2.5
+            . Dp @3
+            . Dp @3.5
+            . Dp @4
+            . Dp @4.5
+            . Dp @5
+            . Flowrate 140gpm 0.734 0.3387 1.2098 2.1777 5.08139 0.01355 0.009407 0.00691 0.00529 0.00418 0.0039 Flowrate 500gpm 2.628 0.08467 0.30241 0.5444 1.2702 0.04839 0.033601 0.02469 0.01890 0.01487 0.0121 Flowrate 900gpm 4.730 0.03763 0.1344 0.24193 0.56451 0.08709 0.06050 0.04444 0.03402 0.02688 0.0218 Flowrate 2100gpm 11.04 0.02117 0.0756 0.138608 0.31753 0.2032 0.141125 0.10368 0.07938 0.06272 0.0508 Qm
+            . Av
+            . Dp @ 0.5
+            . Dp @ 1
+            . Dp @ 1.5
+            . Dp @ 2
+            . Dp @2.5
+            . Dp @3
+            . Dp @3.5
+            . Dp @4
+            . Dp @4.5
+            . Dp @5
+            . Flowrate 140gpm 0.734 0.3387 1.2098 2.1777 5.08139 0.01355 0.009407 0.00691 0.00529 0.00418 0.0039 Flowrate 500gpm 2.628 0.08467 0.30241 0.5444 1.2702 0.04839 0.033601 0.02469 0.01890 0.01487 0.0121 Flowrate 900gpm 4.730 0.03763 0.1344 0.24193 0.56451 0.08709 0.06050 0.04444 0.03402 0.02688 0.0218 Flowrate 2100gpm 11.04 0.02117 0.0756 0.138608 0.31753 0.2032 0.141125 0.10368 0.07938 0.06272 0.0508 View Large
+
+
+Table 4.19Mud Density Used for Calculating Cutting Lifting Criteria at Different Mud Annular Velocities Cf
+            . 1 - Cf
+            . ρm
+            . β@ 0.5
+            . β@ 1.0
+            . β@ 1.5
+            . β@ 2.0
+            . β@ 2.5
+            . β@ 3.0
+            . β@ 3.5
+            . β@ 4.0
+            . β@ 4.5
+            . β@ 5.0
+            . 0.0 1 8.96 0 0 0 0 0 0 0 0 0 0 0.005 0.995 9.027 0.0023 0.0006 0.0003 0.0001 0.0009 0.0001 0.0001 0.0000 0.0000 0.0000 0.010 0.990 9.095 0.0047 0.0012 0.0005 0.0003 0.0002 0.0001 0.0001 0.0001 0.0001 0.0001 0.015 0.985 9.162 0.0071 0.0018 0.0008 0.0004 0.0003 0.0002 0.0001 0.0001 0.0001 0.0001 0.020 0.980 9.229 0.0095 0.0024 0.0011 0.0006 0.0004 0.0003 0.0002 0.0002 0.0001 0.0001 0.025 0.975 9.296 0.0120 0.0030 0.0013 0.0008 0.0005 0.0003 0.0003 0.0002 0.0002 0.0001 0.030 0.970 9.364 0.0145 0.0036 0.0016 0.0010 0.0006 0.0004 0.0003 0.0002 0.0002 0.0002 0.035 0.965 9.431 0.0171 0.0043 0.0019 0.0011 0.0007 0.0005 0.0003 0.0003 0.0002 0.0002 0.040 0.960 9.498 0.0182 0.0046 0.0020 0.0012 0.0007 0.0005 0.0004 0.0003 0.0002 0.0002 0.045 0.955 9.565 0.0205 0.0051 0.0023 0.0013 0.0008 0.0006 0.0004 0.0003 0.0003 0.0002 0.050 0.950 9.633 0.0228 0.0057 0.0025 0.0014 0.0009 0.0006 0.0005 0.0004 0.0003 0.0002 Cf
+            . 1 - Cf
+            . ρm
+            . β@ 0.5
+            . β@ 1.0
+            . β@ 1.5
+            . β@ 2.0
+            . β@ 2.5
+            . β@ 3.0
+            . β@ 3.5
+            . β@ 4.0
+            . β@ 4.5
+            . β@ 5.0
+            . 0.0 1 8.96 0 0 0 0 0 0 0 0 0 0 0.005 0.995 9.027 0.0023 0.0006 0.0003 0.0001 0.0009 0.0001 0.0001 0.0000 0.0000 0.0000 0.010 0.990 9.095 0.0047 0.0012 0.0005 0.0003 0.0002 0.0001 0.0001 0.0001 0.0001 0.0001 0.015 0.985 9.162 0.0071 0.0018 0.0008 0.0004 0.0003 0.0002 0.0001 0.0001 0.0001 0.0001 0.020 0.980 9.229 0.0095 0.0024 0.0011 0.0006 0.0004 0.0003 0.0002 0.0002 0.0001 0.0001 0.025 0.975 9.296 0.0120 0.0030 0.0013 0.0008 0.0005 0.0003 0.0003 0.0002 0.0002 0.0001 0.030 0.970 9.364 0.0145 0.0036 0.0016 0.0010 0.0006 0.0004 0.0003 0.0002 0.0002 0.0002 0.035 0.965 9.431 0.0171 0.0043 0.0019 0.0011 0.0007 0.0005 0.0003 0.0003 0.0002 0.0002 0.040 0.960 9.498 0.0182 0.0046 0.0020 0.0012 0.0007 0.0005 0.0004 0.0003 0.0002 0.0002 0.045 0.955 9.565 0.0205 0.0051 0.0023 0.0013 0.0008 0.0006 0.0004 0.0003 0.0003 0.0002 0.050 0.950 9.633 0.0228 0.0057 0.0025 0.0014 0.0009 0.0006 0.0005 0.0004 0.0003 0.0002 View Large
+
+
+References
+
+
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+
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+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212004-MS
+
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+

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+Title: Decommissioning of Oil and Gas facilities in Nigeria: Challenges and Opportunities
+Authors: Abubakar Raji, Shadrach Ogiriki
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211920-MS
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+Abstract
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+Decommissioning of oil and gas facilities in Nigeria is a relatively new activity. Decommissioning is essentially a set of activities undertaken to manage or dispose aged and worn-out Oil and Gas facilities. The idea of Decommissioning has become paramount in Nigeria due to aging of assets and mature fields operations as they are approaching the end of their economic life. However, presently no facility has ever been decommissioned in Nigeria. Decommissioning is an inherently hazardous exercise that requires meticulous planning, cognate experience, efficient management and other relevant defined skill set for it to be successful. This paper dwells on how to harness the opportunities that come with Decommissioning as well as suggest ways to mitigate some of its challenges.The challenges could be technical, economic, environmental, and legal, but could be further narrowed to include but not limited to poor information management, inadequate regulatory readiness, poor talent management, waste management disposal, lack of previous experience, poor supply chain management, and low portfolio management. While opportunities include securing direct and indirect employment and procurement to home nationals, which in turn could foster the development of local skills, technology transfer, and use of local manpower in capital projects.
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+Keywords:
+offshore facility decommissioning,
+upstream oil & gas,
+well decommissioning,
+social responsibility,
+asset and portfolio management,
+subsea system,
+information management,
+strategic planning and management,
+sustainability,
+nigeria
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+
+Subjects: 
+Offshore Facilities and Subsea Systems,
+Environment,
+Sustainability/Social Responsibility,
+Asset and Portfolio Management,
+Strategic Planning and Management,
+Professionalism, Training, and Education,
+Offshore facility decommissioning,
+Waste management,
+Well Decommissioning,
+Facilities Decommissioning and Site Remediation
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+Introduction
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+Since the discovery of Petroleum in 1956 in Nigeria, Nigeria has been a large producer of Petroleum. Production started onshore before gradually moving offshore. Most matured or aged assets would need to be decommissioned in the next few years. The market for decommissioning is an emerging market which keeps growing. In order for this task to be successful, technological and operational challenges need to be minimized and also take into account economic and environmental considerations (Prasthofer, 1998). According to a recent report released by Wood Mackenzie, between 2018 and 2022 nothing less than $32 billion would be spent on decommissioning around the world, with more to spend in the future. Also, according to United Kingdom Continental Shelf (UKCS) decommissioning market review by Wood Mackenzie in 2018, Nigeria has spent close to $1billion on decommissioning feasibility and environmental impact assessment study.
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+Decommissioning is basically a set of activities to be undertaken to manage or dispose of aged Oil and Gas facilities, then eliminate environmental footprint once a producing field is nearing or reaches the end of its economic life. This includes well abandonment, total or partial removal of disused facilities or leaving the facility in place (Altit and Igiehon, 2007). Decommissioning of offshore installations includes several complex issues on the subject of environment, safety, health, technology, legal, stakeholder, and economics. It also involves carefully drafting and implementation of comprehensive and sustainable solutions that would deal with constant and obvious stakeholders’ concerns. Consequently, the execution and regulation of offshore decommissioning is multifaceted.
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+The defining moment of decommissioning, at least in the North Sea, came as a consequence of the Brent Spar case in 1990s which uncovered an enormous public conflict involving environmental pressure groups like the Greenpeace, general public, shipping interests, the fishing industry, host governments, and last but not the least, Shell (the platform owner). The Greenpeace activists occupied the Brent Spar platform because they were against deep sea disposal of the platform after dismantling. They also wrongly claimed that heavy metals, other chemicals and over 5,500 tonnes of oil were on the Spar, though they later apologized. Shell forcefully evicted these activists after obtaining a legal permission. But the public perception about Shell and brand image was damaged leading to boycott of Shell products across Europe (though other oil production companies supported Shell's position). This event gave evidence that the removal and disposal of decommissioned offshore installations/pipelines should be regulated by international environmental values and standards, and that situation like this confirms that there is always the possibility for social boycott of activities and regulations that are not accepted by the public.
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+The goal of this study was to gain a comprehensive understanding of the Decommissioning practices in Nigeria so far, identifying some of the challenges associated with platform decommissioning/abandonment operation and proffers solutions, and as well as highlight opportunities associated with decommissioning. Interviews with major industry actors, particularly field operators and regulators, were also undertaken. Hence, this paper aims to improve decommissioning debate in Nigeria and review advances in decommissioning of offshore oil and gas production activities in Nigeria.
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+Overview of decommissioning
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+Decommissioning refers to the dismantling, decontamination and removal of process equipment and facility structures. It may be described as the best way to shut down production operation at the end of a field's life. This involves a multidisciplinary process, which requires detailed method that encompass several areas such as environmental, financial, political, health and safety. Well abandonment is part of these activities. Decommissioning has been successfully done in the North Sea and Gulf of Mexico, so the activities involved are well known, from dismantling of structures, plugging of wells, to disposal of waste etc. But these activities are capital and energy intensive, and associated with waste generation as well as concerns for safety of personnel and efficient stakeholder management.
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+Figure 1View largeDownload slideDecommissioning activities taking place.Figure 1View largeDownload slideDecommissioning activities taking place. Close modal
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+The decommissioning of offshore structures is a severing intensive operation. Cutting is often required throughout the structure above and below the water line and mud line. For the cutting technique to be effective, it must be safe, reliable, repeatable, flexible and adaptable under filed conditions, environmentally sensitive and economical (Tularak, Khan and Thungsuntonkhun, 2007).
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+In selecting a decommissioning method for production facility or platform, the following key considerations should be taken into account:
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+The age of the production facility.The location of the production facility and depth of water (for offshore fields)The design and type of platformThe weight of the lifts and Soil strengthEnvironmental conditions such as weather.International and National Laws and Regulations
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+Scale of the challenge
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+According to the Energie Beheer Nederland (EBN) Master plan for decommissioning, the estimated cost of decommissioning platforms often surpasses by 10% while well plugging and abandonment cost surpasses by 50%. In 2014, the cost of decommissioning was estimated to €4.3billion. In the Gulf of Mexico, the Bullwinkle and Pompano platforms are expected to cost an estimated $265million and $203million respectively (Kaiser and Liu, 2014). This shows that decommissioning is very capital-intensive. Decommissioning and abandonment costs have to be considered at the conceptualization stage of field development.
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+The timing of when to undergo decommissioning is a critical factor. Decommissioning starts from cessation of production. Cessation of production could be as a result of two factors – failure in wells that would necessitate a work or future expected cashflow from the field being negative (Kirby, 1999). Companies try to defer decommissioning as much as possible. For oil companies, decommissioning amounts to losing money. When operator cash flow is reduced, there is less money in the budget, so they try to put off decommissioning for as long as possible. They prefer to find reserves and spend on new construction (Ruivo and Morooka, 2001).
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+Figure 2View largeDownload slideOffshore Decommissioning Process (Eke et al., 2021)Figure 2View largeDownload slideOffshore Decommissioning Process (Eke et al., 2021) Close modal
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+Nigeria Oil and Gas Upstream Industry
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+After the discovery of the first commercial oil reserve in Nigeria, further geological and geophysical investigations were conducted and led to discovery of more commercial oil reserves. These subsequent discoveries have impacted positively on the socio-economic development of Nigeria. Hence, the Oil and Gas industry has become the dominant sector influencing the nation's economy because it accounts for about 90 percent of her total revenue generation (Nwaobi, 2005) and over 70 percent of Nigeria's national export earnings (Atakpo and Ayolabi, 2009).
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+Over the past three decades, Nigeria have sometimes attained peak in her exploration and production operations of Oil and Gas both in the upstream sector, producing at 2.4Mbpd at peak production. Nigeria has over 159 oil fields and 1481 wells in operation according to the Nigerian Upstream Petroleum Regulatory Commission.
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+In 2000, there were approximately 7,000 offshore oil and gas platforms and production facilities in operation worldwide with over 500 located in Africa, and Nigeria accounts for most of them (Dempsey, Mathieson and Winters, 2000). These figures must have increased by now due to increased global oil and gas outputs to meet the ever-increasing energy demand.
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+Figure 3View largeDownload slideSome Oilfields in Nigeria (Iheobi et al., 2020)Figure 3View largeDownload slideSome Oilfields in Nigeria (Iheobi et al., 2020) Close modal
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+Major Oil companies in Nigeria are now more inclined to offshore production as a better alternative to onshore production. Offshore production mainly involves producing from water depths exceeding 400 meters or more. Offshore production facilities are less prone to attacks by local militants and vandalism thereby making it attractive to oil producing companies.
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+Challenges of Decommissioning
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+In Nigeria, decommissioning is a relatively new activity. There have been few decommissioning initiatives for our petroleum plants. As more fields fail to generate enough cash to paying operating costs, decommissioning will undoubtedly increase. This shows that increased efficiency and, more critically, industry reform are required. Decommissioning activities, like all other activities in the E&P lifecycle, must follow the defined framework and any regulatory and contractual provisions. Below are some challenges highlighted:
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+An Unprecedented Event
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+The fact that decommissioning has never happened in Nigeria is the biggest challenge so far, and when this is coupled with the high cost and complexity of decommissioning, the need for operators to start planning for it as early as possible becomes imperative as there are no universally applicable decommissioning options for new fields. It is advisable that decommissioning should be factored into the field development (what type of platform would least be hard to dismantle, waste management, efficient energy usage etc.). But for existing aging fields, there are presently no benchmarks for cost, schedule and scope for these types of projects. They can only be extrapolated from those of other regions e.g., the North Sea and Gulf of Mexico where decommissioning has successfully taken place. However, the extrapolated data could be defective and unreliable due to differences in climatic conditions, structural type, water depth, soil type and regulatory frameworks between Nigeria and the other regions.
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+The most important step for a company before starting a decommissioning project is company culture. The company policy must ensure that everyone understands that decommissioning is a process and not just another construction project. Of course, there are small structures that are no more than a project but the large high profile structures will require the full process (Gfiffin and Company, 1998). Because decommissioning is a complex process that necessitates a wide range of knowledge, equipment, and services, which are frequently offered by numerous vendors, costs are likely to rise as the project progresses as seen from previous decommissioning activities worldwide. Decommissioning projects must be designed and managed with tight coordination of the various service providers for safe and cost-effective operations (Price, Ross and Vicknair, 2016).
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+A good starting point for a team is to review industry lessons learned both internally and externally. As an example, review what happened with the Spar case. Are there any similarities? Begin to evaluate how to find the right balance between the environment, cost, technology, safety, regulations and the public interest. This is where a diversified team and a good internal/external network will be helpful.
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+Regulatory readiness
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+The scope of decommissioning planning covers technology, legislation, politics, safety and environment. This means an open discussion is needed between operators and regulators to develop solutions and standards to help in planning and reducing uncertainty (Hustoft and Gamblin, 1995).
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+Regulatory framework is needed to achieve the following;
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+Efficient and prudent use of funds.Incentives and penalties to improve performance.Clearly spelt out role of all stakeholders in the process.
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+There are international, regional and national regulations with regards to decommissioning, examples are the International Maritime Organization (IMO) guidelines, and Oslo/Paris (OSPAR) conventions. Regions and countries also have their regulations for example the Netherlands have EBN guidelines which has a master plan for decommissioning while Malaysia has the Malaysia Master Abandonment Plan Strategy. In the United Kingdom, the Oil and Gas Authority has a decommissioning and repurposing taskforce which has an objective to support the industry to reduce cost and providing direction and oversight to the government. The Oil and Gas Authority has guidance note for decommissioning of the Oil and Gas installation with key details like waste treatment and disposal in environmentally friendly manner and Oil and Gas UK Guidelines for the Suspension and Abandonment of Wells.
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+In Nigeria, the Nigerian Upstream Petroleum Regulatory Commissions (NUPRC) is the main upstream regulator after the assent to the Petroleum Industry Act (PIA) of 2021, but before then, there was Department of Petroleum Resources (DPR) as the upstream regulator. Most DPR regulations do not talk much about decommissioning; DPR has guides for "Construction and Maintenance of Surface Productions" and "Construction and Maintenance of Offshore Structures" but none of them talks about decommissioning except Environmental Guidelines and Standards for the Petroleum Industry in Nigeria (EGASPIN) which is in general terms, it does not talk in detail about decommissioning. The DPR guideline for marginal field only talks about funding of decommissioning activities. The PIA is the main law governing the oil and gas sector in Nigeria, it talks about contributory decommissioning cost but not about abandonment expenditure, and pegged the cost relative to a value, like by certain percent of profit or OPEX, and this could be unrealistic. Also, for fields that have changed hands, what percent would previous owners contribute was not clearly spelt. In December 2019, DPR inaugurated a committee with membership drawn from the private sector, academia and industry professionals to review and develop a framework for decommissioning requirements in Nigeria but up to this moment nothing has been heard from this committee. So NUPRC has a lot to do in terms of developing a framework and it's implementation.
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+There should also be a regulatory framework on how assets are evaluated based on their production potential and net present values in the ever-changing market dynamics which should put the right focus to the right assets to be decommissioned. An asset should be submitted to periodic technical and commercial reviews to relevant stakeholders prior to decommissioning or ceasation of production (CoP), the asset's life could be prolonged, preserved, divested, or decommissioned after these reviews are completed (Mohd Nasahie Akbar Ali, Karim and Rusli, 2020).
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+Cost
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+Decommissioning is a capital intensive project as shown from previous experience with cost overrun frequently happening. According to the Brent Delta Topside Decommissioning Interim Close-Out Report, over 40% of the decommissioning costs on manned installations are associated with the Plugging & Abandonment (P&A) of platform wells, therefore there should new ways of working on driving down costs (Well P&A) and duration (post-CoP Opex). With no prior experience and data in Nigeria like earlier stated, only comparison and extrapolation from other regions from where it has successfully taken place can be used to determine cost of decommissioning, which would be an estimate (it could right or wrong). But also who bears this cost? The host Government or Operator or both, if both what is the sharing formula? When fields have changed hands, what percentage of the decommissionming cost would previous owners contribute? This is not clearly stated in the PIA or any working document. This has to be tackled by the upstream regulator.
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+Talent management
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+As previously stated, Decommissioning has never taken place in Nigeria. Decommissioning involves special skills set and dealings with heavy machinery. Activities such as jet cutting of tubing, casing downhole delivery of cement and handling of explosives are not developed overnight. Licenses are needed to handle explosives (Boschee, 2012). Slick line operators, structural engineers, and project engineers are part of the decommissioning process. These are also skill set that are not classroom based but, on the job based and this require time to be acquired. It takes time to develop the expertise. High level of experience is needed to perform these operations efficiently.
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+Figure 4View largeDownload slideA Cruciform Strengthening Beam being Lifted into Place on decommissioning process (Shell U.K. Limited, 2015)Figure 4View largeDownload slideA Cruciform Strengthening Beam being Lifted into Place on decommissioning process (Shell U.K. Limited, 2015) Close modal
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+Operators have two options when planning to undergo decommissioning – either it is done by the in-house team or outsource the project to another company. Experienced and International companies can have the company team execute the decommissioning activities while others (experienced or inexperienced depending on company policy) can outsource the project and maintaining oversight. With either option, there are shortages of personnel and companies in the decommissioning field in Nigeria. For example, in the United Kingdom, there are decommissioning companies like Aubin group, Rushmore well, and Decomm Engineering. Also in the United Kingdom, educational institutions are thinking towards decommissioning and having research centers on this, but this is not the case in Nigeria. When decommissioning starts in Nigeria in a few years time, it would not be wise to have shortfall in local manpower or experienced personnel. Also operators could be hesitant to commit experienced senior individuals to supervise the decommissioning process when it arises as their abilities could be better used in production-related activities because decommissioning is not a profit making activity.
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+Information management
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+In order to achieve effective well abandonment, well data and downhole conditions are needed. Problems like obstruction in well and/or sustained casing problem could derail the whole project. Without accurate data, well testing must be performed in order to ensure the process comply with safety and environmental regulations. Incomplete or inaccurate data is a major hindrance for proper decommissioning planning. Without good data management, the process becomes cost ineffective. Some companies also do not store their data efficiently, so these data may not be available because of poor record keeping.
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+Also, ownership of some fields has changed overtime in Nigeria and these companies could have different ways of storing and managing information and document control. Historical data of wells could have gone missing or not compatible with the system as ways of storing data in the late 70s, e.g. in floppy disk or on drawings and converting these drawings into as-built drawing comes with its complication.
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+While decommissioning in the South Timbalier area of the Gulf of Mexico, discrepancies were found while measuring pile depth due to rope stretch, resulting in inaccurate measurements before it was rectified (Price, Ross and Vicknair, 2016). This shows data management is very paramount.
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+Waste Management
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+Another huge challenge of decommissioning is in the area of waste generation and management. Wastes include steel, oil, chemicals, production wells, flushing, wellheads etc. One of the primary objectives of decommissioning is protecting the environment and not contributing to pollution of the environment (Shaw, 1994). Therefore, developing a waste management strategy is paramount. Some wastes are reusable while some are not. International regulatory obligations will result in most installations being returned to shore for reuse, recycling or disposal (Tularak, Khan and Thungsuntonkhun, 2007).
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+Figure 5View largeDownload slideBrent Delta Topside Skidded onto Quay 6 ASP Facility Teesside, May 2017 (Shell U.K. Limited, 2015)Figure 5View largeDownload slideBrent Delta Topside Skidded onto Quay 6 ASP Facility Teesside, May 2017 (Shell U.K. Limited, 2015) Close modal
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+Since no decommissioning has taken place in Nigeria, there are no dismantling or scrapping facilities in Nigeria. Though the Niger dock (Snake Island, Lagos) and Dorman have large construction yards in Lagos which could be used for the purpose of dismantling. Solid wastes such as steel would best go for recycling or channel to steel plants which sadly are presently not working optimally in Nigeria. Waste management strategy should be incorporated in the field development such that only reusable and recyclable materials are used for the construction. Build a waste management team to include company personnel, experts, external consultants to plan and oversee this operation and making it compliant with international regulations. Due to the enormous waste generation, keeping track of volume and records must be dealt with in detail. A little spill or accident would result in penalty from regulatory agencies and backlash from local community and stakeholders. The PIA and previous DPR guidelines do not state any specific method of removal or reuse of installations.
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+Stakeholder Management
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+The impact of an oil or gas operation on the communities where it operates lasts long after the assets have been decommissioned. Depending on the nature of the operations, particularly the vicinity of sensitive receptors, the level of influence and/or impact will vary (e.g. communities, commercial and recreational fisheries). Stakeholders are frequently unable to adjust to the changes brought about by decommissioning as a result unrealistic expectations and grievances from stakeholders emerge. This has been found to have an impact on an operator's social license to operate, which has recently been exacerbated by the transparency provided by platforms such as social media. The need to establish and maintain a social license during the decommissioning process is becoming apparent (and beyond). This will necessitate careful planning and wide consultation with all parties, including affected communities and regulators (Genter, 2020)
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+Opportunities of Decommissioning
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+Employment
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+As previously discussed, there would be a lot of decommissioning activities happening in the near future also Monitoring and Maintenance post-decommissioning. When that time comes, it would not be wise to not have local expertise to undertake those projects because if there's no available and capable local manpower, expatriate would be brought to do the job and use locals for cheap and unskilled labour. Nigeria's Local content development agenda in the Oil and Gas sector has the core goal of securing direct and indirect opportunities for employment to citizens, and at the same time fostering the acquisition of local skills, technology transfer, and use of local manpower and manufacturing in capital projects. Decommissioning projects can help achieve this goal by providing employment opportunities for locals thereby boosting the local economy. These projects will ensure safety of the environment and human health if efficiently managed and properly handled in strict compliance to relevant regulations. Local companies that are able to demonstrate competent and relevant construction or deconstruction skills, as well as maintenance or fabrication experience, could be engaged to undertake the projects. This will further enhance their skills and acquire on-the-job experience in the area of Decommissioning of Oil and Gas facilities, thereby making them one of the few experienced Decommissioning companies globally.
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+Technology transfer
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+There are huge opportunities in key regions around the world where the skills and knowledge of people working in the decommissioning industry are going to be very advantageous. So, it is paramount for Government agencies (e.g., Petroleum Technology Development Fund and Nigerian Content Development and Monitoring Board) and companies to invest in decommissioning study. Investing in technology to know what suits our climate, locations, and regulations and to formulate cost-effective strategies. It is also an opportunity to develop an efficient industry that can be exported to other regions especially those similar to our terrain like the Gulf of Guinea. According to the UKCS Decommissioning Cost Estimate 2020 report in the United Kingdom, 2% cost reduction on a like-for-like basis was achieved in 2019, building on the 17% achieved in 2017/2018. This was driven by minor improvement in planning and execution practices, which helped reduce the estimated cost of platform and subsea infrastructure removals in the Northern North Sea (NNS) and Central North Sea (CNS) and reduce cost risk associated with estimating uncertainties. This type of research could be replicated here in Nigeria and if successfully implemented, can be exported to other regions. In Malaysia also PETRONAS has recognized that the upcoming abandonment works will necessitate more innovation and new technology in order to save costs. It is also optimizing costs through its research arms while also building human capabilities in decommissioning works through its Group Research and University collaboration.
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+Waste disposal and management
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+This could be an industry on its own. While not everything may be reusable, the assumption from the outset should be that each and every part will be a valuable building block in a future use, either on or off the site. Recycling and reusing of waste from decommissioning could provide raw materials for other projects and industries. Though there is lack of infrastructure for now, it could be work on in advance. Strategies to be used could include identifying opportunities for engaging relevant stakeholders and ensuring a good level of positive engagements. Also, the Regulators should initiate the Waste disposal and management process as early as possible and develop a sustainable implementation plan. Metals could be reuse or recycled by using them as Steel Rods, Tin Cans, Shipping Containers, and File Cabinets. These metals could be collected, processed, shredded and then melted in furnace at high temperature to produce blocks or sheets which could be sold to manufacturers of metal products. Also, these metals could be smelted to be used as fence or for decoration.
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+Conclusions
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+This work has highlighted different challenges that could confront decommissioning of aged Oil and Gas facilities in Nigeria and proffered some solutions. It is paramount to regard decommissioning as a process with its peculiar nature, as against a one-off project. Decommissioning of Oil production facilities anywhere in the world is challenging, but inevitable. With fast aging facilities in Nigeria's Oil and Gas sector, the need for relevant stakeholders to start planning and preparing for decommissioning on time is emphasized. Early planning and preparation would provide Nigeria the opportunity to tap from the experiences of places where decommissioning has been done and harness it to develop her own local manpower and capacity for decommissioning in Nigeria. Nigeria can then start exporting the skills, know-how, and experience acquired to other countries. Hence, decommissioning can result in win-win situation for all stakeholders and bring about economic benefit and employment generation. This study shows challenges on a substantial scale. Gaps in legislation and the supply chain, to mention a few, have an impact on the readiness to effectively undertake decommissioning. Noting that the inevitable will arrive sooner rather than later, regulators, operators, and service providers have to start working to close these gaps by enacting specialized decommissioning legislation, developing long-term integrated plans, and collaborating with the services sector to assure readiness.
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+However, the success of decommissioning in Nigeria will largely depend on political will on the side of the government and decisive role played by Regulators and Government agencies such as the Nigerian Upstream Petroleum Regulatory Commission, the Petroleum Technology Development Fund and the Nigerian Content Development and Monitoring Board.
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+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
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+References
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(1999) ‘Donan field decommissioning project’, Proceedings of the Annual Offshore Technology Conference, 3, pp. 211–219. 10.4043/10832-ms.Google Scholar Meenan, P. A. (1998) ‘Technical Aspects of Decommissioning Offshore Structures’, Gorman, D. G. and Neilson, J.Decommissioning Offshore Structures. London: Springer London, pp. 23–56.Google ScholarCrossrefSearch ADS  Mohd Nasahie Akbar Ali, I., Karim, M. A. and Rusli, H. (2020) ‘Decommissioning: Turning challenges into opportunities, through the eyes of the regulators’, Society of Petroleum Engineers – SPE Symposium: Decommissioning and Abandonment2019. 10.2118/199179-ms.Google Scholar Nwaobi, G. (2005) ‘Oil Policy In Nigeria: A Critical Assessment(1958-1992)’, Public Economics.Google Scholar OGA (2020) ‘UKCS Decommissioning Cost Estimate 2020’, (August), pp. 1–32. Available at: https://www.ogauthority.co.uk/media/6638/ukcs-decommissioning-cost-estimate-2020.pdf.Oil & Gas Decommissioning: Challenges & Opportunities (2020). Available at: https://www.ogv.energy/news-item/oil-gas-decommissioning-challenges-opportunities (Accessed: 29 September 2021).Prasthofer, P. H. (1998) ‘Decommissioning Technology Challenges‘. 10.4043/8785-MS.Price, W. R., Ross, B. and Vicknair, B. (2016) ‘Integrated decommissioning – Increasing efficiency’, Proceedings of the Annual Offshore Technology Conference, 4(May), pp. 3027–3033. 10.4043/27152-ms.Google Scholar Procaccini, G.et al. . (2021) The Coming Decommissioning Wave in Southeast Asia: What to Expect and the Relevance of Experiences in the North Sea and U.S. Gulf of Mexico. Available at: https://www.akingump.com/en/experience/industries/energy/speaking-energy/the-coming-decommissioning-wave-in-southeast-asia-what-to-expect-and-the-relevance-of-experiences-in-the-north-sea-and-us-gulf-of-mexico.html (Accessed: 29 September 2021).Royal Academy of Engineering (2013) ‘Decommissioning in the North Sea Decommissioning in the North Sea: A report of a workshop held to discuss the decommissioning of oil and gas platforms in the North Sea’, p. 15.Ruivo, F. M. and Morooka, C. K. (2001) ‘Decommissioning Offshore Oil and Gas Fields’, Proceedings – SPE Annual Technical Conference and Exhibition, pp. 3781–3793. 10.2523/71748-ms.Google Scholar Shaw, K. (1994) ‘Decommissioning and abandonment: The safety and environmental issues’, Society of Petroleum Engineers – SPE Health, Safety and Environment in Oil and Gas Exploration and Production Conference 1994, HSE 1994, pp. 293–300. 10.2523/27235-ms.Google Scholar ShellU.K.Limited (2015) ‘Shell U. K. Limited Brent Delta Topside Decommissioning Programme Consultation Draft’, (February), pp. 1–57.Google Scholar Steyn, P. (2009) ‘Oil Exploration in Colonial Nigeria, c. 1903-58’, The Journal of Imperial and Commonwealth History, 37(2), pp. 249–274. 10.1080/03086530903010376.Google ScholarCrossrefSearch ADS  Tularak, A., Khan, W. A. and Thungsuntonkhun, W. (2007) ‘Decommissioning challenges in Thailand’, Society of Petroleum Engineers – Asia Pacific Health, Safety, Security and Environment Conference and Exhibition 2007 – ‘Responsible Performance: Are We Doing the Best We Can’, pp. 338–344. 10.2118/108867-ms.Google Scholar Vann, R. (2020) Offshore decommissioning – challanges and opportunities. Available at: https://www.offshore-mag.com/field-development/article/14168417/offshore-decommissioning-challenges-and-opportunities.
+
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211920-MS
+
+
+

files/2022/Design and Construction of Rotary Drilling Rig Prototype.txt

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+----- METADATA START -----
+Title: Design and Construction of Rotary Drilling Rig Prototype
+Authors: Adekunle Adeniyi, Anselm Igbafe, Olokpa Ebis, Adebayo Ogunyemi, Sikiru Yusuff, Oluwadare Oyebode
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211999-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Drilling in search for hydrocarbon is an essential component of exploration and production activities. Chemicals, Drill rig, Casing, Tubing, Drill pipes and bits are basic requirements to successfully drill a well. Rotary Drilling rig is very crucial among the basic requirements. A major function of rotary drilling rig, is continuous circulation of drilling fluid and removal of cuttings. Hence, this paper focused on the design and construction of drilling rig prototype, for training purposes in academic environment. Components were constructed from the most suitable materials obtained from metal scraps individually, and when put together forms an integrated system that enables the drilling process to make a well. The prototype was produced successfully. The mixing hopper, hoisting and the mud circulatory systems were fully incorporated and connected. The rig prototype was, in principle, to transport fluid from the mud pit up the stand pipe to the swivel via the rotary hose down the drill pipe to the annulus and back to the mud pit through the shale shaker, De-sander, De-gasser, De-silter units, via the mud return line. The drawworks is to lift the drill pipe and lower it back into the rotary table with the aid of the drawworks motor and a top drive system.
+
+
+
+
+Keywords:
+drilling equipment,
+well control,
+annular pressure drilling,
+rig,
+representation,
+prototype,
+mini rotary drilling rig prototype,
+upstream oil & gas,
+drilling fluids and materials,
+pipe
+
+
+Subjects: 
+Drilling Operations,
+Pressure Management,
+Drilling Equipment,
+Drilling Fluids and Materials,
+Well control,
+Drilling fluid management & disposal
+
+
+
+
+Introduction
+
+
+Drill is to bore a hole in the earth, usually to find and remove subsurface formation fluids such as oil and gas. And drilling rig often refered to as rig, it is the derrick or mast, drawworks, and attendant surface equipment of a drilling or workover unit (Petroleum Extention Service, 1991)6. Drilling in search for hydrocarbons has gone through different stages and increasing levels of sophistications. In the early days of oil business, oil seepages were observed on the surface of the earth, such flows could be scoop and keep. Then came the cable tool sytem, up and down movements of heavy loads. Rotary drilling system is the modern and most sophisticated method of oil well drilling. Rotary drilling is a method in which a hole is drilled by a rotating bit to which a downward force is applied. The bit is fastened to and rotated by the drill stem, which also provides a passageway through which the drilling fluid is circulated. Additional joints of drill pipe are added as drilling progresses (Petroleum Extention Service, 1991)6.
+
+
+The three types or classifications of rotary drilling rigs are; Onshore, Swamp, Offshore Rigs
+
+
+The Onshore type of rig is also called land rig and were constructed in a way that, the derrick can be moved easily and reused for another well. Components in land rigs were skid mounted in such a way that rig can be moved in units and assemble at the location. Examples of the land rigs are the Jack Knife and Helicopter rig. The jack knife is a rig that has access road to site while the Helicopter drilling rig does not have access road to site because of the topography of the environment and communal problems (Gatlin Carl.,1960). The offshore rigs are put in place after its components were assembled on the barge, and then unit is towed to the location and sunk by flooding the barge. After a successful drilling operation is completed, the water is pumped out from the barge, allowing the barge to float and then moved to the next location (Robert, et al., 2017). Examples of offshore drill rigs are Jack up rig, Permanent platforms, Barge mounted/Submersible rig, Tender support rig, Drill ship, Semi-submersible rig, Floating Production and Storage Operations (FPSO). Wherever there is transition from land to deep offshore, depth sea bottom or competent layers determine the choice of appropriate rig. Among the available choices are Drillship, FPSO.
+
+
+As stated earlier, rotary drilling rig is an assembly of machines of different types and functions that are linked up to create a hole. Those components included, derrick or mast, substructure, Power and prime movers, hoisting components, rotating components, Circulating components, Well control component, Tubular and tubular handling equipment, and Bits (Paul Bommer, 2008). Each of the components listed are also made up of subconponents as shown in figure 1. In onshore operations, the rig includes virtually everything except living quarters. In this study, a signicant percentage of all the components were sourced locally. Figure 2 illustrated side views of a rotary drilling rig.
+
+
+Figure 1View largeDownload slideThe fluid circulation system in roatary drilling (Petroleum Extention Service 1991)Figure 1View largeDownload slideThe fluid circulation system in roatary drilling (Petroleum Extention Service 1991) Close modal
+
+
+Figure 2View largeDownload slideSide view of a Rotary Drilling RigFigure 2View largeDownload slideSide view of a Rotary Drilling Rig Close modal
+
+
+Various types of oil well that were drilled with rotary drilling, each with different functions and purposes are Exploration wells (or wildcat wells), Appraisal wells, Development or production wells, Relief wells are drilled, and Injection well. Depth of each these wells varies.
+
+
+Plate 1View largeDownload slideA Rotary Drilling RigPlate 1View largeDownload slideA Rotary Drilling Rig Close modal
+
+
+Methodology
+
+
+A design of rotary drilling rig patent number #3,826,472, plate 1, registered in United State of America on 30th July 1974 as detailed by (Woolslayer, et al., 1974) is adopted as the base for construction of the rig prototype shown figure 7.
+
+
+Figure 3View largeDownload slideFlow chart for detailed proceduresFigure 3View largeDownload slideFlow chart for detailed procedures Close modal
+
+
+Figure 7View largeDownload slideThe Rotary Drilling RigrotoptypeFigure 7View largeDownload slideThe Rotary Drilling Rigrotoptype Close modal
+
+
+Basic steps taken to arrive at the prototype rig was detailed below (Charles, et al.,2016).
+
+
+List of Equipment
+
+
+The following set of equipment were used during the construction; Filing machine, Brazing machine, Welding torch, Generator, Grinding machine, Pliers, Screw drivers, biding wires and Cutters.
+
+
+Materials Used
+
+
+A list of materials and the quantities of materials used to make a mini rotary drilling rig prototype are tabulated in table 1.
+
+
+Table 1Materials Used Materials
+            . Quantity
+            . Plates 4 pieces 13mm Rods 5 pieces 8mm Rods 4 pieces 1-inch Square pipe 3 pieces Angle iron 2 pieces 1¾ inch pipe 1 piece 1 inch pipe 1 piece ¼ rod 1 piece 1¾ pvc pipe 1 piece 1 inch pvc pipe 1 and ½ pieces ¾ inch pipe 1 piece ½ inch pipe 2 and ½ pieces 1 inch valve 7 pieces ¾ valve 8 pieces ½ inch valve 12 pieces 1 inch elbow 5 pieces ¾ inch elbow 9 pieces ½ inch elbow 6 pieces Copper cable 15 ft Cable connectors 4 pieces Bulbs 5 pieces Lamp holders 5 pieces 0.5 horse power pump 2 pieces Draw works motor 1 piece Top drive motor 1 piece Draw works control switch 1 piece Breaker switch 1 piece Materials
+            . Quantity
+            . Plates 4 pieces 13mm Rods 5 pieces 8mm Rods 4 pieces 1-inch Square pipe 3 pieces Angle iron 2 pieces 1¾ inch pipe 1 piece 1 inch pipe 1 piece ¼ rod 1 piece 1¾ pvc pipe 1 piece 1 inch pvc pipe 1 and ½ pieces ¾ inch pipe 1 piece ½ inch pipe 2 and ½ pieces 1 inch valve 7 pieces ¾ valve 8 pieces ½ inch valve 12 pieces 1 inch elbow 5 pieces ¾ inch elbow 9 pieces ½ inch elbow 6 pieces Copper cable 15 ft Cable connectors 4 pieces Bulbs 5 pieces Lamp holders 5 pieces 0.5 horse power pump 2 pieces Draw works motor 1 piece Top drive motor 1 piece Draw works control switch 1 piece Breaker switch 1 piece View Large
+
+
+Components and Dimemtions
+
+
+Similarly, components and dimensions of the mini rotary drilling rig prototype are tabulated in table 2.
+
+
+Table 2Components and Dimension Components
+            . Material
+            . Dimension
+            . The barge 2 rods and 1½ plates 36" * 82" * 9" the engine room 1-inch pipe and plate 22" * 30" * 39" The pump room and sack room 1-inch pipe, plate, and mesh 28" * 30" * 27" The pits Metal plate and pvc pipe fittings 43 litres The living quarters and helideck 8mm rod, plate, square pipe - The pipe deck 8mm rod, 1 inch square pipe, 2inch square pipe - The rig floor 8 mm rods, metal plate - The draw works Electric motor, metal plate shaft bolt and nuts and pulley - The drillers console Switches indicator light metal plate - The choke manifold Pvc pipes and pvc fittings - The rotary table Pvc pipes - The poor boy degasser Metal cylinder and pvc pipe - The derrick 8mm rods - The crown block Pulley and shaft - The travelling block Metal plate pulleys shaft and nuts - The traveling block dolly Metal bar - The top drive Electric motor metal bars and metal plates - The top drive motor Electric motor - The shale shaker Metal plate - The degasser Metal plate - The deader Metal plate - The desilter Metal plate - The blow out preventer (BOP) Pvc pipe and pvc fittings - Components
+            . Material
+            . Dimension
+            . The barge 2 rods and 1½ plates 36" * 82" * 9" the engine room 1-inch pipe and plate 22" * 30" * 39" The pump room and sack room 1-inch pipe, plate, and mesh 28" * 30" * 27" The pits Metal plate and pvc pipe fittings 43 litres The living quarters and helideck 8mm rod, plate, square pipe - The pipe deck 8mm rod, 1 inch square pipe, 2inch square pipe - The rig floor 8 mm rods, metal plate - The draw works Electric motor, metal plate shaft bolt and nuts and pulley - The drillers console Switches indicator light metal plate - The choke manifold Pvc pipes and pvc fittings - The rotary table Pvc pipes - The poor boy degasser Metal cylinder and pvc pipe - The derrick 8mm rods - The crown block Pulley and shaft - The travelling block Metal plate pulleys shaft and nuts - The traveling block dolly Metal bar - The top drive Electric motor metal bars and metal plates - The top drive motor Electric motor - The shale shaker Metal plate - The degasser Metal plate - The deader Metal plate - The desilter Metal plate - The blow out preventer (BOP) Pvc pipe and pvc fittings - View Large
+
+
+Procedures for each Components
+
+
+Table 3 contained summary of procedural steps to produce component parts of the mini rotary drilling rig prototype (Ding, et al.,2018).
+
+
+Table 3Procedures S/N
+            . Component
+            . Procedure
+            . 1 Barge Rods were welded together to provide strength and structure of the barge. Metal plate was cut to size to fit the dimension of the barge. There was also the provision of the moon pool 2 Engine room Rods, metal plates were welded together to fabricate the engine room. The engine room was constructed with wires, sockets and a breaker switch, 3 Pump and sack room Rods and metal plates were welded together to fit the dimension of the pump and sack room in such a way that it accommodates the mud pits, the mud pumps, the mixing pump and the mud mixing chemicals. 4 The mud pits 2 mud pits were constructed i.e the active and the reserve pits and they were properly connected to the mud pump and mixing pump using appropriate pvc fittings. 5 The living quarters and helideck Rods and metal plates were welded together to meet the require specification of the living quarters, bulbs were also installed to help illuminate the living quarters in order to provide beauty. Above the living quarters is the helideck where choppers land and take off with personnel’s 6 The pipe deck This is found just above the pump and sack room rods were welded to represent the pipe rack, the cat walk was constructed with rods and plates welded together. Also hand rails were constructed on it to provide guide 7 Rig floor 1-inch square pipe metal plates and rods were constructed and welded together to meet the desired specification of the rig floor in order for it to house and accommodate all components that will be mounted and installed on it 8 The draw works Metal plate, drum, nut, bolt and washers were welded and designed to carry out the function of the draw works that is provide the strength for hoisting movement. The electric motor was also connected to the draw works 9 The drillers console Metal plates and switches were designed to form the drillers console on the rig floor 10 Choke manifold Pvc pipes, valves and elbows were designed to divert and control flow 11 The stand pipe The stand pipe is made out of pvc piping, it takes mud from the pit and delivers it to the drill pipe via the rotary hose and delivers it to the drill pipes 12 Rotary table The rotary table was constructed out of pvc pipe to guide the drill string into the hole 13 Poor boy degasser A cylindrical metal was fabricated and designed to suit the representation of the poor boy degasser 14 The derrick Rods were cut to suit the mast height and also to brace in other to add strength to the derrick so as to support the raising and lowering of the load the draw works pulls 15 The crown block A single pulley was placed at top of the derrick to represent the crown block 16 The travellin g block Metal plate was cut to shape and size also welded to meet the description of the travelling block 17 Travelling blockdolly A long length of metal bar was welded for the top of the derrick to just above the draw works to aid the hoisting movement of the top drive. 18 The to pdrive The top drive was constructed out of metal plated metal bars and an electric motor it has a pair of bails that holds the elevator also a kelly hose that is connected to the stand pipe which aids the passage of mud from the pit to the drill pipe. 19 Shale shaker, degasser, desilter and de sander Metal plates were cut and welded together to make physical representations of these components 20 The blow out preventer The blow out preventer was constructed in a way that it is connected directly from the choke manifold allowing you to kill the well the kill line and and diverting the flow from the annulus to the poor boy degasser first via the choke line before going back to the shale shaker. It also provides an opening for the drill string 21 The mud return line Pvc piping was cut and positioned in a slant position to allow the mud return to the pit by gravity. Mud flows out of the drill pipe and come up through the annulus and returns to the mud pit via the mud return line. As the drilling system is a closed system S/N
+            . Component
+            . Procedure
+            . 1 Barge Rods were welded together to provide strength and structure of the barge. Metal plate was cut to size to fit the dimension of the barge. There was also the provision of the moon pool 2 Engine room Rods, metal plates were welded together to fabricate the engine room. The engine room was constructed with wires, sockets and a breaker switch, 3 Pump and sack room Rods and metal plates were welded together to fit the dimension of the pump and sack room in such a way that it accommodates the mud pits, the mud pumps, the mixing pump and the mud mixing chemicals. 4 The mud pits 2 mud pits were constructed i.e the active and the reserve pits and they were properly connected to the mud pump and mixing pump using appropriate pvc fittings. 5 The living quarters and helideck Rods and metal plates were welded together to meet the require specification of the living quarters, bulbs were also installed to help illuminate the living quarters in order to provide beauty. Above the living quarters is the helideck where choppers land and take off with personnel’s 6 The pipe deck This is found just above the pump and sack room rods were welded to represent the pipe rack, the cat walk was constructed with rods and plates welded together. Also hand rails were constructed on it to provide guide 7 Rig floor 1-inch square pipe metal plates and rods were constructed and welded together to meet the desired specification of the rig floor in order for it to house and accommodate all components that will be mounted and installed on it 8 The draw works Metal plate, drum, nut, bolt and washers were welded and designed to carry out the function of the draw works that is provide the strength for hoisting movement. The electric motor was also connected to the draw works 9 The drillers console Metal plates and switches were designed to form the drillers console on the rig floor 10 Choke manifold Pvc pipes, valves and elbows were designed to divert and control flow 11 The stand pipe The stand pipe is made out of pvc piping, it takes mud from the pit and delivers it to the drill pipe via the rotary hose and delivers it to the drill pipes 12 Rotary table The rotary table was constructed out of pvc pipe to guide the drill string into the hole 13 Poor boy degasser A cylindrical metal was fabricated and designed to suit the representation of the poor boy degasser 14 The derrick Rods were cut to suit the mast height and also to brace in other to add strength to the derrick so as to support the raising and lowering of the load the draw works pulls 15 The crown block A single pulley was placed at top of the derrick to represent the crown block 16 The travellin g block Metal plate was cut to shape and size also welded to meet the description of the travelling block 17 Travelling blockdolly A long length of metal bar was welded for the top of the derrick to just above the draw works to aid the hoisting movement of the top drive. 18 The to pdrive The top drive was constructed out of metal plated metal bars and an electric motor it has a pair of bails that holds the elevator also a kelly hose that is connected to the stand pipe which aids the passage of mud from the pit to the drill pipe. 19 Shale shaker, degasser, desilter and de sander Metal plates were cut and welded together to make physical representations of these components 20 The blow out preventer The blow out preventer was constructed in a way that it is connected directly from the choke manifold allowing you to kill the well the kill line and and diverting the flow from the annulus to the poor boy degasser first via the choke line before going back to the shale shaker. It also provides an opening for the drill string 21 The mud return line Pvc piping was cut and positioned in a slant position to allow the mud return to the pit by gravity. Mud flows out of the drill pipe and come up through the annulus and returns to the mud pit via the mud return line. As the drilling system is a closed system View Large
+
+
+Results and Discussion
+
+
+Results obtained on each component are listed under appendices.
+
+
+The Shale Shaker, Desander, Degasser and Desilter
+
+
+These components are all mere representation to create awareness to the learner and also to give a physical representation on how these components are interconnected to one another. A physical representation of the shale shaker, desander, degasser and desilter after construction and installation is shown in  appendix F. The function of this unit is to remove impurities in the mud stream.
+
+
+The Mixing Pump and Hopper
+
+
+The mixing pump unit also consists of 2 pumps with a manifold. 1 active and 1 on standby. In the process of mixing, a particular pit is circulated via the mixing pump the mixing hopper is mounted along the circulating line. Mud materials such as barite, bentonite, drilling salt amongst others are being introduced through the hopper. A physical representation of the mixing pump and the mixing hopper is shown in  appendix D.
+
+
+Conclusions
+
+
+A complex system of rotary drilling rig has been simplified in this study. Therefore, learning could made really easy for students. Similarly, a real operational rig could be produced by upscaling and use of quality materials. Another beauty of this study is that, the technology could be domesticated in some environments of needs. Such that a fewer amount of the component will be purchace.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgements
+
+
+We would like to express our appreciations to Mr. and Mrs. Ebis Olokpa for their financial support and Abiola Construction Yard for the permissions to make use of his workshop and tap from his wealth of experience. Contributions made by Engrs. John Owolabi, Busayo Ajediti, and Oluwagbenga Omotara were appreciated.
+
+
+Appendices
+
+
+The Barge
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+The Engine Room
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Field Men Quarters and Helipad
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Mixing Pump and Hopper
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Derrik Mast
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+The Shale Shaker, Desander, Degasser and Desilter
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+References
+
+
+Charles P.Alvord, Elsie M.Davis(2016): The Alvord and Davis Drill and Problem Book in Arithmetic: For Fifth to Eighth Grades. Published by Leopold Classic Library. Available on https://www.amazon.com/Alvord-Davis-Drill-Problem-Arithmetic/dp/B01AXXHVGY.Google Scholar DingZhu, KenjiFurui (2018). Modern Completion Technology for Oil and Gas Wells. First Edition. Published by McGraw Hill; Kindle edition. Available on Modern Completion Technology for Oil and Gas Wells, Zhu, Ding, Furui, Kenji, eBook - Amazon.com.Google Scholar Gatlin, Carl. (1960). Petroleum Engineering: Drilling and well Completion. Printed by the Library of Congress, with Catalog Number 60-6874. Downloadable on https://docs.google.com/file/d/0B8SQRbBWV6-pZUpWWU9VVVZmSU0/edit?resourcekey=0-5oe8nwpoEmkkx3V0-INheQ.Google Scholar PaulBommer (2008). A Primer of Oilwell Drilling. Published by The University of Texas Press. Available on https://www.libramar.net/news/a_primer_of_oilwell_drilling/2020-08-07-1918.Google Scholar Petroleum Extention Service (1991). Well Servicing: Introduction to Oil Well Service and WorkOver. Lesson 1, 2nd Edition, published by Petroleum Extension Service, Continuing & Extended Education, The University of Texas at Austin, Austin, Texas. In cooperation with Association of Energy Service Companies, Dallas, Texas.Robert F.Mitchel, StefanZ.Miska, R. F. (2017). Fundamentals of Drilling Engineering. SPE Textbook Series Vol. 12. Available on https://petroleumpdf.com/tag/fundamentals-of-drilling-engineering-download-free/.Google ScholarCrossrefSearch ADS  Woolslayer, Homer J., Jenkins, Cecil (1974). Oil Well Derrick with Guide Track for Travelling Block Dolly. United States Patent No. 3,826,472, available on https://www.freepatentsonline.com/3826472.pdf.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211999-MS
+
+
+

files/2022/Developing a Chemical Database for Resolving Enviromental Issues in the Petrochemical Industry in Nigeria.txt

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+----- METADATA START -----
+Title: Developing a Chemical Database for Resolving Enviromental Issues in the Petrochemical Industry in Nigeria
+Authors: Abraham Ogheneruemu Ekperusi, Anthonia Ejiroghene Gbuvboro
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211948-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Petrochemical exploration in Nigeria poses a significant threat to the environment, health and livelihoods of local people. The inability to find a holistic solution to address amicably the issues associated with oil and gas exploration and production has resulted in an unending wave of tension, crises and countless legal battles between communities and oil operators. This development is further complicated by the lack of adequate capacity on the part of regulators in the sector. The situation has forced some oil operators to move their operations from land and shallow waters into the deep sea with the hope to reduce hostilities within operational facilities and conflict with local people. Despite efforts to have a better understanding among the stakeholders, particularly oil operators and local communities, environmental issues persist creating mistrust between parties. Developing a chemical database with a comprehensive contaminants profile in the petrochemical industry would improve the management of chemical spills and associated issues and bring some level of fairness to conflict resolution in the sector.
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+Keywords:
+downstream oil & gas,
+chemical spill,
+air emission,
+investigation,
+nigeria,
+regulator,
+sustainability,
+operator,
+stakeholder,
+social responsibility
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+
+Subjects: 
+Environment,
+Sustainability/Social Responsibility,
+Air emissions,
+Oil and chemical spills
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+
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+Introduction
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+Petrochemical exploration in Nigeria poses a significant threat to the environment, health and livelihoods of community members due to the lack of global best practices in petrochemical spill management in the region. The inability to find a holistic solution to address amicably the environmental issues associated with oil and gas exploration and production has resulted in an unending wave of tension, crises and countless legal battles between communities and oil operators (Ekperusi et al. 2020; Ekperusi and Ekperusi 2021).
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+In a bid to avoid direct conflict with local communities and reduce hostilities within operational facilities, several multinational companies divest their interest in their onshore and shallow assets to indigenous players and move their operations to offshore or deep waters. Many stakeholders view this geographical shift as evasive tactics by multinationals to abdicate their responsibility to environmental stewardship which is well codified in corporate communications but lacking in practice in the Nigerian oil and gas industry (Ekperusi et al. 2020; Ekperusi and Ekperusi 2021). Shifting operational base from onshore to offshore by oil giants has not ameliorated the lingering issues in the region. Although oil assets may become inaccessible to local communities for picketing actions, oil spill accidents that occurred several kilometres on the high seas get to the shore through tidal and wave action and negatively impact the life support system of local communities like fishing grounds, aquaculture ponds and farmlands (Ekperusi et al. 2020).
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+Despite efforts to have a better understanding among the stakeholders especially operators and local communities, environmental issues persist creating mistrust between parties. The lack of transparency, frustration and suppression of voices related to spill management has led to the long quest for justice, even outside the shores of Nigeria by local communities (Ekperusi et al. 2020; Ekperusi and Ekperusi 2021). The situation is further complicated by the lack of adequate capacity on the part of federal and state regulators and the inability to apply current and sound technological tools in the industry, particularly with oil spill reporting, assessment, containment, clean-up and restoration (Rim-Rukeh, 2015; Ekperusi et al. 2020; Ekperusi and Ekperusi 2021). This persistent scenario is not good for an industry that plays a critical role in providing energy solutions to society.
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+Conventional analytical methods for the determination of organic compounds in environmental matrices are becoming inadequate in meeting the needs of monitoring programmes designed to evaluate chemical safety for public health (Sobus et al. 2018). Also, as more chemicals are developed and used in the petrochemical industry, conventional analytical techniques will be insufficient to provide the relevant knowledge and insight to protect human health and the environment. Crude oil contains certain chemical biomarkers or fingerprints, depending on its origin, location and geologic conditions. Crude oil from different wells contains different molecular makeup that could be exploited to differentiate one from another. The application of chemical forensics with chromatographic methods could unravel the chemical profile unique to oil extracted from a particular oilfield (Duncombe, 2019).
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+Developing a chemical database with a comprehensive contaminants profile in the petrochemical industry with the combination of conventional and new analytical tools is essential for the management of chemical spills and associated issues in an open and transparent manner to resolve key issues in the industry and also bring some level of fairness to conflict resolution.
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+Environmental Issues in the Petrochemical Industry
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+In Nigeria, within the last three decades, several environmental issues have perpetually put the petrochemical industry in a bad light. Some of the major issues are highlighted below;
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+Oil Spills
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+Oil spill management, either from accidents, old and broken facilities or sabotage in the Niger Delta region has left decades of scars in the memory of local communities, oil operators and regulatory agencies. The lack of adequate spill management programme over the years has resulted in a drastic decline of mangrove swamp forests, rendered many lands unproductive for agricultural purposes, and destroyed fishing grounds and aquaculture farms in local communities. In extreme cases, it has led to military invasion, threats, arrest, detention and death of several community members and leaders. For oil operators, it has resulted in the assault of oil workers, kidnapping, and death, while regulators have also experienced the wrath of community members due to a perceived lack of transparency and inconclusive investigations of oil spill management in the region (Rim-Rukeh 2015; Ekperusi et al. 2020; Ekperusi and Ekperusi 2021).
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+In many instances, community members are the first to raise the alarm of an impending oil spill in the coastal region, which is further amplified in the news circle by civil society groups operating in the region. This in turn triggers state and federal regulators into action (Ekperusi and Ekperusi 2021). Oil operators are very quick to deny the existence of oil spills. This approach has been attributed to many reasons. The denial could provide oil operators with the opportunity to swiftly try to contain the spill and clean up the impacted area in the cases of minor spill incidents and claim there was no spill in the first place. It also helps the spiller to avoid conflicts and payment of compensation to impacted community resources and fines from regulators where the cause of the spill is established to be a result of negligence on the part of the spiller. Regulators lack the capacity to swiftly mobilize resources to investigate oil spill incidents and rely on the spiller to do so. A spiller can deny an oil spill or slow the investigation delay in mobilization for joint investigation visits. When the evidence of an oil spill becomes incontrovertible due to the volume or extent of the spill, the spiller could attribute the spill incident to sabotage from third-party activities. The argument for sabotage is gaining more credence due to the increased level of third-party activities in the sector. When an operator attributes an oil spill to sabotage, the onus now lies on the affected community and the regulators to prove otherwise. Although there is no justification for oil operators to abdicate their corporate and social responsibility, there has been an unprecedented rise in oil facilities tempering and oil theft in the Niger Delta region (NOSDRA 2022). Community members and their associates are known to puncture pipelines and steal oil for artisanal refining. Others tamper with pipelines and create channels to direct spilt products into farmlands, shrines and other vital resources to increase compensation claims from oil operators. This practice has complicated oil spill management and conflict resolution process in the Niger Delta region. Over the years, the activities of illegal oil bunkers and local refiners have contributed significantly to environmental degradation, oil revenue loss and extra burden on oil operation activities. There is a need for new thinking in dealing with this menace in the region if the environment must be spared from the activities of third parties in the oil and gas industry.
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+In January 2021, fishermen and community leaders of Koluama in Bayelsa State reported oil leakage around well 5 of the Funiwa oilfields, but officials of Chevron Nigeria Limited (CNL) denied the claims. It was further reported that CNL hurriedly deploy helicopters to spray chemical dispersant on the affected area while using security agents to stop community members from accessing the area and taking photographs (Oyadongha 2021). This is done to stop community members or advocacy groups from procuring evidence. Without evidence, oil operators could avoid regulatory oversight, sanction and liability, while they quietly mobilize a response team to mitigate the spill (Oyadongha 2021). In November 2021, women in various communities in Warri Southwest accused CNL of negligence in an oil spill that was affecting fishing communities, but CNL denies the claim. CNL insisted that aerial surveillance did not show any evidence of a spill from any of their facilities (Maclean et al. 2021). Dissatisfied with the inaction of CNL, women from several communities staged a protest at CNL office in Warri to force the company to act towards mitigating the spill (Maclean et al. 2021). Fishing in creeks and mangroves is dominated by women, while men fish in the coastal and open seas, so it is important that these women would speak up when the source of their livelihood is threatened.
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+More than 80 per cent of oil spills in the Niger Delta region generates controversy in trying to determine the remote and immediate cause. Oil operators are quick to blame spills on sabotage while community members accuse oil companies of negligence. Negligence results in fines and compensation while sabotage absolves oil companies from any fines or compensation. Notwithstanding the increasing third-party activities in the sector, the majority of the oil installations across the region have overwhelmingly exhausted their lifespan and could easily malfunction on their own accord. Efforts to replace old facilities have been very slow on the part of oil operators and sometimes made complicated by community members. Some communities can block access to oil contractors and collect bribes while others want the contracts to be awarded solely to members of their community even when they lack the competence to handle such technical projects. The outcome of the intervention of regulatory agencies at the federal and state level, with the joint participation of all interested stakeholders, may not be suitable for the concerned parties. Federal regulators could be seen to be shielding oil operators due to the large stake of the federal government in joint venture operations or as a result of bribery and corruption, while state regulators could be perceived to be supporting communities against multinationals due to ties or closeness to impacted communities.
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+For over 12 months, Shell Petroleum Development Company (SPDC) and Ikarama Community in Bayelsa State were in a confrontational face-off due to a pipeline spill in November 2019. The National Oil Spill Detection and Response Agency (NOSDRA) conducted a joint investigation and the report concluded that the spill at Ikarama Community was due to corrosion, but the report was rejected by SPDC which renders the investigation inconclusive. It is interesting to note that although NOSDRA employed technology owned by SPDC in the investigation, the outcome of the investigation which indicated that the spill was a result of corrosion was rejected by SPDC (Oyadongha 2020). The lack of adequate legal and regulatory provisions, unreliable infrastructure and inadequate skilled manpower in regulatory agencies weakens the agency's capacity to hold oil operators accountable. It is difficult for regulators to make a balanced judgement where the regulator relies on tools from operators to investigate the activities of operators. The funding of joint investigation is not also from an oil fund but from the operators. These poor structures also weaken the capacity of regulators to act independently in holding accountable the big players in the oil and gas industry. Multinationals are usually responsible for the funding of joint investigation, laboratory analysis and even printing of reports. Such a situation gives oil operators undue advantage in the sector to the detriment of local communities. It creates a conflict of interest and gives the perception that the regulator and the operators are working together against the interest of affected communities (Rim-Rukeh 2015; Ekperusi et al. 2020; Ekperusi and Ekperusi 2021). This is further complicated by misrepresentation or admission of errors in oil spill reporting by NOSDRA (Akpan 2020).
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+Legal actions against oil operators in Nigeria are defeated by technicalities, lack of adequate representation and long legal battles which in most cases, discourage affected communities from pursuing legal action. It took 13 years for local community members in Ogoni with the Dutch judiciary to get justice and settlement for a spill that occurred in 2008 and 2009. Some of the litigants did not live long to see the victory. It was only after the victory that Shell started the clean-up of the spill (Falayi 2021). The spill which was due to negligence as a result of old oil facilities in the region was initially dismissed by Shell as an act of sabotage which does not require compensation and settlement in the oil and gas industry. The existing approach to oil spill management is unreliable and inadequate. There is a need to find alternative and improved methods for oil spill assessment that is fairly acceptable to all interested parties (Rim-Rukeh, 2015; Ekperusi et al. 2020).
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+Mystery Spills
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+Any oil spill whose source of origin is unknown is referred to as a mystery spill. There are multiple reports of mystery spills recorded in the Niger Delta region every year (Shell 2020). Many factors are attributed to a mystery spill. Leakage from an illegal bunkering vessel can result in a mystery spill. Illegal bunkers pay little attention to the environment. Leakage from an illegal bunkering vessel sailing across the creeks or coastal area is of less priority to the actors, who are more concerned about reaching their destination and disposing of the oil for profit. Oil operators could also remove the source of a spill by repairing a damaged pipeline and deny the spilt products within their operational vicinity to avoid compensation or fines. CNL and Conoil dismissed the spill reported within the operational assets of both companies after fishermen sighted a spill on the Atlantic coast (Oyadongha 2021). An investigation by NOSDRA did not provide conclusive evidence on the origin of the spill (Oyadongha 2021). Oil operators are expected to clean up a mystery spill within their operational proximity but compensation and fines are largely avoided, hence the interest in denying a spill keeps increasing in the oil industry. Mystery spills can only be resolved by chemical fingerprinting and biomarker analysis of the spilt products. State and federal regulators are incapable of forensic analysis in oil spill investigations, hence the need for a chemical database becomes urgent and necessary for oil spill management in Nigeria.
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+In September 2019, oil mysteriously started washing up on Brazilian beaches. The oil spread across 2,000 kilometres affecting beaches in nine coastal states, killing wildlife and forcing beach closures (Duncombe, 2019). Petrobras, the Brazilian state oil company denied any involvement in the spill. The Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA), further indicated that the spill does not match any of the oil in their oil bank. A separate analysis by the Federal University of Bahia supported IBAMA's findings. This led to a further investigation to unravel the mystery spills. Samples were sent to the Woods Hole Oceanographic Institution (WHOI) in the United States for comprehensive analysis to characterize the hydrocarbons present in the oil. Other samples were sent to Norway and France for further analysis (Duncombe 2019). After comprehensive analyses involving chemical fingerprinting, oil transport models and three-dimensional mapping, the mystery oil was found to be of Venezuelan origin. The oil was narrowed down to three oilfields in Venezuela (BBC 2019). The findings were rejected by the Venezuelan government but a further analysis indicated a possible spill from a vessel carrying Venezuelan crude oil through the Brazilian coast to Europe or Asia. Due to sanctions from the United States, vessels carrying oil from Venezuela usually turn off their tracking device to avoid secondary sanctions from the United States, but the investigation was able to attribute the oil spill to a Greek-flagged ship carrying Venezuelan crude oil (BBC 2019).
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+Gas Spill
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+In August 2013, community members reported gas leakage spewing gas into the air and oil into the coastal environment from CNL gas field off the Atlantic coast in Bayelsa State. CNL denied the allegations, but community members reported the deployment of chemicals by CNL to sink oil along the coastline. The leakage was reported about 5 km from the location of a similar gas explosion in January 2012 (Oyadongha 2013). Community members decry the pollution of the air and marine environment by the gas leakage with associated oil discharge. No investigation was initiated by regulators to ascertain the cause of the spill, potential impact and possible remedial action.
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+Massive Fish Kills
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+There are several events both natural and human-induced that could result in massive fish kills in an aquatic ecosystem. During hot weather, the temperature in a water body increases and dissolved oxygen is usually consumed at a faster rate by microbial degradation of organic matter. If the dissolved oxygen in water gets below the critical threshold needed by certain species, it could trigger discomfort and other physiological processes that could lead to the suffocation of aquatic species. Also, the introduction of chemicals especially agrochemicals like fertilizers and pesticides with nitrogen and phosphorous could accelerate the growth of microbes and algae, which could increase the algal population giving rise to algal blooms. During algal blooms, oxygen levels in water decline significantly below the levels required by certain species which could lead to suffocation and death of species (USGS 2022).
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+Drilling and exploration activities could also result in a massive fish kill (USGS 2022). The release of naturally occurring radioactive materials (NORMs) from oil and gas wells, the illegal and unabated dumping of untreated produced water in coastal areas and other associated chemicals in oil and gas exploration could result in massive fish kills. Coastal areas are relatively large for toxic compounds release to have impacts on marine biodiversity due to dilution factor, but the release of very dangerous chemicals could affect highly sensitive species caught up in a chemical zone, resulting in death. Although there are reports of harmful algal populations in the coastal region of Nigeria and the Gulf of Guinea, their distribution and effects on marine life have not been substantiated to warrant massive species die out (Zendong et al. 2016).
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+Within the last few years, there have been rising cases of massive fish kills reported within coastal communities in oil-producing states of Ondo, Delta, Bayelsa, Rivers and Akwa Ibom (Chinwo 2020). In February 2020, community members and environmental advocates attributed the massive fish kill reported in the region to the illegal discharge of toxic chemicals mixed with sludge into the coastal environment from the Forcados oil terminal in Ogulagha, Delta State which was promptly denied by Shell (Akam 2020). Worried about the implication of massive fish kills on the environment, health and economy of the affected coastal states, various stakeholders including federal lawmakers, scientists, civil societies, environmental advocates and local communities call for a thorough investigation to unravel the mystery behind the massive death of fishery products (Salem 2020). Stakeholders went further to urge the governments at the federal and state level to declare the situation as a public health emergency to avert potential crises for human health, food security, community livelihoods, and biodiversity in Nigeria (Oluwarore et al. 2020). They warned that such events maybe not be unconnected to the activities of major oil and gas companies operating within the region (Salem 2020). Personal investigation and preliminary findings from community leaders and experts in the region indicated that croaker fish and the crabs were the affected species along the coastline (Oluwarore et al. 2020). A multi-agency investigation led by NOSDRA, the Nigerian Maritime and Safety Agency (NIMASA), National Environmental Standard and Regulations Enforcement Agency (NESREA), Nigerian Institute for Oceanography and Marine Research (NIOMR) was undertaken. After weeks of preliminary investigation, the head of NOSDRA gave a press release that the massive fish death is unrelated to oil and gas activities in the region. He indicated that hydrocarbons in the investigated samples were below regulatory limits in water, sediments and fish tissue analysed. Elevated levels of heavy metals such as cadmium, chromium, copper, zinc and iron found in the samples were attributed to toxic wastes from non-extractive industrial activities (Premium Times, 2020). The press release by NOSDRA without exhaustive investigation angers local community leaders, environmental advocates and scientists who rejected the findings and regrettably indicated that the federal regulator is shielding oil operators from their nefarious activities in the region (Akam, 2020; Oduware 2020). Separate investigations carried out by ministries of the environment in the affected states were not disclosed to the general public.
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+Government agencies are only able to conduct targeted screening and preliminary investigations during a chemical incident in the region. The limited approach to environmental monitoring is unhelpful in resolving critical environmental, ecosystem and health issues in the region. It is usually narrow in scope and lacking in the use of current analytical tools for resolving environmental questions and proffering answers to interested stakeholders.
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+Figure 1View largeDownload slideDead fishes along the coastline (Oduware 2020)Figure 1View largeDownload slideDead fishes along the coastline (Oduware 2020) Close modal
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+Figure 2View largeDownload slideDead crabs along the shoreline (Brachyura sp), (Oluwarore et al. 2020).Figure 2View largeDownload slideDead crabs along the shoreline (Brachyura sp), (Oluwarore et al. 2020). Close modal
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+Black Soot
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+Air pollution is one of the major environmental problems confronting Nigeria, particularly the Niger Delta region. Aside from a few studies by researchers and corporate organizations, there are no comprehensive or empirical studies on the magnitude of the degrading air quality and its deleterious effects on the environment, ecosystems and people of the region (Ana 2011). Black soot or black carbon has become a persistent occurrence in the Niger Delta, especially in Port Harcourt metropolis. The sky of Port Harcourt is usually covered in dense soot. In the earlier hours of the day, black soot is seen in residential amenities and on vehicles parked overnight across the state. The city sunlight is gradually being covered by a cloud of black soot. The soot has been attributed largely to the activities of illegal refining of crude oil in the creeks by local people. There are public health concerns as soot is reported in nasal droppings from residents of the city (Brisibe 2017). Both state and federal regulators have promised to investigate the root cause and stem the situation, but progress has not been made to stop the spread of ‘soot rain’ in the city (Brisibe 2017).
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+Preliminary investigations from environmental officials have attributed the black soot to the burning of old tyres for scrap copper harvesting and illegal oil refining as the main culprit of black soot in the city (Brisibe 2017). Other factors attributed to the worsening air quality condition in the city include the use of generators and industrial plants for electricity supply, increase burning of wastes and increase vehicular emissions. To mitigate the black soot, federal regulators also shut down an asphalt-processing plant due to a lack of air quality control measures (Brisibe 2017).
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+The worsening black soot situation will increase and accelerate the environmental problems in the region. It could lead to impaired visibility, degrading air quality, respiratory diseases, increased acid rain and thermal conditions (Ana, 2011). Doctors are already raising the alarm about increasing consultations for respiratory infections in the city (Brisibe, 2017). Common health effects include breathing difficulties, bronchitis, aggravated asthma, cardio-respiratory disorder, eye and skin irritations and diseases. Severe health effects may include cancers (skin, eye and lungs) and birth defects (Brisibe, 2017; Ana, 2011). The soot situation is gradually spreading across the states in the region. Soot has been reported in Bayelsa, Delta and Akwa Ibom State (Dachen, 2022). Industrial activities and emissions from oil refineries, petrochemical plants, liquefied natural gas, chemical fertilizer, aluminium smelters, paper, cement, flour, wood, battery and textile industries, gas flaring and pipelines explosion all releases gases and particulates into the atmosphere and could be contributing significantly to the rising soot epidemics (Ana, 2011). Without a comprehensive assessment and chemical profiling of the air pollutants in the affected areas, it is difficult to tackle the soot crisis from spreading across the region.
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+Figure 3View largeDownload slideBlack soot in the palm of a resident in Port Harcourt (Brisibe 2017).Figure 3View largeDownload slideBlack soot in the palm of a resident in Port Harcourt (Brisibe 2017). Close modal
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+Additives and Unknown Chemicals
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+Multiple chemicals known as additives are used in the oil and gas industry. These chemicals aid the exploration, production, transport and processing of petrochemicals. Without the application of chemical additives, the production of oil and gas will be economically difficult. Each stage in the crude oil lifecycle requires a unique set of chemicals that will aid and facilitates the various operations in the oil and gas industry. The most common chemicals and additives include scale inhibitors, emulsion breakers, drag reducers, antifoam agents, polyelectrolytes, corrosion inhibitors, trimethylene glycol (gas dryers) and biocides (Devold, 2013). For example, in a production of 40,000 barrels of oil, about 2,000 litres (500 gallons) of antifoam can be used (Devold 2013). Similar volumes of other additives are also applied in the various stages of oil production. Without these essential chemicals, the production of petroleum and petrochemicals needed for powering modern society will be practically impossible. In the daily usage of these chemicals, both oil workers, local community members and the environment are exposed to a considerable quantity of the chemicals over time. Although federal regulators and oil operators outline policy and action to ensure the use of safe and approved chemicals to guarantee public and environmental health, the lack of adequate monitoring in the sector could result in sharp practices by oil operators and their contractors. Community members have voiced concern over the years that operators and their contractors apply chemicals that are not registered or approved by the regulators in the industry. This claim is difficult to verify or disapproved due to poor regulation and lack of transparency in the oil and gas industry, especially in environmental issues. Community members in Ogulagha attributed the massive fish kills in 2021 to the release of toxic chemicals mixed with sludge by SPDC. This claim was never investigated by regulators.
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+Firefighting equipment produced with various flame retardants are used extensively in controlling fire outbreaks in the industry. Research has shown that several brands of flame retardants used in controlling fire outbreaks contain persistent and emerging contaminants with profound health implications (Laitinen et al. 2014). The use of these equipment complicates the chemical burden in the environment, creating a cascade effect on soil and aquatic biota with potential effects on public health. In many incidents within the region, investigations usually focus on aromatics in petrochemicals and a few impurities like heavy metals. Although aromatics make up the large components of crude oil that are of public health concern, other petrochemical compounds like olefins, synthetic gases, naturally occurring radioactive materials and emerging contaminants should also be included in chemical investigations.
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+Developing A Chemical Database
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+The long history of conflicts related to chemical management in the oil and gas industry made the case for the development of a chemical database to aid the investigation of environmental, health and other socio-economic issues in the industry necessary and relevant. The development of a chemical database would require commitment from all stakeholders to create the appropriate legal, regulatory and institutional framework for the effective implementation and execution of such a programme.
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+Legal and Regulatory Framework
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+There is a need for the provision of a legal and regulatory framework to support the infrastructure and management of the proposed chemical database for it to be successful. The Nigerian Upstream Petroleum Regulatory Commission (NUPRC) formerly the Department of Petroleum Resources and NOSDRA have regulations related to chemical registration, testing and use in the industry, but there is no policy, regulation or legislation supporting the establishment and functioning of a chemical database to support chemical investigation openly and transparently. Although crude oil assay is submitted to NUPRC during oil well development, NOSDRA, state regulators and other stakeholders do not have access to such information. So, the first step in the proposition and establishment of a chemical database would be to introduce a robust legal and regulatory framework for its existence.
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+The Nigerian government recently just succeeded in updating the archaic legislation in the oil and gas industry recently. The new law can be amended to create a section for the chemical database management. The NOSDRA Act can also be amended to cater for the chemical programme. Stakeholders in the sector can also propose a new bill to the national assembly for the establishment of a chemical database to complement existing legislation and regulations in the sector. The new law or amendment will cover the need for the chemical database, the infrastructure, the institutional and management structure, function and procedure needed for the use and maintenance of the database. It will also provide provisions for the funding and the smooth running of the new department or agency of government.
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+Institutional Framework
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+There is a need to establish a multi-stakeholder institutional framework codified in the legal system for the establishment of a chemical database for the industry. The institution or agency that will develop, manage and maintain the chemical database should have representatives of all interested stakeholders at the management level and competent and skilled manpower at the technical level. The agency or institution should have qualified staff with hands-on knowledge of analytical and environmental chemistry, petroleum engineers, ecologists and laboratory technicians competent to handle the various aspect of machines, equipment and chemicals in various environmental matrices for the effective running of the agency. Recruitment into the agency should be based solely on a merit system and sound knowledge of what is required to handle the task in the agency rather than federal character, quota system and nepotism. There should be an independent funding instrument in the legal framework for the agency including remuneration of staff to discourage undue advantage from stakeholders and provide a level playing field for the agency to succeed. The critical role and job of the agency require trust and confidence in the fact that investigations conducted and report released should be respected by all parties concerned. Like the Clean Network Associates which was established by multinationals to combat oil spills in the region, the new agency should be created as an autonomous institution to provide sound, independent and reliable investigation, analysis and report on environmental and public health issues associated with the petrochemical industry.
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+Database Development
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+The key components in the development of the chemical database are discussed below;
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+Petrochemical Profiling - The first step in building the chemical database is to develop a comprehensive list of chemical contaminants in the oil and gas industry. There are broadly two categories of chemicals in the industry. They include petrogenic chemicals and synthetic or market chemicals. Petrogenic are natural chemicals associated with petroleum, while synthetic chemicals are chemicals purchased for use in the oil industry. Petrogenic chemicals include petroleum, inorganics like metals associated with petroleum, NORMs and gases. Petroleum is a complex mixture of many organic compounds consisting of aromatic and aliphatic hydrocarbons. There are no exhaustive protocols for the analysis of the exhaustive complex list of compounds found in crude oil, so it is very important to select and profile the essential compounds of environmental and health concern with unique markers for the identification and classification of petrogenics based on their origin and locality. There are already existing analytical protocols for the many synthetic and emerging chemicals used in the industry while efforts are ongoing to develop fast and more reliable protocols for emerging contaminants.
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+Existing Database - Existing regulations in the industry require oil operators to collect and submit vital information including crude oil fingerprints for existing wells to NUPRC. Unfortunately, this information only exists with NUPRC and the oil operators. NOSDRA, the main federal regulator for oil spill management does not have access to the data which could be essential in determining chemical pollution especially mystery spills in the region. This existing information on oilfields should be updated and transferred to the new chemical database under the new agency for efficient delivery of the mandate of the new institution.
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+Target and Non-Target Analysis - The creation of a chemical database will require not just conventional knowledge of chemical analysis, but advanced techniques in analytical chemistry. In recent times, the application of conventional analysis for target analytes has not provided reliable and satisfactory outcomes in the sector. Preliminary investigations that are solely focused on heavy metals and hydrocarbon analysis with scanty analysis of NORMS are no longer providing the answers to issues of concern, especially to communities where oil is extracted. During chemical incidents such as oil spills, mystery spills or massive fish kills regulators and oil operators run hydrocarbon and heavy metals analyses using reference materials to investigate the source of the incident. This approach known as target screening is useful when looking for a shortlist of predetermined organic compounds while ignoring other compounds that could be of serious environmental and health concern. The wide range of chemicals especially new and emerging chemicals used in the oil industry requires more than traditional knowledge of chemical analysis, to investigate and proffer solutions to chemical-related issues in the petrochemical industry. A non-target analysis (NTA) is essential to investigate the presence of all the organic compounds within a sample for the discovery of new or emerging contaminants, metabolites or biomarkers in a sample (Llorca and Rodríguez-Mozaz 2013; Cavanna et al. 2018). NTA analysis can be applied as a standalone method for chemical analysis or used as a complementary approach where target analysis is insufficient to provide further insights for critical investigations (Llorca and Rodríguez-Mozaz 2013; Cavanna et al. 2018). The advancement in analytical tools such as high-resolution mass spectrometry (HRMS) with benchtop instruments like Time-of-Flight (ToF) and Orbitrap with high sensitivity, high resolving power and accurate mass measurement is increasing the frontiers in organic chemical analysis for a better understanding of chemical exposome (Cavanna et al. 2018). Resolving critical questions in recent times on chemical safety in the global petrochemical industry now requires the combination of conventional and new techniques and tools in analytical chemistry. The Nigerian oil and gas industry should not be left behind in proffering answers to stakeholders where conventional analytical methods are no longer reliable.
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+Access, Operation and Maintenance
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+The new agency charged with the management of the chemical database, including access, security and maintenance, should be able to provide access to the database to all stakeholders in the sector when a dispute arises regarding the authenticity of results. There should be a clearly defined process for accessing the database by third parties, especially for criminal, educational, or research purposes. Access by law enforcement, academic and research scholars should be available and the process should be well defined to improve the development of the sector in criminal and legal matters, research and development for the industry. The level of access, security and maintenance by third parties should be well defined before choosing the digital platform or server on which the database will be built.
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+Conclusion
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+The Nigerian oil and gas industry is marred by conflict concerning chemical management, especially for public and environmental health issues. As more uncertainty began to emerge with regulatory investigations on the analysis of environmental samples for chemical safety, public health and environmental management there is a need to provide new tools and techniques to provide answers to critical questions that are beyond the boundary of conventional chemical analysis. As more chemicals are developed and used in the petrochemical industry, conventional analytical techniques will be inadequate to provide the relevant knowledge and insight to protect human health and the environment. The creation of a new agency with the sole mandate to develop, operate and manage a chemical safety database is essential in the petrochemical industry as we move into the future.
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+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
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+References
+
+
+Akam, J. (2020). Shell accused of discharging toxic chemicals, killing fishes in Niger Delta coastline, Daily Post, April26, 2020. https://dailypost.ng/2020/04/26/shell-accused-of-discharging-toxic-chemicals-killing-fishes-in-niger-delta-coastline/Google Scholar AkpanU (2020). Oil Spill: SPDC rejects Angiama 45,000 barrel report as NOSDRA admits error, Vanguard Nigeria, September, 8, 2020https://www.vanguardngr.com/2020/09/oil-spill-spdc-rejects-angiama-45000-barrel-report-as-nosdra-admits-error/Google Scholar Ana, G.R. (2011). Air Pollution in the Niger Delta Area: Scope, Challenges and Remedies, Intech Open, September26, 2011, DOI: 10.5772/16817https://www.intechopen.com/chapters/18639Google Scholar BBC (2019). Brazil oil spill: Where has it come from?, BBC News, 1November2019https://www.bbc.com/news/world-latin-america-50223106BrisibeP. (2017)’Strange black soot’ blankets Nigeria's oil hub, FEBRUARY14, 2017https://phys.org/news/2017-02-strange-black-soot-blankets-nigeria.htmlGoogle Scholar Cavanna, D, Righetti, L., Elliott, C. and Suman, M (2018). The scientific challenges in moving from targeted to non-targeted mass spectrometric methods for food fraud analysis: A proposed validation workflow to bring about a harmonized approach, Trends in Food Science & Technology, 80: 223–241, https://doi.org/10.1016/j.tifs.2018.08.007Google ScholarCrossrefSearch ADS  Duncombe, J. (2019), Brazil's oil spill is a mystery, so scientists try oil forensics, Eos, 100, https://doi.org/10.1029/2019EO136177Google Scholar EkperusiOA and EkperusiOH (2021). 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PMID 25447453.Google ScholarCrossrefSearch ADS PubMed Chinwo, E (2020). Thousands of Dead Fish Washed up on Rivers Shoreline, ThisDay Newspaper, April17, 2020https://www.thisdaylive.com/index.php/2020/04/17/thousands-of-dead-fish-washed-up-on-rivers-shoreline/Google Scholar DachenI. (2022). Blame govs for failure to control black soot, Buhari's aide absolves Nigerian govt, Ripples Nigeria, February1, 2022https://www.ripplesnigeria.com/blame-govs-for-failure-to-control-black-soot-buharis-aide-absolves-nigerian-govt/Google Scholar DevoldH (2013). An introduction to oil and gas production, transport, refining and petrochemical industry: Oil and gas production handbook, Edition 3.0, ISBN 978-82-997886-3-2 Oslo, August2013, pp 162Google Scholar Llorca, M.; Rodríguez-Mozaz, S. (June2013). State-of-the-art of screening methods for the rapid identification of chemicals in drinking water. European Commission, Joint Research Centre. doi:10.2788/80087. ISBN 9789279382949.Google Scholar MacleanR, Ezeamalu, B and Asiayei, E (2021). The Fisherwomen, Chevron and the Leaking Pipe, New York Times, November, 2, 2021https://www.nytimes.com/2021/07/25/world/africa/nigeria-fisherwomen-chevron.htmlGoogle Scholar Nwakamma, N (2020). Shell denies being responsible for dead fishes on Niger Delta coastline, Environ News Nigeria,May5, 2020https://www.environewsnigeria.com/shell-denies-being-responsibile-for-dead-fishes-on-niger-delta-coastline/Google Scholar NOSDRA (2022). Oil spill monitoring data, National Oil Spill Detection and Response Agency, Nigeriahttps://nosdra.oilspillmonitor.ng/Oduware, S. (2020). Environmentalists Reject NOSDRA's Report on Dead Fish Along Niger Delta Coastlines, HOMEF,May20, 2020https://homef.org/2020/05/20/environmentalists-reject-nosdras-report-on-dead-fish-along-niger-delta-coastlines/Google Scholar Oluwarore, K, AkpanS and Oladunjoye, I (2020). Dead Fishes on Nigerian Shores – A Public Health Emergency, One Health Development Institute,May6, 2020https://onehealthdev.org/author/ohdi/Google Scholar Oyadongha, S (2013). Chevron, Bayelsa communities disagree on gas leakage, Vangaurd Nigeria, August20, 2013https://www.vanguardngr.com/2013/08/chevron-bayelsa-communities-disagree-on-gas-leakage/Google Scholar Oyadongha, S (2021). Chevron, host community differ over oil spill at Funiwa oilfield in Bayelsa, Vanguard Nigeria, January19, 2021https://www.vanguardngr.com/2021/01/chevron-host-community-differ-over-oil-spill-at-funiwa-oilfield-in-bayelsa/Google Scholar Oyadongha, S. (2020). NOSDRA intervenes again in SPDC/Bayelsa community oil spill face-off, Vanguard Nigeria, October28, 2020https://www.vanguardngr.com/2020/10/nosdra-intervenes-again-in-spdc-bayelsa-community-oil-spill-face-off/Google Scholar Oyadongha, S. (2021) Bayelsa spill not from our operations - Chevron, Vanguard Nigeria, January22, 2021https://www.vanguardngr.com/2021/01/bayelsa-spill-not-from-our-operations-%e2%80%95-chevron/Google Scholar Premium Times (2020). Why dead fishes were found on Niger Delta coastline -NOSDRA, Premium Times, May14, 2020https://www.premiumtimesng.com/regional/south-south-regional/392742-why-dead-fishes-were-found-on-niger-delta-coastline-nosdra.htmlRim-Rukeh, A. (2015). Oil spill management in Nigeria: SWOT analysis of the joint investigation visit (JIV) process, J. Environ. Protect. 6: 259–271 DOI: 10.4236/jep.2015.63026Google ScholarCrossrefSearch ADS  Salem, T (2020). Rep calls for probe of dead fishes in Niger Delta, Vanguard Nigeria, April29, 2020https://www.vanguardngr.com/2020/04/rep-calls-for-probe-of-dead-fishes-in-niger-delta/Google Scholar Sobus, J.R., Wambaugh, J.F.et al. . Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA. J Expo Sci Environ Epidemiol28, 411–426(2018). https://doi.org/10.1038/s41370-017-0012-yCrossrefSearch ADS PubMed USGS (2022). What causes fish kills?United State Geological Surveyhttps://www.usgs.gov/special-topic/water-science-school/science/water-qa-what-causes-fish-kills?ZendongSZ, KadiriM, HerrenknechtC, NézanE, MazzeoA. and HessP. (2016). Algal toxin profiles in Nigerian coastal waters (Gulf of Guinea) using passive sampling and liquid chromatography coupled to mass spectrometry, Toxicon114: 16–27, doi:10.1016/j.toxicon.2016.02.011Google ScholarCrossrefSearch ADS PubMed 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211948-MS
+
+
+

files/2022/Developing a Model for Effective Cutting Transport Mechanism.txt

@@ -0,0 +1,1063 @@
+----- METADATA START -----
+Title: Developing a Model for Effective Cutting Transport Mechanism
+Authors: Oghenetega Shadrack David
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211957-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The transportation of cuttings and wellbore stability are indispensable in any drilling program. This is because a successful drilling operation is the key to a profitable business in the oil and gas sector, efficiency of cuttings transport is very important for a good drilling program, the transportation of these cuttings through the annulus is a complex problem that is affected by many parameters.The objective is to design drilling mud for efficient cutting transport in different holes sections and develop a model that is more effective in lifting cuttings through the annulus without affecting the stability of the wellbore.Cutting transport efficiency in vertical and deviated wellbore has been reported to depend on the following factors; hole geometry and inclination, average fluid velocity, flow regime, drill pipe rotation, pipe eccentricity, fluid properties and rheology, cuttings size and shape, cuttings transport velocity, slip velocity, etc., the accumulation of drilling cuttings in the wellbore causes several drilling problems. These includes an increase in torque and drag, which may limit drilling from reaching to a desired target formation, the analysis is carried-out using different cutting transport models like; larsians model, rubiandini's model, moore's model, Hopkins method and zeidler's slip velocity correlation.This paper work is aimed at achieving a cutting transport model (I,e developing a model), that is more effective in lifting the drilled cuttings from the wellbore through the annulus to the surface after considering different existing cutting transport models and it is required that parameters affecting the efficient cutting transport are considered simultaneously.
+
+
+
+
+Keywords:
+drilling fluids and materials,
+production monitoring,
+crit 1,
+drilling operation,
+cuttings,
+well planning,
+reservoir surveillance,
+mw 1 0,
+ang 0,
+upstream oil & gas
+
+
+Subjects: 
+Well Planning,
+Drilling Operations,
+Drilling Fluids and Materials,
+Well & Reservoir Surveillance and Monitoring,
+Production logging
+
+
+
+
+Introduction
+
+
+Transportation of cuttings is a mechanism that is a vital factor for a good drilling program. In directional and horizontal drilling, hole cleaning is a common and costly problem. Ineffective removal of cuttings can result in several problems, such as bit wear, slow drilling rate, increased ECD (which can lead to formation fracturing), high torque, drag, and in the worst case, the drill pipe can be stuck. If this type of situation is not handled properly, the problem can escalate to side tracking or loss of well, at worst.
+
+
+Cuttings transport is controlled by many variables such as well inclination angle, hole and drill-pipe diameter, rotation speed of drill pipe (RPM), drill-pipe eccentricity, rate of penetration (ROP), cuttings characteristics like cuttings size and porosity of bed and drilling fluids characteristics like flow rate, fluid velocity, flow regime, mud type and non - Newtonian mud rheology. The key factors for optimizing hole cleaning is a result of good well planning, good drilling fluid properties, and good drilling experience.
+
+
+Methodology
+
+
+The research design chosen for this research work is the descriptive approach. In this descriptive research, coherent inferences will be drawn from series of available data.
+
+
+The data generated will be presented in a tabular form by the researcher and also with the aid of Microsoft Excel. The researcher intends to use a simple comparative analysis to represent the answers to the research questions. In this regard, the different formation configurations, different mud system, and potential damage that can happen during the course of using any of these mud systems will be taken into consideration in selecting the most effective and efficient drilling fluid for cutting transport mechanism.
+
+
+The data was further analyzed comparatively using Microsoft Excel. Microsoft Excel is a spreadsheet-based software tool that employs formulae and functions to arrange numbers and data. Excel analysis is used by businesses of all sizes all around the world to undertake various analysis. In this regard, the wellbore damage due to drilling will be quantified based on cutting transport parameters. The effects of these parameters on the different cutting transport models and the best model to be used in HTHP wells in niger delta.
+
+
+Schematic Form of Some of the Models
+
+
+Figure 1View largeDownload slideLarsen's model in schematic formFigure 1View largeDownload slideLarsen's model in schematic form Close modal
+
+
+Figure 2View largeDownload slideRudi Rubiandini's model in schematic formFigure 2View largeDownload slideRudi Rubiandini's model in schematic form Close modal
+
+
+Figure 3View largeDownload slideMoore's model in schematic formFigure 3View largeDownload slideMoore's model in schematic form Close modal
+
+
+Results and Analysis
+
+
+Parameters for Cutting Transport Mechanism
+
+
+Constant parameters
+
+
+Diameter of pipe (Dpipe) = 5(inches)
+
+
+Diameter of hole (Dhole) = 8.5(inches)
+
+
+Rate of penetration (ROP) = 33(ft/hr)
+
+
+Plastic viscosity (PV) = 7(cp)
+
+
+Yield point (YP) = 7(lbf/100ft2
+
+
+Diameter of cuttings/ cuttings size (Dcut) = 0.3(inches)
+
+
+Mud weight (ρm) = 10.83 (ppg)
+
+
+RPM = 80
+
+
+Cuttings density (ρs) = 19(lbf/gal)
+
+
+Inclinations = 0° to 90°
+
+
+Empirical constant (C) = 40
+
+
+Gravitational acceleration (g) = 9.81
+
+
+Table 1Varying parameters ROP(ft/hr) 33 98.3 164 Mud weight (ρm) 10.83 12.5 15 cuttings size (Dcut) 0.1 0.3 0.6 Mud rheology(YP= PV) 7 10 15 ROP(ft/hr) 33 98.3 164 Mud weight (ρm) 10.83 12.5 15 cuttings size (Dcut) 0.1 0.3 0.6 Mud rheology(YP= PV) 7 10 15 View Large
+
+
+Moore's Model Analysis
+
+
+For constant parameters
+
+
+Vs1 = 39.70539046ft/min
+
+
+Vs1 = 0.6617565077ft/sec
+
+
+Vmin = 1.2846313 + 0.6617565077
+
+
+Vmin = 1.946387808ft/s
+
+
+μa = 99.910113985cp
+
+
+Re = 19.97038059
+
+
+F = 4.922996308
+
+
+Vslip=4.92*0.3*(19−10.83)10.83
+
+
+Vslip = 2.340574828ft/s
+
+
+Using 15
+
+
+Vcut = 1.2846313ft/s
+
+
+Vs1 = 39.04203797ft/min
+
+
+Vs1 = 0.6507006328ft/sec
+
+
+Vmin = 1.2846313 + 0.6507006328
+
+
+Vmin = 1.935331933ft/s
+
+
+μa = 150.6356476cp
+
+
+Re = 13.02420303
+
+
+F = 2213.02420303
+
+
+F = 6.096030089
+
+
+Vslip=6.10*0.3*(19−10.83)10.83
+
+
+Vslip = 2.901932206ft/s
+
+
+Rudi-Rubiadini's Model
+
+
+For constant parameters
+
+
+Using data's from moore's model
+
+
+Vslip = 1.945721758 ft/s
+
+
+If θ≪45°
+
+
+Vmin=Vcut+Vslip[1+θ(600−RPM)*(3+ρm)202500]
+
+
+θ ≫45°
+
+
+Vmin=Vcut+Vslip[1+(600−RPM)*(3+ρm)4500]
+
+
+Considering θ≪ 45°
+
+
+Vmin=Vcut+Vslip[1+θ(600−RPM)*(3+ρm)202500]
+
+
+At 30°
+
+
+Vmin=1.2846313+1.945721758[1+30(600−80)*(3+10.83)202500]
+
+
+Vmin = 5.303368257ft/s
+
+
+At 15°
+
+
+Vmin=1.2846313+1.945721758[1+15(600−80)*(3+10.83)202500]
+
+
+Vmin = 4.266860658ft/s
+
+
+considering θ≫45°
+
+
+Vmin=1.2846313+1.945721758[1+(600−RPM)*(3+10.83)4500]
+
+
+Vmin=1.2846313+1.945721758[1+(600−80)*(3+10.83)4500]
+
+
+Vmin = 6.339875857ft/s
+
+
+For varying parameters
+
+
+First case
+
+
+ROP(ft/hr) = 33
+
+
+Mud weight(ppg) = 10.83
+
+
+Cuttings size(inch) = 0.1
+
+
+Mud rheology(PV=YP) = 7
+
+
+Vslip = 2.916898497ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 7.309258276ft/s
+
+
+For 30°
+
+
+Vmin = 5.7553594036ft/s
+
+
+Second case
+
+
+ROP(ft/hr) = 98.3, = ρm (ppg) = 12.5,Dcut = 0.3, YP = PV = 10.
+
+
+Vslip = 1.808955057 ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.823019556ft/s
+
+
+For 15°
+
+
+Vmin = 4.743006388ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.903032723ft/s
+
+
+Third case
+
+
+ROP = 164, ρm (ppg) = 15, Dcut = 0.6,YP = PV = 15.
+
+
+Vslip = 1.38ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.33041357ft/s
+
+
+For 15°
+
+
+Vmin = 4.37361357t/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.28721357ft/s
+
+
+Considering Varying Individual Parameters
+
+
+For ROP, using 33ft/hr
+
+
+Vslip = 1.945721758 ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.303368257ft/s
+
+
+For 15°
+
+
+Vmin = 4.266860658ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.339875857ft/s
+
+
+Using, 98.3(ft/hr)
+
+
+Vslip = 1.403393444ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 4.75263817ft/s
+
+
+For 15°
+
+
+Vmin = 4.005034889ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 5.500241451ft/s
+
+
+Using 164ft/hr
+
+
+Vslip = 1.446208837ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.02384544ft/s
+
+
+For 15°
+
+
+Vmin = 4.253433923ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 5.794256956ft/s
+
+
+For mud weight (ppg)
+
+
+Using 10.83
+
+
+Vslip = 1.945721758 ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.303368257ft/s
+
+
+For 15°
+
+
+Vmin = 4.266860658ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.339875857ft/s If θ≪45°
+
+
+Using 12.5
+
+
+Vslip = 1.698363919ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.010967543ft/s
+
+
+For 15°
+
+
+Vmin = 3.996981381ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.024953705ft/s
+
+
+Using 15ppg
+
+
+Vslip = 1.368958728ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 4.551879464ft/s
+
+
+For 15°
+
+
+Vmin = 3.602734746ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 5.501024182ft/s
+
+
+For cuttings size (inches)
+
+
+Using 0.1
+
+
+Vslip = 2.916898497ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 7.309258276ft/s
+
+
+For 15°
+
+
+Vmin = 5.755394036ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 8.863122515ft/s
+
+
+Using 0.3
+
+
+Vslip = 1.945721758 ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.303368257ft/s
+
+
+For 15°
+
+
+Vmin = 4.266860658ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.339875857ft/s
+
+
+Using 0.6
+
+
+Vslip = 1.527208323ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 4.438961308ft/s
+
+
+For 15°
+
+
+Vmin = 3.625400466ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 5.252522151 ft/s
+
+
+For mud rheology (PV=YP)
+
+
+Using 7
+
+
+Vslip = 1.945721758 ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 5.303368257ft/s
+
+
+For 15°
+
+
+Vmin = 4.266860658ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 6.339875857 ft/s
+
+
+Using 10
+
+
+Vslip = 2.340574828ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 6.118906563ft/s
+
+
+For 15°
+
+
+Vmin = 4.872056345ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 7.365756678 ft/s
+
+
+Using 15
+
+
+Vslip = 2.901932206ft/s
+
+
+If θ≪45°
+
+
+For 30°
+
+
+Vmin = 7.278346566ft/s
+
+
+For 15°
+
+
+Vmin = 5.732455036ft/s
+
+
+If θ ≫45°
+
+
+Vmin = 8.824238095 ft/s
+
+
+Larsen's Model
+
+
+For constant parameters
+
+
+μa = 69.72033487 cp
+
+
+Since μa > 53 cp
+
+
+∇slip = 0.02554*(μa−53)+3.28
+
+
+∇slip = 0.02554*(69.72033487−53) + 3.28
+
+
+∇slip = 3.707037353ft/s
+
+
+Considering different angles θ
+
+
+Say, 30°, 45°, and 60°
+
+
+Cang =0.0342(θ) −0.000233(θ)2 −0.213
+
+
+For 30°
+
+
+Cang =0.0342(30) −0.000233(30)2−0.213
+
+
+Cang = 0.6033
+
+
+Csize = 0.3
+
+
+Cmw = 1 − 0.0333(ρm−8.7)
+
+
+Since ρm > 8.7
+
+
+ρm = 10.83
+
+
+Cmw = 1 − 0.0333(10.83-8.7)
+
+
+Cmw = 0.929071
+
+
+Vslip = ∇slip * Cang * Csize *Cmw
+
+
+Vslip = 3.707037353 * 0.6033 * 0.3*0.929071
+
+
+Vslip = 0.623347822ft/s
+
+
+Vmin = Vcrit = Vcut + Vslip
+
+
+Vmin = Vcrit = 1.2846313+ 0.623347822
+
+
+Vmin = Vcrit = 1.907979122ft/s
+
+
+For 45°
+
+
+Cang = 0.0342(45) −0.000233(45)2 −0.213
+
+
+Cang = 0.854175
+
+
+Csize = 0.3
+
+
+Cmw = 1 − 0.0333(ρm−8.7)
+
+
+Since ρm > 8.7
+
+
+ρm = 10.83
+
+
+Cmw = 1 - 0.0333(10.83−8.7)
+
+
+Cmw = 0.929071
+
+
+Vslip = ∇slip * Cang * Csize*Cmw
+
+
+Vslip = 3.707037353*0.854175* 0.3*0.929071
+
+
+Vslip = 0.882559466ft/s
+
+
+Vmin = Vcrit = Vcut + Vslip
+
+
+Vmin = Vcrit = 1.2846313+ 0.882559466
+
+
+Vmin = Vcrit = 2.167190766ft/s
+
+
+For 60°
+
+
+Cang = 0.0342(60)−0.000233(60)2−0.213
+
+
+Cang = 1.0002
+
+
+Csize = 0.3
+
+
+Cmw = 1 −0.0333(ρm−8.7)
+
+
+Since ρm > 8.7
+
+
+ρm = 10.83
+
+
+Cmw = 1 − 0.0333(10.83−8.7)
+
+
+Cmw = 0.929071
+
+
+Vslip = ∇slip * Cang * Csize*Cmw
+
+
+Vslip = 3.707037353*1.0002* 0.3*0.929071
+
+
+Vslip = 1.033436916ft/s
+
+
+Vmin = Vcrit = Vcut + Vslip
+
+
+Vmin = Vcrit = 1.2846313+ 1.033436916
+
+
+Vmin = Vcrit = 2.318068216ft/s
+
+
+For varying parameters
+
+
+First case
+
+
+ROP = 33, ρm (ppg) = 10.83,Dcut = 0.1, YP = PV = 7
+
+
+μa = 81.95631257cp
+
+
+Since μa > 53 cp
+
+
+∇slip = 0.02554*(μa−53)+3.28
+
+
+∇slip = 0.02554*(81.95631257−53) + 3.28
+
+
+∇slip = 4.019544223ft/s
+
+
+Considering different angles θ
+
+
+Say, 30°, 45°, and 60°
+
+
+Cang = 0.0342(θ) −0.000233(θ)2 −0.213
+
+
+For 30°
+
+
+Cang = 0.0342(30)−0.000233(30)2−0.213
+
+
+Cang = 0.6033
+
+
+Csize = 0.1
+
+
+Cmw = 1 − 0.0333(ρm−8.7)
+
+
+Since ρm > 8.7
+
+
+ρm = 10.83
+
+
+Cmw = 1 - 0.0333(10.83-8.7)
+
+
+Cmw = 0.929071
+
+
+Vslip = ∇slip * Cang * Csize*Cmw
+
+
+Vslip = 4.019544223*0.6033* 0.1*0.929071
+
+
+Vslip = 0.2252988841ft/s
+
+
+Vmin = Vcrit = Vcut + Vslip
+
+
+Vmin = Vcrit = 1.2846313+ 0.2252988841
+
+
+Vmin = Vcrit = 1.509930184ft/s
+
+
+For 45°
+
+
+Cang = 0.0342(45) −0.000233(45)2−0.213
+
+
+Cang = 0.854175
+
+
+Csize = 0.1
+
+
+Cmw = 1 − 0.0333(ρm−8.7)
+
+
+Since ρm > 8.7
+
+
+ρm = 10.83
+
+
+Cmw = 1 − 0.0333(10.83−8.7)
+
+
+Cmw = 0.929071
+
+
+Vslip = ∇slip * Cang * Csize*Cmw
+
+
+Vslip = 4.019544223*0.854175* 0.1*0.929071
+
+
+Vslip = 0.318986697ft/s
+
+
+Vmin = Vcrit = Vcut + Vslip
+
+
+Vmin = Vcrit = 1.2846313+ 0.318986697
+
+
+Vmin = Vcrit = 1.603617997ft/s
+
+
+Zeidler's Slip Velocity Correlation Model
+
+
+For constant parameters
+
+
+Vcut = 1.2846313ft/s
+
+
+Vs1 = 0.6684832721ft/s
+
+
+Vmin = 1.953114572ft/s
+
+
+μa=PV+5YP(Dhole−Dpipe)Vmin
+
+
+μa=7+5(7)(8.5−5)1.953114572
+
+
+μa = 69.72033487cp
+
+
+NRE=928*ρm*Dcut*Vs1μa
+
+
+NRE = 28.90871365
+
+
+IF,
+
+
+2 ≤ NRE,p ≤ 15
+
+
+VS=13.42(ρs−ρl)0.782*deq1.35ρl0.218*μ0.564
+
+
+15 ≤ NRE,p ≤ 80
+
+
+VS=13.88(ρs−ρl)0.612*deq0.836ρl0.388*μ0.224
+
+
+80 ≤ NRE,p ≤ 1500
+
+
+VS=17.88(ρs−ρl)0.516*deq0.548ρl0.48*μ0.032
+
+
+Since, NRE = 28.90871365
+
+
+15 ≤ NRE,p ≤ 80
+
+
+VS=13.88(19−10.83)0.612*0.30.83610.830.388*69.720334870.224
+
+
+Vslip = 2.813159004ft/s
+
+
+Discussion
+
+
+In general the hole-cleaning becomes worse as well inclination increases from vertical to horizontal. Increasing flow rates can improve the cuttings-transport performance. As hole- inclination increases from vertical to horizontal, if appropriate flow rate is not used a cuttings-bed development will occur. Especially at inclinations between 40 and 60°, hole- cleaning is most difficult because of back sliding of the cuttings inside the wellbore. for any well inclination and under all operational conditions the higher the flow rate clean the well effectively.
+
+
+Conclusion
+
+
+The results show that the impact of parameters depends on various combinations parameters.
+
+
+Increasing viscosity, density of fluid requires an increase flow rateIncreasing ROP requires an increase flow rateIncreasing well size requires an increase flow rateIncreasing cutting density requires an increase flow rateIncreasing cutting size requires an increase flow rateIncreasing RPM reduces the flow rate requiredIncreasing mud weight reduces the flow rate required
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+T.Nazari and G.Hareland, University of Calgary, and J.J.Azar, University of Tulsa //Review of Cuttings Transport in Directional Well Drilling: Systematic Approach// SPE 132372, SPE Western Regional Meeting, 27-29 May 2010, Anaheim, California, USAInge F.Larsen, //A study of the critical fluid velocity in cuttings transport for inclined wellbores// MSc thesis, 1990Google Scholar Larsen, T.I., Pilehvari, A.A., and Azar, J.J.: "Development of a New Cuttings-Transport Model for High-Angle Wellbores Including Horizontal Wells," paper SPE 25872 presented at the 1993 SPE Annual Technical Conference and Exhibition, Denver, April 12–14.Rudi Rubiandini, R.S.: "Equation for Estimating Mud Minimum Rate for Cuttings Transport in an Inclined-Until-Horizontal Well", paper SPE/IADC 57541 presented at the 1999 SPE Annual Technical Conference and Exhibition, Abu Dhabi, November 8–10.
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211957-MS
+
+
+

files/2022/Digitalization of Old Generation Equipment for Field Operations Optimization.txt

@@ -0,0 +1,194 @@
+----- METADATA START -----
+Title: Digitalization of Old Generation Equipment for Field Operations Optimization
+Authors: Eriagbaraoluwa Adesina, Bukola Olusola
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211944-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Equipment such as generators and export pumps, among others are crucial in the continuous running of oil and gas operations in the oil field. However, this equipment can fail without prior notice leading to costly downtime, therefore; it is paramount to minimize equipment failure. The unpredictability of equipment failure leads to the repair time being prolonged due to difficulty in scoping and procuring the damaged parts. To solve this problem, we proposed the use of digital control systems and equipment telematics to be installed on analog generation units for data analytics and business efficiency.In the proposed data acquisition strategy, the control systems interpret analog inputs, convert them to digital data, continuously monitor the data, and upload the data to a cloud database for seamless data transfer to data analyst. This data was used in evaluating the performance of the generators, identifying parameters that largely affects the efficiency of the analog machine and then the results were used in the optimization of field operations. Telematics devices convert analog readings such as current, voltage and gauges into digital data, send out event activity reports and receive commands can be installed in old analog equipment. For instance, fuel level sensors are placed in fuel tanks of generators to detect the volume as well as the rate at which the tanks are getting filled up and drained. The sensors also measure fuel temperature and quality. Fuel flow meters directly measure the engine fuel consumption, engine operation time and the fuel rate in the supply line from the storage tank to the generation equipment. In addition, a contactless reader reads and transmits engine parameters of the equipment - the Revolutions per Minute (RPM) of the machine, oil pressure, oil temperature, coolant temperature and other engine parameters to the cloud database storage.The data collated is analysed with statistical methods and data analytical techniques. The engine parameters are weighted and used to determine the performance and the health level of the engine with all parameters being measured in real time. Based on the simulated training dataset and its respective results for each entry, generators can be predicted to be "healthy" or about to be faulty in real time. With this approach, old generation equipment and power output can be constantly monitored and connected to an event detection system.This paper presents a way to digitalise old generation equipment, prevent power outages and prolong the life cycle of generation equipment in oil fields. Finally, the methods presented in this paper can be extended to any analog or old generation equipment requiring performance monitoring.
+
+
+
+
+Keywords:
+big data,
+machine learning,
+old generation equipment,
+artificial intelligence,
+dataset,
+upstream oil & gas,
+data mining,
+generator,
+accuracy,
+digitaliztion
+
+
+Subjects: 
+Information Management and Systems,
+Data mining,
+Artificial intelligence
+
+
+
+
+Introduction
+
+
+In the industry, equipment such as generators and export pumps are crucial to the smooth running of any operation and periodically develop faults leading to downtime and a costly loss of revenue. Most operations in Nigeria use analog monitoring devices in these equipment which is limited in their capability to only make readings. This paper proposes an inexpensive method of digitalising analog monitoring equipment and using the resulting aquired data to aid in predictive analytics, limiting equipment downtime to the barest minimum, increasing efficiency, optimising the entire production process and maximising profit.
+
+
+Breakdown is a gradual degradation of a machine or an equipment. This unpredictable continuous degradation leads to a rise in operating cost, unstable delivery time and decreased profit. Even through a rigorous and strict adherence to maintenance related checklists it is increadibly difficult to manage to have zero downtime as a number of features important to the cause of breakdown are not monitored accurately enough. Breakdown is caused by poor conditions of the equipment, poor maintenance, operating error and design error.
+
+
+The fourth industrial revolution, otherwise known as Industry 4.0, facilitates greater production efficiency, and significantly impacts economic, environmental, and social sustainability (Sreenivasan Jayashree et al., 2022). Industry 4.0 is a technology-driven digital transformation to enable data-driven decision-making based on real-time data to enhance the competitiveness of traditional manufacturing. Moving forward, adopting Industry 4.0 is an evident requirement for manufacturers to remain competitive (Shreyanshu Parhi et al., 2022).
+
+
+It consists of emerging technologies like Internet of Things, wireless sensor networks, big data, cloud computing, and embedded systems in the manufacturing environment (Wang et al., 2016; Ferreiro et al., 2016). The failure of machine parts may lead to breakdown, defects or accidents which causes huge cost and delay in product delivery. According to Ferreiro et al., 2016, the total revenue loss due to breakdown throughout a year in the world is approximately US$ 450 billion. Investing in and using Industry 4.0 reduces idle machines, idle manpower, human input and by extension human error.
+
+
+Predictive diagnostics along with the aid of telematics devices and sensors can aid in reducing unexpected machine downtime, reduce the repair time and find faulty systems and components before failure occurs. Predictive analytics can help point out potential problems before a point is reached where it results in a downtime event.
+
+
+Data collection would occur through sensors and equipment telematics devices. The word "Telematics" is gotten from the amalgamation of the words Telecommunication and Data Processing. It is the blending of machines and remote communication to clearly give information over enormous systems. Data gathered by the telematics device is sent in a digital package to a server after which the data gets decoded. This information is uploaded into the cloud, becomes accessible from anywhere and is used for analytical purposes. These systems greatly increase efficiency and cut down on errors brought about by human hands (Priyabrata Pattanaik et al., 2021).
+
+
+Data analysis is performed on the stored data and from the data, performance and event warning dashboards are created creating an easy way to view performance of equipment from anywhere and be immediately alerted to any problems. As shown in the Figure - 1,2, and 3 below, the dashboards show information about the engine performance, power output, fuel monitoring and event detection.
+
+
+Figure 1View largeDownload slideDashboard showing engine performance and health check features. Key metrics like Fuel volume, Fuel Rate, RPM, Battery, Oil Pressure, Coolant, Fuel temperature, Engine runtime are being displayed.Figure 1View largeDownload slideDashboard showing engine performance and health check features. Key metrics like Fuel volume, Fuel Rate, RPM, Battery, Oil Pressure, Coolant, Fuel temperature, Engine runtime are being displayed. Close modal
+
+
+Figure 2View largeDownload slideDashboard showing equipment power output. Total power, relative power output, power factor, total fuel consumption, RPM, Fuel rate are the metrics on display.Figure 2View largeDownload slideDashboard showing equipment power output. Total power, relative power output, power factor, total fuel consumption, RPM, Fuel rate are the metrics on display. Close modal
+
+
+Figure 3View largeDownload slideDashboard monitoring fuel consumption. Display of fuel consumption over time preventing fuel cheating.Figure 3View largeDownload slideDashboard monitoring fuel consumption. Display of fuel consumption over time preventing fuel cheating. Close modal
+
+
+Figure 4View largeDownload slideSetup for telematic devices. Fuel level sensors in main tank, fuel flow meters, contactless gateways, wifi, telematics gateway.Figure 4View largeDownload slideSetup for telematic devices. Fuel level sensors in main tank, fuel flow meters, contactless gateways, wifi, telematics gateway. Close modal
+
+
+Figure 5View largeDownload slideFuel Flow Meter. For direct fuel consumption measurement inside fuel lines of diesel engines and other equipment.Figure 5View largeDownload slideFuel Flow Meter. For direct fuel consumption measurement inside fuel lines of diesel engines and other equipment. Close modal
+
+
+APPLICATION OF MACHINE LEARNING
+
+
+Predicting faults in analog equipment is a difficult obstacle due to the complexity of the features and parameters needed to be analysed to reach a consensus. Refined methods such as machine learnine (decision trees, random forest algorithm and neural networks) can sort through the complex variable interactions and provide accurate results. Advanced machine learning techniques like clustering algorithms and random forest algorithm understand production behaviours and correlation and make near accurate predictions using dominant production attributes to estimate accurate predictive diagnostic results. Therefore, in this work we had used a random forest algorithm to predict the performance of the equipment. A Random forest algorithm is a supervised machine learning algorithm that is popularly used in both classification and regression problems. It constructs decision trees on various samples and uses their majority vote for classification and mean in case of regression (Sruthi E.R. — 2021).
+
+
+Methods
+
+
+In the proposed data acquisition and analytics strategy, control systems will be installed in analog generation equipment to interpret analog inputs and convert them to digital data. This data will be uploaded via the internet and digital cloud systems to a cloud database. There will be a continuous transfer of data enabling real time analysis and monitoring of equipment performance.
+
+
+Fuel level sensors are to be placed in the fuel tanks to detect the volume as well as the rate at which the tanks are getting filled up and drained. The sensors also measure fuel temperature and quality. Fuel flow meters will also be installed to measure the engine fuel consumption, engine operation time and the fuel rate in the supply line from the storage tank to the generation equipment. Contactless readers are also installed to read and transmit the engine parameters to the cloud database storage.
+
+
+The data being used to undergo this study is the "Diesel Engine Faults Features Dataset" provided by Denys Pestana.
+
+
+The dataset consists of figures simulating diesel engine failures based on a group of algorithms by varying the generator pressure, temperature and fuel volume using the producer's data. The simulation covers the most frequent kinds of faults in diesel engines according to four cases:
+
+
+Normal operationsCompression faultInjected fuel mass faultPressure in the intake manifold fault
+
+
+The simulation data depicting faulty conditions will take the binary value of 1 while normal operating conditions with no fault will take the binary value 0.
+
+
+The database developed by Denys Pestana-Viana et al., 2019 is to mimic all operating conditions, and every possible combination of diesel faults and system variations. The database consists of 84 features and 3500 rows. In this study we will be using a Random Forest classifier to test the system's ability to distinguish between the engine running with a fault (likelihood to fail) and at normal conditions, the algorithm will take the features as input while returning a "0" or "1" to indicate "not faulty" and "faulty" respectively.
+
+
+A Random forest algorithm is a supervised machine learning algorithm that is popularly used in both classification and regression problems. It constructs decision trees on various samples and uses their majority vote for classification and mean in case of regression (Sruthi E.R. — 2021). Figure 6 shows a visual representation of this process.
+
+
+Figure 6View largeDownload slideRandom Forest Algorithm Process Flowchart showcasing how the algorithm works to make predictions.Figure 6View largeDownload slideRandom Forest Algorithm Process Flowchart showcasing how the algorithm works to make predictions. Close modal
+
+
+Figure 7View largeDownload slideFlowchart showing all steps taken in the completion of this project.Figure 7View largeDownload slideFlowchart showing all steps taken in the completion of this project. Close modal
+
+
+Flow Chart
+
+
+A Random Forest Classification based machine learning model was trained to classify diesel generator data as either "faulty" or "not faulty" using the "Diesel Engine Faults Features Dataset" mentioned above. In this project, the input variables are the 84 features with the output being the classification of the data.
+
+
+The total length of the dataset is 3500 rows with 84 features to be used to train the machine learning model.
+
+
+A histogram and box plot are drawn to understand the distribution of the data and to ensure that each feature followed a normal distribution.
+
+
+The data was cleaned, starting with searching for and removing all non-numerical values if present from the dataset. Next, missing values if present are identified and depending on the situation the rows are dropped, or the empty values are replaced with the median for that specific entry. Outliers are identified through observing the created histogram and box plots. Outliers can be treated by either dropping the outlier row in some cases, using statistical flooring and capping techniques to limit the values to an acceptable range or leaving the data as is to avoid interfering with the results. In this project, outliers were left untampered as the data ranges was deemed acceptable.
+
+
+The "Diesel Engine Faults Features Dataset" was randomly split into a training (60%), testing (20%) and validation (20%) dataset. After training the model on the training data, the validation dataset was used to tune the hyperparameters of our machine learning algorithm and increase its accuracy and generalization capabilities before using on the test dataset. Model accuracy was scored on accuracy, recall, precision and f1 score.
+
+
+Accuracy is a measure of the ratio of the correctly predicted entries to the number of total entries. Recall is the ratio of the correctly predicted positive entries to the total number of actual positive entries. Precision is the ratio of the correctly predicted positive entries to the total number of predicted positive entries. F1 score is the weighted average of precision and recall (Data Science Blogathon: Rohit Pant, 2020).
+
+
+Results
+
+
+As the dataset is large, a subset of the data used in the building the classification model will be shown below:
+
+
+As seen in Figure 9 the model overfits slightly on the training data with an accuracy of 0.999, a recall value of 0.999, precision of 1.0 and an F1 score of 0.999. However, the model still generalizes well when fit to the test data showing high scores in all metrics.
+
+
+Figure 8View largeDownload slideSubset of diesel generator data used in the training and the validation of the fault prediction model. All parameters used in the training of the model are present here. Temperature, Pressure and Volume were the primary paraters used in the modelling of the engine simulation.Figure 8View largeDownload slideSubset of diesel generator data used in the training and the validation of the fault prediction model. All parameters used in the training of the model are present here. Temperature, Pressure and Volume were the primary paraters used in the modelling of the engine simulation. Close modal
+
+
+Figure 9View largeDownload slideTable showing the accuracy, recall, precision and F1 score of the model on the training and the test data. Fitting the model to the test data happened after model tuning.Figure 9View largeDownload slideTable showing the accuracy, recall, precision and F1 score of the model on the training and the test data. Fitting the model to the test data happened after model tuning. Close modal
+
+
+An accuracy score, recall score, precision score and F1 score all above 0.90 indicates that the model would be great at correctly classifying future data and correctly differentiating between faulty and non-faulty diesel engines.
+
+
+According to the confusion matrix in Figure 10, when fitting the model to the test data, it correctly classified 654 entries out of 700. 3.14% of the predictions were false positives while 3.43% of predictions were false negatives. These scores show that the model generalizes well and is very good at identifying faults.
+
+
+Figure 10View largeDownload slideConfusion matrix showing the accuracy of the predictions made by the prediction model on the test data. This figure indicates high accuracy predictions by the model.Figure 10View largeDownload slideConfusion matrix showing the accuracy of the predictions made by the prediction model on the test data. This figure indicates high accuracy predictions by the model. Close modal
+
+
+Discussions and Conclusion
+
+
+Considering the predictions made regarding the classification of the entries and the values of the metrics used to evaluate the model. The model has an accuracy of 0.934, a recall value of 0.963, precision of 0.966 and an F1 score of 0.965. Given these scores, we can conclude that this a successful prediction model and one that can be used for predictive analysis regarding faults in diesel generators.
+
+
+Analysis of the confusion matrix also supports this inference.
+
+
+The model's performance is satisfactory and can be trusted. However, a more extensive and comprehensive project would be to be able to identify the specific fault of the generator using data. One way to do this would be to create a subgroup of faults to be able to accurately pinpoint the specific problem.
+
+
+From the tests performed on the result of the classification model we can conclude that this approach for predicting faults in diesel generators is a functional one. The results of the simulated dataset can be used as a model to predict whether a generator is about to be faulty in real time and raise an alert to generate the appropriate response. The same process through which this data was collected and used for analytical purposes extends to other metrics in generation equipment. Fuel level data can be monitored in real time; the volume used can also be estimated and monitored; also, the temperature; coolant levels, and power output levels. All machine performance can be monitored with an event detection system creating a notification whenever a problem is discovered.
+
+
+This method presents a novel way the predict faults early in analog generation equipment by installing telematics devices on them and then analyzing the data to improve productivity, maintenance, and boost profits.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+DenysPestana-Viana, RicardoH. R.Guti´errez, Amaro A.de Lima, Fabr´icioL. e Silva, Luiz Vaz, Thiago deM. Prego, and UlissesA. Monteiro - Application of Machine Learning in Diesel Engines Fault IdentificationRuihanWang, HuiChen, CongGuan, WenfengGong, ZehuiZhang - Research on the fault monitoring method of marine diesel engines based on the manifold learning and isolation forestRoosefert MohanT, Preetha RoselynJ, Annie UthraR, DevarajD, UmachandranK - Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery.Data Science Blogathon: Rohit Pant, 2020.
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211944-MS
+
+
+

files/2022/Dispersion Modeling of Accidental Release of Propane and Butane Case Studies of Some Locations in Lagos Nigeria.txt

@@ -0,0 +1,261 @@
+----- METADATA START -----
+Title: Dispersion Modeling of Accidental Release of Propane and Butane: Case Studies of Some Locations in Lagos, Nigeria
+Authors: Olumuyiwa M. Joseph, Almoruf O. F. Williams
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211935-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+This paper presents the study of the dispersion modeling of accidental release of propane and butane using three locations in Lagos as case studies. The first case scenario was an actual incident while the other two were hypothetical case scenarios. In this research work, the purpose is to predict and evaluate the dispersion behaviour of the accidental releases of propane and butane using the Areal Location of Hazardous Atmosphere (ALOHA) modeling software, developed and made freely available by the US Environmental Protection Agency (EPA), along with Google Earth Pro mapping software which is also freely available. The modelling approach is applied to three (3) different study areas in Lagos: Propane Tanker along Iju Ishaga Road, Butane Cylindrical Tank at ABC Refilling Plant along Ikorodu Road and Butane Spherical Storage Tank at XYZGas Terminal in Apapa. The overall modelling study is concentrated on three (3) different hazardous scenarios of interest – flammable area of vapour cloud, blast area from vapour cloud explosion (uncongested) and blast area from vapour cloud explosion (congested). The flammability (flash fire) and overpressure (blast force) hazards considered in this study were modeled using the aforementioned free software. Primarily, the threat zones generated by ALOHA for separate scenarios were mapped on their respective location maps in order to evaluate the location of the dispersion plumes. For the hypothetical release scenarios considered, the dispersion modeling results showed that the Case 3 (XYZGas LPG Terminal in Apapa) has the most impacted areas for the red, orange and yellow threat zones with respect to buildings, institutions, shops, companies, streets, roads, etc. For the first study area, the results predicted the reported impact of the damaging effects for the Scenario C release. For the second study area, the results show that no threat zones are generated for the uncongested overpressure of Secnario B release. The kind of analysis and results obtained from this study would prove beneficial to the emergency planners and responders such as Lagos State Emergency Response Agency specialized in these study areas to help minimize the impacts of these dangerous releases and plan for safety decisions and mitigation techniques to be implemented where appropriate.
+
+
+
+
+Keywords:
+liquefied natural gas,
+north america government,
+downstream oil & gas,
+software,
+united states government,
+modeling & simulation,
+gas monetization,
+lng,
+hsse standard,
+chemical spill
+
+
+Subjects: 
+Natural Gas Conversion and Storage,
+HSSE & Social Responsibility Management,
+Environment,
+Professionalism, Training, and Education,
+Information Management and Systems,
+Liquified natural gas (LNG),
+HSSE standards, regulations and codes,
+Oil and chemical spills,
+Communities of practice,
+Knowledge management
+
+
+
+
+Introduction
+
+
+Industrial materials can be hazardous because of their inherent toxicity or flammabilitynature. Hazardous chemical release which could be accidental or due to improper handling of the chemical by personnel, is a serious threat to the public, primarily to human health, safety and sometimes, to asset depending on th chemical released (Ilić, et al, 2019). Consequently, there has been a growing concern when these materials are released into the environment due to process mal operation or sabotage. Thus, the work presented here focuses on the accidental release scenarios of the flammable substances, propane/butane either due to leaking of gas cylinders/pipes or rupture of gas tankers, thereby causing environmental disasters, notably fires and explosions with the consequent fatalities and significant destruction of assets/valuables and properties.
+
+
+The major components of liquefied petroleum gas (LPG) are propane and butane. They are categorically under the natural gas, a type of fossil fuel; coal and oil being the other types (Christina, 2019). It is well known (Mike, 2018; Leadership, 2018; Ogbette et al, 2018; Nathaniel, 2020 and This Day, 2020) that there have been several occurrences of fire and explosion incidents resulting from the accidental release of propane/butane (LPG) during handling, transportation or from storage tanks in the country. It is therefore of utmost necessity to prevent or mitigate the incidents in order to minimize its impacts to lives and built assets. One of the steps that can be taken in this regard is to routinely carry out risk assessment and implement mitigation measures to prevent the accidental releases of propane/butane, and in the event of a release, to use available tools, such as presented here, to quickly assess the potential impact and extent, and to effectively plan for emergency respons.
+
+
+Dispersion is an air-driven phenomenon that describes the diffusion and transport of pollutant gases released from one location to another. Hence its name, air dispersion. The Center for Chemical Process Safety (CCPS) Glossary defines "Accidental Chemical Release "as an unintended or sudden launch of chemical(s) from manufacturing, processing, coping with or on-site storage services to the air, water or land". There was an accidental release of methyl isocyanate (MIC) in Bhopal, India (Bowonder, 1985) which resulted in several fatalities and injuries to many thousands of people, and thus prompted extended public focus of the effects of chemicals launched into the environment. It is apparent that chemical release incidents, either accidental or intentional, can have devastating effects on people's lives and properties.
+
+
+Owing to the detrimental effects of chemical release incidents, various air dispersion models are developed to curb the problems imposed by pollutant gases, and there are three (3) categories of models available today for use, which are the emergency response models, planning models and research models with their distinct characteristics (Cornwell, 1999). The emergency response models only need minimal inputs from the user, and are able to quickly generate results. Thus, they are more suitable for application to emergency chemical releases since the user is able to make speedy decision about the required input parameters with only occasional need to estimate some critical input parameters that may be required so that results can be obtained quickly. CAMEO (Computer-Aided Management of Emergency Operations) /ALOHA (Areal Location of Hazardous Atmosphere) and ARCHIE (Automated Resource for Chemical Hazard Incident Evaluation), are two of the more common emergency models. Unlike the emergency response models designed to be run during crisis, planning models give practically better estimates of the likely hazardous zones produced by the accidental releases of flammable and/or toxic chemical materials. To successfully use these models, the user often has to check for material physical properties for input to the system. The application of these models are useful during the planning stage o a project, and are usually deployed to check plant layout, establishment of buffer zones with adjacent facilities and/or the public, and for compliance with regulatory requirements. Three examples of the models in this category that are used to assess toxicity, flammability and explosive hazards are: PHAST (Process Hazard Analysis Software Tool), CHARM (Chemical Hazard Assessment and Risk Management) and CANARY. The third category are the the research models which are designed for detailed assessment of the particular nature of a relase incident. Often, the model analysis is site dependent making it difficult to apply the outcome more generally. Apart from requiring multiple programs for complete analysis, these programs are awkward to operate, and the user has to supply siginificant amount of required input data. As a result, the research models are not employed in the assessment o emergency releases. Common ones include DEGADIS (Dense Gas Dispersion Model), SLAB and HEGADAS (HEavy GAs Dispersion from Area Sources) (Corwell, 1999; Tauseef et al., 2017).
+
+
+In this research study, the ALOHA software is adopted to model the dispersion of accidental release of propane/butane. The ALOHA software is suitably applicable for both emergency and planning release scenarios. The ALOHA software is part of the CAMEO® Software suites developed for the calculation of dispersion of pollutant gases (NOAA and EPA, 1999). It is easy to use and is widely and freely distributed as an open-source software. The ALOHA® software can model both real and potential chemical releases and is generally applicable in planning for and responding to chemical emergencies (Jones et al. 2013). The software is able to produce approximate threat zones related to the hazardous chemical releases, including toxic gas clouds, fires, and explosions. It does this by combining source strength models and dispersion models for the estimation of the spatial extent of toxic clouds, flammable vapour and explosive vapour clouds. A major highpoint is the ability of the software to generate reasonable results quickly enough to be of practical use to responders during a real emergency.
+
+
+Heavy gas dispersion model is employed in this research work with the aid of the ALOHA software to evaluate and predict the dispersion of the accidental release scenarios involving LPG (propane and/or butane). Heavy gas dispersion model is attributed for use for dense or heavier-than-air gases which exhibit different dispersion behaviours after a release occurrence from their sources. Some pollutant gases tend to slump toward the earth surface because of their higher molecular weight / density than air (the dispersion medium) but the vertical diffusion is inhibited as it moves downward very close to the ground and it tends to disperse laterally and thereafter the upwind and downwind movement follow. This dispersive behaviour is typical of heavier-than-air/dense gases such as propane (C3H8), butane (C4H12), hydrogen sulfide, ammonia, carbon tetrafluoride (CF4), hexafluoroethane (C2F6), etc. Many gases released from processes in the petroleum industries, refinery industries have higher molecular weight and higher densities than air at ambient temperatures. Therefore, modelling the dispersion of hazardous pollutant gases is helpful to predicting the effects of the accidental chemical releases into the atmosphere (Leelossy, 2014). Thus, this research work uses the ALOHA modeling software to estimate the source strength of the release and also to predict the dispersion behaviour of the flammable vapour cloud. In this dispersion modelling, the ALOHA software also generates areas where people and assets may be at risk of exposure using the selected user-specified Levels of Concern (LOC) for the case scenarios in view and calculates concentrations of the flammable chemical released in different scenarios. The outcomes of this dispersion modelling are used to determine the effects of the accidental release incidents e.g. locations of impacted areas in terms of persons/personnel/buildings and more and also the ambient concentrations of propane (C3H8) /butane (C4H10) releases which are the two major constituents of liquefied petroleum gas (LPG). The common composition is about 65% of butane and 35% of propane at ordinary temperature and atmospheric pressure (Brzezinska and Markowski, 2017). With respect to hazardous properties posed by these chemicals, flammability is of interest to consider rather than the less severe toxic effect as the effects of the chemical release because their flammable/explosive natures are very much devastating and life-threatening.
+
+
+The increases in explosions/fires from accidental releases of propoane/butane across many locations in Lagos in particular, and the nation in general, may not the unconnected with the increasing adoption of the subject chemicals for energy utilization in most and major parts of the nation particularly for households and businesses without the expected preventive and predicitive measures in the event of a release incident (Sacha, 2016, Giovanni and Giacomo, 2018). Thus, the purpose of this paper is to report the prediction and evaluation of the dispersion of the accidental releases of propane and butane in the following Lagos-based areas: Iju Ishaga Road, Ifako-Ijaye; ABC LPG Refilling Plant, Ikorodu; and XYZGas LPG Plant, Apapa with the names of the LPG plants masked for anonymity. The findings of this analysis will be beneficial to specialists such as firefighters, industries, schools, environmental organizations, police departments, Lagos State Agencies such as Lagos State Emergency Management Agency (LASEMA), LASEMA Response Unit (LRU) as well as relevant Federal government agencies in planning for emergency response of accidential releases and to help in minimizing the damaging explosive impacts on people and assets.
+
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+While a number of studies on accidental propane/butane releases for stationary and mobile sources have appeared in the literature in other climes (e.g. Al-Sarawi, 2018; Anjana et al., 2015; Behesti, 2018; Bendib et al., Bubbico and Marchini, 2008), to the knowledge of the authors, this appears to be the first of such study in the open literature in the country.
+
+
+Methodology
+
+
+The methodology consists of the following: identification/description of the study areas, compilation of the required data for the modeling/analysis using the ALOHA Software, and data entry and simulation of the study Areas using the ALOHA Software.
+
+
+Identification/Description of Study Areas
+
+
+Case 1: Release Scenario along Iju Ishaga Road, Lagos State
+
+
+The actual case scenario involves the accidental release of propane gas along Iju Ishaga Road, in Iju Ishaga Area of Lagos on Thursday 24th September, 2020 at 3.30pm (15:30). Iju Ishaga, originally known as Iju, is one of the major neighbourhoods under Ifako-Ijaiye Local Government Area in Lagos, Nigeria. Iju Ishaga is apparently regarded as a suburb of Lagos, that is, it is one of the border towns of Lagos because of its proximity to Ogun State. It is geographically located in Agege, Lagos, Nigeria, Africa and its geographical coordinates are 60 38’ 28" North and 30 19’ 24.8" East. The local government area is populated with over four hundred thousand (400,000) occupants according to the Census held in 2006 (Abiola, 2018). Nathaniel (2020) and the newspaper publication, This Day (2020), reported the devastating destruction of buildings from the explosion that occurred from the lone accident of a gas tanker with at least one fatality and many persons critically injured.
+
+
+Case 2: Hypothetical Release Scenario at ABC LPG Refilling Plant, Ikorodu Road, Lagos State
+
+
+This is a hypothetical case scenario that involves the accidental release of butane gas at ABC LPG Refilling Plant along Ikorodu Road, Kosofe Local Government Area of Lagos State. The geographical coordinates of Kosofe are 60 36��� 16.9" North and 30 24’ 25.6" East, with a land area of about 18km2 and a population of about one million according to Kosofe Voice (2019). The accidental release at the LPG Refilling Plant is assumed to have occurred on Tuesday 20th October, 2020 by 11.00am.
+
+
+Case 3: Hypothetical Release Scenario at XYZGas LPG Terminal, Lagos State
+
+
+This hypothetical case scenario involves the accidental release of butane gas in XYZGas LPG Terminal, Apapa, Lagos. Apapa is a local government area in Lagos State with a total population of 217,362 people based on the Nigerian National Population Census (2006). The geographical coordinates of the location of the incident are 60, 36’ 16.7" North and 30 24’ 25.6" East. The accidental release is assumed to have occurred on Tuesday 4th March, 2020 by 1.00pm.
+
+
+Compilation of the required data for the modeling/analysis using the ALOHA Software
+
+
+The data that are required for the modeling/analysis using the ALOHA software (Jones et al., 2013) include the following: site data, atmospheric data, chemical data, source strength.  Appendix A shows the compiled data for the study areas described above. While some of the data (such as the chemical data) are available in the ALOHA software, others have to be provided by the user for entry into the software.
+
+
+Data Entry and Simulation of the Study Areas using the ALOHA Software (Version 5.4.7)
+
+
+The following basic procedural steps were used in the successful modelling of the accidental release of (propane and butane) as distinctly applied to all the scenarios presented in  Appendix A.
+
+
+The city where the accidental chemical release has occurred and the time and date of the accident were entered on appropriate graphical interface.The chemical of concern for every scenario was specified from ALOHA's library of chemical information.Critical and valuable information about weather conditions/meteorological information were correctly entered for every case scenarioThe source of the chemical release was selected and described with respect to the release scenario in consideration.The concentration of chemical in the air at which there is a concern was chosen i.e. Level of Concern (LOC)ALOHA is then requested to display the intended threat zone for every scenarioALOHA's feature of "export as KML file" was then preferably used in order to display the exact location of each and every threat zone generated by ALOHA on a map.
+
+
+In the ALOHA software, Level of Concern (LOC) is an important requirement in analyzing a chemical release scenario. One or more (up to three LOCs) must be selected in order for the ALOHA software to estimate a threat zone, an area where the hazard is predicted to exceed that LOC at some time after a release begins. A Level of Concern (LOC) is defined in the ALOHA sotware as a threshold value of a hazard (toxicity, flammability, thermal radiation or overpressure). A flammable Level of Concern (LOC) is a threshold concentration of fuel in the air above which a flammability hazard may exist. Similarly, an overpressure Level of Concern (LOC) is a threshold level of pressure from a blast wave, usually the pressure above which a hazard may exist. In the ALOHA software, multiple hazards ranging from toxicity, flammability, thermal radiation to overpressure can be modeled, but the type of LOC to select will vary based on the hazard. The threat zones are displayed by the ALOHA software in red, orange and yellow, which are overlaid on a single picture. The red zone describes the worst hazard while the other zones describe a relatively less decreasing hazard. The threat zones can also be displayed on a map using a mapping software application such as MARPLOT or Google Earth Pro.
+
+
+Results and Discussion
+
+
+Due to file size limitations, only a sample of the results of the three cases investigated are presented and discussed here.
+
+
+Case 1: Model Outputs for releases - Iju Ishaga Road
+
+
+Fig. 1 shows the threat zone estimates for Scenarios A, B and C obtained from the ALOHA software. In addition, Tables 1 through 3 are summaries of the results of the dispersion modeling from the ALOHA software outputs.
+
+
+Figure 1View largeDownload slide(a): Flammable Threat Zone of Scenario A, (b) Overpressure (Blast Area) Threat Zone of Scenario B, (c) Overpressure (Blast Area) Threat Zone of Scenario CFigure 1View largeDownload slide(a): Flammable Threat Zone of Scenario A, (b) Overpressure (Blast Area) Threat Zone of Scenario B, (c) Overpressure (Blast Area) Threat Zone of Scenario C Close modal
+
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+Table 1Threat Zone Summary- Scenario A (Case 1)   View Large
+
+
+Table 2Threat Zone Summary- Scenario B (Case 1)   View Large
+
+
+Table 3Threat Zone Summary- Scenario C (Case 1)   View Large
+
+
+Based on Fig. 1a and Table 1, it can be seen that the ALOHA software estimated that the red threat zone, which represents the worst level hazard, will extend 57m in the downwind direction. The orange and yellow threat zones being the areas of decreasing hazard, will extend 88m and 136m respectively in the downwind direction. However, the flammable area red threat zone which represents 40% LEL at a concentration of 8400ppm, is the estimated area where the ALOHA software predicts a flash fire or vapour cloud explosion could occur at some point after the release begins. Fig. 2b and Table 2 show that the red, orange and yellow threat zones extend 22m, 27m and 29m respectively in the downwind direction. In this case, the red, orange and yellow threat zones indicate the areas where the overpressure is predicted to exceed the corresponding LOCs at some time in the hour after the release begins. Thus, the resulting impact could be slight - characterized by minor damages to house structures which may include some window frames damages and/or windows shatters at the specified levels of concern (LOCs). On the other hand, Fig. 1 and Table 3 show the red, orange and yellow threat zones extend 22m, 29m and 59m respectively in the downwind direction. These threat zones indicate the areas where the predicted overpressure LOCs exceed the corresponding LOCs at some time in the hour after the release begins. Therefore, the damaging effects accompanying this particular scenario are characterized as nearly complete destruction or partial demolition of houses, fatalities/serious injuries at the different specified overpressure levels of concern (LOCs). Thus the predicted red and orange threat zones for the Scenario C is close to the destructive impact of this accidental release as reported by Nathaniel (2020). Given the reported extent of the coverage of the actual incident, it is most probable that the flammable area of the vapor cloud which extends up to 136m (see Table 1) must also have been ignited in addition to the tanker explosion.
+
+
+Figure 2View largeDownload slideGoogle Earth mapping of threat zone locations generated by the ALOHA software. (a) Secenario A, (b) Scenerio B and (c) Scenario CFigure 2View largeDownload slideGoogle Earth mapping of threat zone locations generated by the ALOHA software. (a) Secenario A, (b) Scenerio B and (c) Scenario C Close modal
+
+
+Fig. 2 shows the map representation of the ALOHA-generated threat zone for scenarios A, B and C using Google Earth Pro. From these figures, it is then possible to visually see any of the areas that would be impacted for each respective scenario. This kind of information is very useful for emergency planning and emergency responders.
+
+
+Case 2: Model Outputs for Releases at ABC LPG Plant
+
+
+Fig. 3 shows the threat zones generated for the Scenario A while no threat zones were generated for the Scenario B because none of the chosen overpressure LOCs was exceeded. The dispersion modeling results are summarized in Tables 4 and 5.
+
+
+Figure 3View largeDownload slide(a) Threat Zones generated by the ALOHA software (b) Google Earth Pro mapping of threat zone locations for Scenario A of Case 2.Figure 3View largeDownload slide(a) Threat Zones generated by the ALOHA software (b) Google Earth Pro mapping of threat zone locations for Scenario A of Case 2. Close modal
+
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+Table 4Threat Zone Summary - Scenario A (Case 2)   View Large
+
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+Table 5Threat Zone Summary - Scenario B (Case 2)   View Large
+
+
+Fig. 3 and Table 4 show that the red threat zone, which represents the worst level hazard, will extend 187m in the downwind direction. The orange and yellow threat zones being the areas of decreasing hazard, will extend 276m and 516m respectively in the downwind direction. However, the flammable red threat zone which represents 40% LEL at a concentration of 9600ppm is the estimated area where ALOHA predicts a flash fire or vapour cloud explosion could occur at some point after the release begins.
+
+
+Case 3: XYZGAS LPG Plant
+
+
+Fig. 4 shows the threat zones generated for this case. The dispersion modeling results are summarized in Table 5 and 6. Although not listed as part of the Case 3 modeling presented in  Appendix A.3, it is instructive to note that the threat zone for the flammable area of the of vapour cloud (in Table 5) is very much larger than the case for overpressure. The predicted impacted areas (determined from the Google Earth Pro) map are shown in Tables 8 and 9.
+
+
+Figure 4View largeDownload slide(a) Threat Zones generated by the ALOHA software (b) Google Earth Pro mapping of threat zone locations for Case 3 under hypothetical releaseFigure 4View largeDownload slide(a) Threat Zones generated by the ALOHA software (b) Google Earth Pro mapping of threat zone locations for Case 3 under hypothetical release Close modal
+
+
+Table 6Case 3 Threat Zone Summary for Flammable Area of Vapour Cloud   View Large
+
+
+Table 7Case 3 Threat Zone Summary for Overpressure from Vapour Cloud Explosion   View Large
+
+
+Table 8Predicted Impacted Areas for Case 3 - Flammable Area of Vapour Cloud   View Large
+
+
+Table 9Predicted Impacted Areas for Case 3 - Overpressure from Vapour Cloud Explosion   View Large
+
+
+Thus using the ALOHA software, it has been possible to estimate the flammable areas and blast areas of flammable vapour clouds for all the cases/scenarios identified graphically as threat zones: red, orange and yellow. Furthermore, using the Google Earth Pro mapping application software, it was possible to display the the estimated locations of the flammability and overpressure threat zones via KML-converted ALOHA files for all case scenarios. This makes it possible to easily identify affected entities such as buildings, roads, companies, populations, etc which could be potentially harmed upon exposures to the accidental releases of propane or butane for the three cases investigated. The red zone represents affected area with the most severe concentration of the flammable and explosive chemical, which may cause life-threatening effects and fatalities upon exposure. On the other hand, the orange zone is the affected area with less severe concentration of the chemical, which may cause lasting adverse health effects. The yellow zone represents an affected area of decreasing concentration of the chemical, which may cause an average individual to feel notable discomfort, irritation but reversible upon cessation of exposure. These results are useful for risk assessment of faciltiies handling propane/butane and to determine their location during planning/approval stage. These kinds of analys/results are also very useful for emergency response personnel in planning evacuation and mitigation measures in the event of a large accidential release of these gases. It should be noted that because the impact of a release incident may occur as soon as the release event itsef, there may not be enough time for emergency response. Thus in practice, much attention is paid to efforts to prevent the release incident using such methods as inherently safer designs, engineering barriers and operational procedures/management. A general presentation of these methods can be found in Crowe and Louvar (2011).
+
+
+Conclusions
+
+
+In this study, three (3) different study areas in Lagos were considered for dispersion modelling of accidental release of propane/butane under three (3) different probable release scenarios which include the flammable area of vapour cloud, blast area from vapour cloud explosion (uncongested) and blast area from vapour cloud explosion (congested). The modelling tool – the ALOHA software. and the mapping application, Google Earth Pro were used in this study. both instrumental in actualizing the objectives of this study. For the first study area, the results correctly predicted the reported impact of the damaging efects for the Scenario C release. For the second study area, the results show that no threat zones are generated for the uncongested overpressure of Secnario B release. However, it was seen that the flammable red threat zone was estimated to extend about 187m in the downwind direction. This is the area that the ALOHA software predicts that a flash or vapour cloud explosion could occur at some point after the release begins. The results for the hypothetical scenario of Case 3 showed that the impact could be very devasting with potential fatalities and destruction of assets around the location as identified by the red and orange threat zones on Google Earth Pro.
+
+
+The availability of the free and open source ALOHA software and the free Google Earth Pro mapping software should make it possible to readily carry out risk assessment of accidental releases of propane/butane as well as use in emergency planning and response.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+A.1 Case 1 Release: Iju Ishaga Road, Ifako Ijaye, Lagos
+
+
+Table A.1.1Site Data   View Large
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+
+Table A.1.2Chemical Data (ALOHA Software)   View Large
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+Table A.1.3Atmospheric Data (World Weather Online)   View Large
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+Table A.1.4Source Strength   View Large
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+
+A.2 Case 2 Release: Abc Lpg Refilling Plant, Lagos
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+Table A.2.1Site Data   View Large
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+Table A.2.2Chemical Data (ALOHA Software)   View Large
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+Table A.2.3Atmospheric Data (Time and Date, 2020)   View Large
+
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+Table A.2.4Source strength Parameters   View Large
+
+
+A.3 Case 3 Release: Xyzgas Lpg Terminal, Lagos
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+Table A.3.1Site Data   View Large
+
+
+Table A.3.2Chemical Data (ALOHA Software)   View Large
+
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+Table A.3.3Atmospheric Data (Time and Date, 2020)   View Large
+
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+Table A.3.4Source strength (hypothetical)   View Large
+
+
+References
+
+
+AbiolaA. (2018). Iju Ishaga: What to Expect and Know. Prestige MagazineAvailable at:https://www.propertypro.ng/blog/iju-ishaga-what-to-expect-and-know/Google Scholar Al-Sarawi, N. M. (2018). ‘Evaluation of Accidental Atmospheric Releases of Chlorine and Butane from a Mobile Source Using ALOHA and MARPLOT’, American Journal of Environmental Protection. Vol. 6, No. 6, pp 144–155. https://doi.org/10.11648/j.ajep.20170606.12Google ScholarCrossrefSearch ADS  Anjana, N. S.et al.  (2015). ‘Population Vulnerability Assessment and a LPG Storage and Distribution Facility near Cochin using ALOHA and GIS’, International Journal of Engineering Science Innovation, Vol. 4, No. 6, pp 23–31. Available at:www.ijesi.org.Google Scholar Behesti, M.H., (2018). Modelling the Consequences of Explosion, Fire and Gas Leakage in Domestic Cylinders containing LPG [Online]. Annals of Medical and Health Sciences Research [Viewed 26 March 2020]. Available from:www.amhsr.org/articles/modelling-the-consequences-of-explosion-fire-and-gas-leakage-in-domestic-cylinders-containing-lpg-4291.htmlGoogle Scholar Bendib, R., Mechhoud, E. A., Rodriguez, M., & Zennir, Y. (2021). A systematic approach for risk assessment in LPG storage tanks area-SKIKDA refinery. Algerian Journal of Environmental Science and Technology, Month edition. Vol.X. NoX. (YYYY), ISSN : 2437-1114.Google Scholar Bowonder, B. (1985). The Bhopal incident: Implications for developing countries. Environmentalist, 5 (2), 89–103. https://doi.org/10.1007/BF02235978Google ScholarCrossrefSearch ADS  Cornwell, J. B. (1999). ‘Real-time Modelling during Emergency Situations. Is this a good idea?’. Presented at Mary Kay O'Conner Process Safety Center 1999 Annual Symposium Beyond Regulatory Compliance, Making Safety Second Nature. College Station, Texas, October 26-27, 1999Available at:http://www.questconsult.com/Google Scholar Crowl, D.A., Louvar, J.F. (2011). Chemical Process Safety: Fundamentals with Applications (3rd Edition)3rd ed., PTR Prentice Hall, Engelwood Cliffs, New Jersey.Google Scholar Giovanni, O. and Giacomo, F. (2018). The Changing Role of Natural Gas in Nigeria. SSRN Electronic Journal. Available at:https://www.researchgate.net/publication/324459715Google Scholar Ilic, P., Markic, D. N., Bjelic, L. S., & Farooqi, Z. U. R. (2019). Dispersion Modeling of Accidental Releases of Propane Gas. QUALITY OF LIFE, 17 (1-2). https://doi.org/10.7251/QOL1901041IGoogle Scholar Jones, R., W.Lehr, D.Simecek-Beatty, R.Michael Reynolds. 2013. ALOHA® (Areal Locations of Hazardous Atmospheres) 5.4.4: Technical Documentation. U. S. Dept. of Commerce, NOAA Technical Memorandum NOS OR&R 43. Seattle, WA: Emergency Response Division, NOAA. 96 pp.Google Scholar Brzezinska, D., & Markowski, AS (2017). Experimental investigation and CFD modeling of the internal car park environment in case of accidental LPG release. Process Safety and Environmental Protection, 110, 5–14. https://doi.org/10.1016/j.psep.2016.12.001Google ScholarCrossrefSearch ADS  Bubbico, R. and Marchini, M. (2008). ‘Assessment of an explosive LPG release accident: A case study’, Journal of Hazardous Materials155 (2008), pp 555–565. Available at:www.sciencedirect.comhttps://doi.org/10.1016/j.jhazmat.2007.11.097Google Scholar Christina, N., (2019). Fossil Fuels, explained. [Online]. National Geographic. [Viewed 26 March 2020]. Available from:https://www.nationalgeographic.com/environment/energy/reference/fossil_fuels/Leadership., (©2018). Nigeria: Gas Plant Explosions. [Online]. AllAfrica. [Viewed 25 March 2020]. Available from:https://allafrica.com/stories/201801160094.htmlLeelossy, Á., Molnár, F., Izsák, F., Havasi, Á., Lagzi, I., & Mészáros, R. (2014). Dispersion modeling of air pollutants in the atmosphere: a review. Open Geosciences, 6 (3), 257–278. https://doi.org/10.2478/s13533-012-0188-6Google ScholarCrossrefSearch ADS  Mike, M., (2018). Nigeria, gas tanker explosion leaves 35 dead in Nasarawa State. [Online]. Lifegate. [Viewed 25 March 2020]. Available from:https://www.lifegate.com/people/lifestyle/nigeria-gas-tanker-explosionGoogle Scholar Nathaniel, Soonest: Lagos Tanker Explosion Leaves Carnage Behind: https://www.channelstv.com/2020/09/24/photos-lagos-tanker-explosion-leaves-carnage-behind/NOAA and EPA (1999). ALOHA User's Manual, Office of Response and Restoration of the National Oceanic and Atmospheric Administration (NOAA) and Chemical Emergency Preparedness and Prevention Office (CEPPO) of the U.S. Environmental (EPA), Seattle, WAOgbette, A.S., Ori, O.E., Idam, M.O. and Abwage, S.T., (2018). Continuous Gas Explosions in Nigeria: Causes and Management. [Online]. Research Gate. [Viewed 25 March 2020]. Available from:https://www.researchgate.net/publication/328916865_Continuous_Gas_Explosions_in_Nigeria_Causes_and_ManagementGoogle Scholar Sascha, S., (2016). LPG an affordable energy alternative. [Online]. Engineering News. [Viewed 26 March 2020]. Available from:https://m.engineeringnews.co.za/article/lpg-an-affordable-energy-alternative-2016-08-26/rep_id:4433Google Scholar Tauseef, S. M., Abbasi, T., Suganya, R., & Abbasi, S. A. (2017). A critical assessment of available software for forecasting the impact of accidents in chemical process industry. International Journal of Engineering, Science and Mathematics, 6(7), 269–289.Google Scholar This Day (2020). Lagos Gas Explosion and Matters Arising. Available at:https://www.thisdaylive.com/index.php/2020/10/12/lagos-gas-explosion-and-matters-arising/Time and Date (2020). Past Weather in Apapa Iganmu Local Council Development Area, Nigeria - Yesterday and Last 2 Weeks - Available at:https://www.timeanddate.com/weather/@10458942/historicTime and Date (2020). Past Weather in Ikorodu North Local Council Development Area, Nigeria - Yesterday and Last 2 Weeks. Available at:https://www.timeanddate.com/weather/@10482975/historicWorld Weather Online (2020). Lagos Weather History. Available at:https://www.worldweatheronline.com/lagos-weather-history/lagos/ng.aspx
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211935-MS
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files/2022/Dynamic Adsorption of Enzyme on Sand Surfaces- An Experimental Study.txt

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+----- METADATA START -----
+Title: Dynamic Adsorption of Enzyme on Sand Surfaces- An Experimental Study
+Authors: Tinuola Udoh, Utibeabasi Benson
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211905-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Enzyme can reduce interfacial tension between oil and water thereby mobilising more oil than would originally be produced but its adsorption on the porous rock surfaces reduces its efficiency. This study presents experimental investigation of dynamic adsorption of enzyme on sand surfaces. The experiment was carried out at varied brine salinities and enzyme concentrations on different sand grain sizes. The concentration depletion method that accounts for the difference in enzyme concentrations in solution before and after its contact with the sand was used to determine the enzyme adsorption on relevant surfaces. The effluent sample from the adsorption process was collected after every three minutes until equilibrium was reached and the final concentration of the enzyme in the effluent solutions was measured and used to determine its adsorbed concentration on the sand surfaces. The results of this study show that increase in concentration of enzyme results in increase in its adsorption on sand surfaces. Also, increase in brine salinity increased enzyme adsorption on the sand surfaces but increase in sand grain size however reduced its adsorption. The result of this study is relevant in the design of enzyme enhanced oil recovery process.
+
+
+
+
+Keywords:
+upstream oil & gas,
+enhanced recovery,
+enzyme,
+salinity,
+concentration,
+enzyme concentration,
+journal,
+static adsorption,
+adsorption,
+brine salinity
+
+
+Subjects: 
+Improved and Enhanced Recovery
+
+
+
+
+Introduction
+
+
+Enzymes are a specific group of proteins that are synthesized by living cells to work as catalysts for the many thousands of biochemical reactions [1]. In enhanced oil recovery (EOR) process, enzyme flooding has gained relevance due to its ability to lower interfacial tension between oil and water thereby mobilizing more oil to the production wells [2, 3]. The enzyme EOR process has a great potential of maximizing oil recovery factor of existing reservoir rocks, where a significant volume of the unrecovered oil is targeted after the conventional method, its adsorption rate may however reduce its efficiency [4, 5, 6]. Adsorption is a dynamic process that involves an interaction between the adsorbed substance (adsorbate) and the substance on which adsorption takes place (adsorbent). Adsorption may result from physisorption or chemisorption process. The difference between them is usually based on their temperature dependence. In physisorption process, adsorption reduces generally with increase in temperature while chemisorption adsorption process, adsorption increases with temperature [7].
+
+
+From literatures, two methods are generally used to determine the adsorption of different adsorbates on solid surfaces. These methods are static and dynamic adsorption processes. The static method allows adsorbate solution to reach equilibrium with crushed solid surfaces over a period. In the case of dynamic process, the solid grains packed in a column are flooded with adsorbate solution and the effluent concentration of the adsorbate at different cumulative injected volumes is determined [8]. A few studies have been conducted on static adsorption of enzyme, but little or no studies on dynamic adsorption of enzyme are available. For example, Udoh [9] investigated static adsorption of rhamnolipid and greenzyme on carbonate and sandstone rock surfaces and observed strong affinity of rhamnolipid for sandstone surfaces while greenzyme adsorbed more carbonate surfaces. Also, in the study conducted by Udoh and Ekanem [10] on static adsorption of greenzyme on sand surfaces of different grain sizes at varied temperatures, their results showed that the adsorption of greenzyme on sand surfaces increases with increase in its concentrations and decrease in sand particles but reduces with increase in temperature.
+
+
+Furthermore, salinity of the brine also plays an important role in the adsorption of enzyme. According to Udoh [9], increase in salinity leads to increase in enzyme adsorption on the porous rock surfaces (carbonate and sandstone). Also, small grain size rock surfaces exhibit high adsorption efficiency because of their large surface areas available for adsorption [10]. Hence, the aim of this study is to experimentally investigate the dynamic adsorption of enzyme on different sand surfaces in different brine salinities relevant to hydrocarbon reservoirs. The concentration depletion method which involves comparison of concentrations of enzyme in aqueous solutions before and after their dynamic contacts with sand surfaces was used to determine the enzyme equilibrium concentration from effluent analyses.
+
+
+Material and method
+
+
+Materials and sample preparation
+
+
+Brine
+
+
+The brines used in this study were prepared based on the composition of the formation brine of the reservoir used as case study. The formation brine has salinity of 32g/L with the compositional breakdown of 98.2% sodium chloride, 0.6% calcium chloride, 0.8% magnesium carbonate, 0.2% potassium chloride and 0.2% sodium sulphate. The salts were dissolved in distilled water and three brine salinities (10%, 50% and 100%) were used during this experiment. The 100% salinity is the formation brine, while the 50% and 10% brines are fifty and ninety percent diluted formation brine. The details of the compositional breakdown of the brines are presented in Table 1.
+
+
+Table 1The brine compositional breakdown. Components
+            . 100% Brine (g/L)
+            . 50% Brine (g/L)
+            . 10% Brine (g/L)
+            . NaCl 31.4240 15.712 3.1424 CaCl2 0.1920 0.0960 0.0192 MgCO3 0.2560 0.1280 0.0256 KCl 0.0320 0.0320 0.0064 Na2SO4 0.0320 0.0320 0.0064 Components
+            . 100% Brine (g/L)
+            . 50% Brine (g/L)
+            . 10% Brine (g/L)
+            . NaCl 31.4240 15.712 3.1424 CaCl2 0.1920 0.0960 0.0192 MgCO3 0.2560 0.1280 0.0256 KCl 0.0320 0.0320 0.0064 Na2SO4 0.0320 0.0320 0.0064 View Large
+
+
+Enzyme
+
+
+The enzyme used in this study is a commercial enzyme with 100% concentration supplied by the Biotech Processing Supply, Dallas, Texas. Four different concentrations of the enzyme solutions (1-, 3-, 5- and 10 wt. %) were prepared with distilled water and saline solutions and used in this study.
+
+
+Dynamic adsorption
+
+
+The experimental variables investigated in this study are salinity, grain size and enzyme concentration variations based on the concentration depletion method [8]. This method involves comparison of enzyme concentrations in the aqueous solutions before and after their dynamic contacts with different sand surfaces. The experiment was carried out with different enzyme concentrations, brine salinities and sand grain sizes. The sand grains used this experiment were gotten from a river in Akwa Ibom state, Nigeria. Prior to the test, the sand grains were sorted into three sizes (0.424, 0.85 and 1.70 mm) with the aid of orbital mechanical shaker. 150 g of each grains size was then cleaned with methanol and distilled water, to remove dirt and impurities from the sand. Thereafter, the sand grains were dried in the oven at 70 °C for a period of 24 hours. After cooling, the different grain sizes were loaded into the sand-pack columns that were used for the dynamic adsorption tests. Fixed mass of sand (32.6 g) was measured and packed continuously into the sand pack column after a mesh of 0.30 mm size was placed in the column to prevent grain migration during flooding. The relevant fluids (distilled water and brines) were first injected through the sand pack to ensure saturation of the porous system. Thereafter, the enzyme solutions were injected through the system and the effluents from the flooding were collected periodically for analysis. The conductivity method was used to determine the concentrations of enzyme in all the effluents based on the measured conductivities of enzyme-aqueous solutions and their relevant concentrations before flooding. The effluents from each flooding were collected and their conductivity was measured every three minutes untill an equilibrium concentration (i.e., the point at which the concentration of effluents did not change with time) was reached. The final concentrations of enzyme were determined from effluents measured conductivity using Equation 1:
+
+
+C0A0=C1A1 .(1)
+
+
+The dynamic adsorption of the enzyme on sand surfaces was calculated using Equation 2:
+
+
+q=C0V−∑i=1nC1V1m ,(2)
+
+
+where A0 and A1 are the initial and final conductivity of enzyme solutions before and after flooding, respectively (μS/cm), q is the enzyme adsorption on sand surface (mg/g), C0 is the primary enzyme concentration before adsorption (wt.%), V is the total volume of injected enzyme solution until the effluent concentrations reached equilibrium (mL), C1 is the effluent enzyme concentration (wt.%), V1 is the volume of every collected effluent sample (mL), m is the mass of the sand in the pack column (g) and n is the total number of the effluent samples collected until equilibrium was attained. This procedure was repeated for different enzyme concentrations and brine salinities on the three grain sizes.
+
+
+Results and discussion
+
+
+Figure 1 shows the effect of variation in the concentrations of enzyme on its adsorption on different grain sizes. A general increase in the adsorption of enzyme was observed with increase in its concentration in all the solution irrespective of the different grain sizes. For instance, increase in enzyme concentrations from 1 wt.% to 10 wt.% on 0.424 mm grain size resulted in increase in adsorption from 0.03 mg/g to 1.61 mg/g. For 0.85 mm grain size, increase in concentrations from 1 wt.% to 10 wt.% also gave a corresponding increase in adsorption density from 0.02 mg/g to 1.29 mg/g. Finally, for 1.70 mm grain size, similar trend was observed with increase in concentrations from 1 wt.% to 10 wt.% that resulted in increase in adsorption density from 0.01 mg/g to 0.95 mg/g. The observed increased in enzyme adsorption with increase in its concentrations is related its molecular interactions on the sand surfaces. At low concentration, a spontaneous adsorption of enzyme occurs, but as the concentration increases, lateral interactions between the adsorbed and bulk molecules takes place until equilibrium is reached, and the adsorption density plateaus [9]. The maximum adsorption is attained when the equilibrium is reached, and the adsorption density becomes relatively constant.
+
+
+Figure 1View largeDownload slideThe effect of varied enzyme concentrations on its adsorption on different grain sizes.Figure 1View largeDownload slideThe effect of varied enzyme concentrations on its adsorption on different grain sizes. Close modal
+
+
+Also, the effect of sand grain size variation on enzyme adsorption can be seen in Figure 1. Generally, the adsorption of enzyme on the different grain sizes increases with increase in the concentration of enzyme until all the surfaces were covered and equilibrium condition was attained. This is evidenced by the plateaued adsorption density that remain constant with increase in enzyme concentration in the aqueous solution. The highest adsorption density of 1.59 mg/g was however observed with the smallest size grains (0.424 mm), while the lowest adsorption of 0.97 mg/g was observed with the biggest size grains (1.70 mm) and an intermediate adsorption of 1.27 mg/g was observed with medium size grains (0.85 mm). That is, the adsorption of enzyme on sand surfaces decreases with increase in grain sizes. This is consistent with the results of the previous study by Udoh and Ekanem [10] on static adsorption of greenzyme on sand surfaces in which decrease in adsorption was observed with increase in the sand grain sizes. This was attributable to availability of more adsorption site on smaller grains than the bigger grains. The observed enzyme adsorption in this study is however higher than their study due to variance in the quantity of sand grains available for adsorption. This shows that irrespective of the adsorption method (static or dynamic) used, the enzyme adsorption reduces with increase in the grain sizes.
+
+
+The results of the combined effect of grain size and salinity on enzyme adsorption are presented in Figure 2. Generally, a decrease in adsorption of enzyme was observed with increase in the size of the grains respective of the brine salinity being used. For the 10% salinity, increase in grain size from 0.424mm to 0.85mm and 1.70mm led to reduction of the adsorption of enzyme from 1.15 mg/g to 1.01 mg/g and 0.96 mg/g, respectively. Also, the use of the 50% salinity brine resulted in reduction of adsorption of enzyme from 1.31 mg/g to 1.25 mg/g and 1.18 mg/g on grain sizes 0.424 mm, 0.85 mm and 1.70 mm, respectively. Finally, the use of 100% salinity gave a corresponding decrease in adsorption of enzyme from 2.58 mg/g to1.52 mg/g and 1.41 mg/g on grain sizes 0.424 mm, 0.85 mm and 1.70 mm, respectively. This further shows that, the smaller the grain size, the more the adsorption of enzyme on the surfaces due to increased surface areas. This result is consistent with previous study on static enzyme adsorption on different sand grains in which higher adsorption was observed on small grain size [10].
+
+
+Figure 2View largeDownload slideThe effect of combined salinity variation and grain size difference on enzyme adsorption in (a) 10% salinity, (b) 50% salinity and 100% salinity brines.Figure 2View largeDownload slideThe effect of combined salinity variation and grain size difference on enzyme adsorption in (a) 10% salinity, (b) 50% salinity and 100% salinity brines. Close modal
+
+
+The brine salinity plays a fundamental role in the adsorption of enzyme. Previous study [9] showed that increase in brine salinity can lead to increase in adsorption of enzyme on the porous rock surfaces. Figure 3 shows the results of the effect of brine salinity on the adsorption of enzyme on different grain sizes. Increase in adsorption was observed with increase in salinity and reduction in grain sizes. Figure 3a shows the effect salinity on the adsorption of enzyme on 0.424 mm grain size. Increase in salinity from 10% to 50% and 100% resulted in a corresponding increase in adsorption of enzyme from 1.15 mg/g to 1.31 mg/g and 1.58 mg/g. Figures 3b and 3c show the effect of salinity on adsorption of enzyme on the 0.85 mm and 1.70 mm grain sizes. It was observed that increase in salinity from 10% to 50% and 100% also increased the adsorption of enzyme from 1.01 mg/g to 1.25 mg/g and 1.52 mg/g, respectively for 0.85 mm grain size and from 0.96 mg/g to 1.18 mg/g and 1.41 mg/g, respectively for 1.70 mm grain size. This shows that lower adsorption of enzyme will be achieved in low salinity brine than high salinity formation brine and emzyme adsorption decreases with increase in grain size irrespective of the brine salinity.
+
+
+Figure 3View largeDownload slideThe effect of brine salinity on enzyme adsorption on grain size (a) 0.424 mm (b) 0.85 mm and (c) 1.70 mm.Figure 3View largeDownload slideThe effect of brine salinity on enzyme adsorption on grain size (a) 0.424 mm (b) 0.85 mm and (c) 1.70 mm. Close modal
+
+
+Conclusion
+
+
+Dynamic adsorption of enzyme on three different grain sizes (0.424 mm, 0.85 mm and 1.70 mm) was systematically investigated with varied brine salinities and enzyme concentrations. The results show that increase in the concentrations of enzyme and brine salinity resulted in increase in adsorption of enzyme on the sand surfaces irrespective of the grain size. The reverse was however the case with the grain sizes, as increase in grain size resulted in decrease in adsorption of enzyme on the sand surfaces.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+R. A.Copeland, Enzymes: a practical introduction to structure, mechanism, and data analysis, John Wiley and Sons, 2000.Google Scholar T. H.Udoh, "Experimental Investigation of Temperature Effects on Low Salinity Enzyme Enhanced Oil Recovery Process," Nigeria Journal of Technological Development, vol. 17, no. 3, pp. 156–164, 2020.Google ScholarCrossrefSearch ADS  T.Udoh and J.Vinogradov, "A Synergy between Controlled Salinity Brine and Biosurfactant Flooding for Improved Oil Recovery: An Experimental Investigation Based on Zeta Potential and Interfacial Tension Measurements," International Journal of Geophysics, vol. 2019, pp. 1–15, 2019.Google ScholarCrossrefSearch ADS  H.Nasiri, K.Spildo and A.Skauge, "Use of enzymes to improve waterflood performance," in Paper presented at International Symposium of the Society of Core Analysts, Noordwijk, Netherlands, 2009.Google Scholar T.Udoh, L.Akanji and J.Vinogradov, "Experimental Investigation of Potential of Combined Controlled Salinity and Bio-Surfactant CSBS in Enhanced Oil Recovery EOR Processes," in Paper SPE 193388 in SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers. 10.2118/193388-MS, Lagos, Nigeria, 2018.Google Scholar T.Udoh and J.Vinogradov, "Effects of Temperature on Crude-Oil-Rock-Brine Interactions During Controlled Salinity Biosurfactant Flooding," in Paper SPE 198761 presented in SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 2019.Google Scholar T. H.Udoh and V.Ekanem, "Experimental Investigation of Greenzyme Adsorption on Sand Surface," ABUAD Journal of Engineering Research and Development, vol. 3, no. 1, pp. 83–89, 2020.Google Scholar A.Rashidi, A. R. S.Nazar and H.Radnia, "Application of Nanoparticles for Chemical Enhanced Oil Recovery," Iranian Journal of Oil & Gas Science and Technology, vol. 7, no. 1, 2018.Google Scholar P.Somasundaran and G. E.Agar, "The zero point of charge of calcite.," Journal of colloid interface science, vol. 24, no. 4, pp. 433–440, 1967.Google ScholarCrossrefSearch ADS  A.Barati-Harooni, A.Najafi-Marghmaleki, S. M.Hosseini and S.Moradi, "Experimental Investigation of dynamic adsorption-desorption of new nonionic surfactant on carbonate rock: Application to enhanced oil recovery (EOR)," Journal of Energy Resources Technology, 139(4), vol. 139, no. 4, 2017.Google Scholar T.Udoh, "Comparative Study on Adsorption of Biologically Generated Surface Active Agents on Carbonate and Sandstone Rock Surface," International Journal of Current Research and Academic Review, vol. 7, no. 2, pp. 21–36, 2019.Google ScholarCrossrefSearch ADS  
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211905-MS
+
+
+

files/2022/Dynamics of Heat Transport from a Reservoir to the Adjoining Formation in a Thermal Flood.txt

@@ -0,0 +1,293 @@
+----- METADATA START -----
+Title: Dynamics of Heat Transport from a Reservoir to the Adjoining Formation in a Thermal Flood
+Authors: Kazeem Lawal
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211976-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Heat transfer from a petroleum reservoir to adjoining rocks is detrimental to thermal floods. While such thermal losses are inevitable, understanding the timescales of these heat exchanges would improve the design and management of thermal floods. Employing lumped-parameter system analysis and assuming series flow, this paper presents transfer functions that characterize response times of a reservoir and its surroundings to changes in temperature of the heat source. The reservoir-surrounding system is modelled as individual thermal capacitors and resistors. The transfer functions, solved for a step disturbance, describe the limiting case of negligible interaction between these subsystems. For a step-change in temperature of the heat source, responses of the reservoir and surroundings are simulated for some combinations of their properties. Simulation results explain time-delay between reservoir and surrounding temperatures. The time-delay is controlled by four distinct parameters vis-à-vis surroundings time constant (τr), reservoir time constant (τa), ratio of thermal resistances (R) as well as the ratio of conductive to convective heat flow (βr). These parameters are governed by petrophysical, transport and thermophysical properties of the heating medium, reservoir, and surrounding formation. It is shown that lag-time in thermal responses of reservoir and surroundings can range from few weeks to several years. For practical applications and analyses, these results provide insights into conditions under which a thermal flood may be approximated as adiabatic vs. non-adiabatic.
+
+
+
+
+Keywords:
+reservoir simulation,
+reservoir,
+reservoir surveillance,
+enhanced recovery,
+upstream oil & gas,
+sagd,
+thermal method,
+timescale,
+reservoir characterization,
+petroleum reservoir
+
+
+Subjects: 
+Well & Reservoir Surveillance and Monitoring,
+Reservoir Characterization,
+Improved and Enhanced Recovery,
+Reservoir Simulation,
+Formation Evaluation & Management,
+Information Management and Systems,
+Thermal methods
+
+
+
+
+Introduction
+
+
+Petroleum reservoirs are bounded laterally and vertically by other geologic systems that are also saturated with fluids, which may or may not be the same as that contained in the subject reservoir. Even within the reservoir, fluids immediately underneath (say, gas) the caprock and above (say, water) the base rock may differ from that being targeted (heavy oil). Therefore, it is imperative that any realistic evaluation of heat exchange between a specific reservoir and its adjoining rocks accounts for all these rock elements, their saturating fluids and relevant petrophysical, transport and thermophysical properties.
+
+
+Heat exchange between a petroleum reservoir and its surrounding formations is a common occurrence in thermal floods. In general, the net loss of heat from the reservoir to the overburden, underburden and other adjoining rocks is detrimental to the thermal efficiency and overall performance of thermal flood (Lawal 2020, Doan et al. 2019, Zargar and Farouq Ali 2017a, b).
+
+
+Estimates of potential heat losses to the surrounding rocks are required for proper design and management of thermal floods. Accomplishing this task in practice is computationally intensive. One approach to solving this problem requires a detailed numerical method (Cho et al. 2015, Lawal 2011, Hansamuit 1992)]. This detailed method requires that the overburden and underburden are described explicitly by grid blocks, which extend far above and below the subject reservoir. In most thermal simulators, a large fraction of the grid blocks is defined as inactive for fluid flow, while these same blocks remain active to describe heat flow within the formation. Although this treatment often yields high accuracy, the additional computational costs in relation to the associated incremental value is a concern. As a result, this method is less attractive for screening studies (Lawal 2020).
+
+
+To simply the estimation of thermal losses across boundaries, some workers have demonstrated the applicability of the heat-exchanger theory (Lawal 2020). Unlike some methods such as the semi-analytical formulation by Vinsome and Westerveld (1980), the former treatment explicitly accounts for key interface (reservoir-overburden and reservoir-underburden) properties such as thermal conductivity, thickness, and heat capacity as well as relevant thermal properties of the adjacent formations and their saturating fluids. In essence, the technique introduced by Lawal (2020) considers reservoir-overburden interface wall, the reservoir-underburden wall as well as the overburden and underburden systems as thermal resistors in series.
+
+
+Regardless of the method used to estimate heat losses to adjoining rocks, it is useful to determine the need (or otherwise) for a rigorous assessment of potential subsurface thermal losses that would occur during the operating lifetime of the project. To address such question, one needs to understand the timescales of temperature responses of the directly heated reservoir and the surrounding formations, which are heated indirectly via the reservoir. A comparison of such timescales against the anticipated project lifetime would justify the need to invoke rigorous evaluation methods at the screening stage of the study.
+
+
+In thermal floods, the reservoir hosts the primary heat source such as steam, hot water and electromagnetic heaters (Sharma et al. 2021, Wang et al. 2019, Abraham et al. 2016, Lawal and Tendo 2015). Assuming that the reservoir is bounded by competent rocks in the vertical and lateral directions, heat transport from the hot reservoir to the colder surrounding formations is limited to conduction. Because the surroundings do not receive heat directly from the primary heat source, one would expect some time-delay in the thermal response of the surroundings in relation to the reservoir. But how large is this time-delay and how could this affect the performance of a thermal flood?
+
+
+Typically, the active lifetime of a thermal flood is equivalent to some 20 - 30 years of continuous heating. To engender proper field management, it would be helpful to evaluate the time-lag in responses of the reservoir and its adjoining rocks to a perturbation in the temperature of the primary heat source. Such understanding is relevant in determining the appropriate boundary conditions to be used to describe the thermal flood in question. Examples of boundary conditions that may be influenced by this knowledge are adiabatic vs. non-adiabatic description.
+
+
+Covington et al. (2011) investigated the mechanisms of heat exchange between water in a conduit and its surrounding rocks. Although the system that they examined was not a thermal flood in a petroleum reservoir, their theoretical work and field data provide useful insights into the timescale of heat exchange in their example karst system. Their results underscore conduction as a key mechanism in explaining the time-lag between a heated system and its surrounding porous medium.
+
+
+On the assumption that heat flows in series from the source through the reservoir to its surrounding formations, this paper employs lumped-parameter system analysis to investigate timescales of temperature responses of the petroleum reservoir and its adjoining formations to temperature perturbations during a thermal flood. The reservoir-surrounding system is described as sub-systems of individual thermal capacitors and resistors. Separate transfer functions are presented and solved for the limiting scenario of non-interacting reservoir and surroundings. For simplicity, solutions are provided for the case of a step-change in temperature of the heating medium, however the same procedure can readily be applied to evaluate other forms of input signals.
+
+
+Model formulation and solution
+
+
+For convenience, the entire system of petroleum reservoir, boundaries and the surrounding formations is described as a lumped-parameter system. As a result, the sub-systems of reservoir and surroundings are modelled as individual thermal capacitors, which have thermal resistors and are in series (Fig. 1). To quantify the full range of dynamics, two limiting scenarios of interactions between the reservoir and surrounding formations are considered.
+
+
+Figure 1View largeDownload slide Lumped model of injector-reservoir-surroundings systemFigure 1View largeDownload slide Lumped model of injector-reservoir-surroundings system Close modal
+
+
+Reservoir and surroundings interact: In this case, the reservoir and its surroundings have stronger influence on one another. In essence, heat flux from reservoir to surroundings via the intervening layer is sensitive to the difference in average temperatures in the reservoir and surroundings at any instant. This implies that the surrounding loads the reservoir.
+
+
+Reservoir and surroundings are non-interacting: In this case, reservoir dynamics affect the surroundings, but the converse is not true. Heat flux from reservoir to surroundings through the reservoir-surrounding intervening layer is influenced primarily by the average temperature in the reservoir at that instant. Temperature transients in the reservoir are largely insensitive to temperature changes in surrounding rocks, which serve as heat sinks. We assume that the downstream subsystem (surrounding) does not "load" its upstream counterpart i.e., petroleum reservoir (Marlin 1995, Coughanowr 1991).
+
+
+However, for simplicity, we limit the modelling to non-interacting systems. In line with the general understanding that a non-interacting system yields a faster response than corresponding interacting system (Marlin 1995, Coughanowr 1991), insights gained from this work should provide lower bounds of timescales of heat transfer between a reservoir and its adjoining formation, which often exhibit some interactions in most realistic thermal floods.
+
+
+The following equations describe the lumped-parameter heat balance for the reservoir and the surrounding formations. The formulation of these equations assumes that (i) heat inflow to the reservoir is convective, while heat outflow to the adjacent formation is conductive; (ii) heat outflow from the surroundings to an imaginary ultimate sink is conductive; and (iii) injection plane, reservoir and surrounding rocks have same cross-sectional area.
+
+
+ρfcpfAvfTf=ρrcprAhrϕrdTrdt+κpATrzp(1)
+
+
+κpATrzp=ρacpaAhaϕadTadt+κuATazu(2)
+
+
+where subscripts f, r and a refer to the heating fluid, reservoir, and surrounding formation, respectively. ρ is density, cp is specific heat capacity (J kg−1 K−1), T is average temperature (K),h is net formation thickness (m), A is cross-sectional area (m2), κ is thermal conductivity (W m−1 K−1), z is thickness of interface layer in the direction of heat flow (m), t is time (s), ϕ is porosity (fraction). T∞ is the average temperature of the ultimate heat sink. Subscript p refers to the reservoir-surrounding formation boundary layer, while u represents the interface between the surrounding formation and another adjacent formation, which is considered the ultimate heat sink in this work.
+
+
+Solution for Tr: Eq. 1 can be solved for the temperature response of the reservoir by applying Laplace transform to obtain the following. Note that the variables Tf and Tr are written in their deviation forms i.e., change from steady state, hence the quantities T'f and T'f⁠.
+
+
+T'f(s)=τrsT'r(s)+βrT'r(s)(3)
+
+
+Eq. 3 can be manipulated to yield the following transfer function relating how average reservoir temperature responds to a change in heating fluid temperature in Laplace domain.
+
+
+T'r(s)T'f(s)=1τrs+βr(4)
+
+
+The following definitions have been introduced to make the foregoing expressions appear elegant. Rr and τr are the thermal resistance (m2 K W−1) and time-constant (s) of the reservoir, respectively. βr is the ratio of conductive heat outflow from the reservoir to convective heat inflow into the same reservoir. The quantity 1/βr is the steady-state gain of the transfer function described by Eq. 4.
+
+
+Rr=zpκp(5)
+
+
+τr=ρrcprhrϕrρfcpfvf(6)
+
+
+βr=1ρfcpfvfRr(7)
+
+
+For a step change in the heating fluid temperature, the corresponding change in the average reservoir temperature in Laplace domain is given by
+
+
+T′r(S)=1s(τrs+βr).(8)
+
+
+By the application of Laplace inversion, we obtain the following expression for the response of T'r(t) to a step change in T'f(t).
+
+
+Tr'(t)=1βr1-e-βr tτr.(9)
+
+
+Solution for Ta: Following from the procedure applied to solve for Tr, Eq. 2 can be solved for the temperature response of the surrounding formation. Hence, the following is the equivalent of Eq. 2 in Laplace domain. Ra and τa are the thermal resistance (m2 K W−1) and time-constant (s) of the surrounding formation, respectively. R is the ratio of reservoir thermal resistance to that of the surrounding porous medium.
+
+
+T'r(s)=τasT'a(s)+RT'a(s),(10)
+
+
+where
+
+
+τa=Rrρacpahaϕa,(11)
+
+
+R=RrRa,(12)
+
+
+Ra=zuκu.(13)
+
+
+From Eq. 10, we derive the following transfer function relating T'a and T'r⁠, where the latter is only an intermediate disturbance to the former.
+
+
+T'a(s)T'r(s)=1τas+R(14)
+
+
+For practical applications, we need a transfer function that connects T'r to the originating disturbance T'f⁠, which induces changes to temperatures of both reservoir and its adjacent formation. This transfer function is derived by combining Eqs. 15, 14 and 4 to obtain Eq. 16.
+
+
+T'a(s)T'f(s)=T'a(s)T'r(s)xT'r(s)T'f(s)(15)
+
+
+T'a(s)T'f(s)=1(τas+R)(τrs+βr)(16)
+
+
+A comparison of the transfer functions in Eqs. 4 and 16 indicates that the dynamics of surroundings is second order, while the reservoir is first order. As a result of any change in the behaviour of the heating medium, the response of the surrounding is expected to lag that of the reservoir. 1/Rβr is the steady-state gain of the transfer function described by Eq. 16.
+
+
+Again, for a step change in T'f(t), we apply Laplace inversion to derive the following expression for the response of T'a(t).
+
+
+T'a(t)=1Rβr{1+(τaτrτrR−τaβr)(βrτre−Rtτa−Rτae−βrtτr)}(17)
+
+
+where τr≠0τa≠0⁠, and (τrR−τaβr )≠0⁠.
+
+
+From a review of the underlying transfer functions as well as Eqs. 9 and 17, it is clear that parameters τr, τa, R and βr are sufficient to fully characterize the magnitudes and timescales of responses of the reservoir and adjacent rocks to perturbations in the heating medium.
+
+
+Simulation examples
+
+
+We simulate different numerical states of the parameters τr, τa, R and βr. Input dataset for the reference case is given in Table 1. With the objective of assessing the full range of responses for the examples under consideration, sensitivity tests are conducted on the four characterizing parameters individually. In principle, insights gained from these parametric tests should aid the design and management of thermal floods.
+
+
+Table 1Input data for the reference-case simulation τa (s)
+            . τr (s)
+            . βr
+            . R
+            . 8.64 × 107 8.64 × 105 1.0 1.0 τa (s)
+            . τr (s)
+            . βr
+            . R
+            . 8.64 × 107 8.64 × 105 1.0 1.0 View Large
+
+
+Results and discussion
+
+
+Fig. 2 (a-b) displays the transient responses of the reservoir and surrounding temperatures to a unit-step change in heating-fluid temperature. It should be emphasized that these responses refer to changes in the corresponding output from their respective prior steady states. In Fig. 2a, where τa/τr=100.0⁠, T'r reaches its new steady state in about 80 days after a step change in T'f⁠. In comparison, T'a remains largely unresponsive within this first 80-day period. From these results, T'a does not reach its new steady state until about 5,500 days after the heating-fluid temperature perturbation was introduced.
+
+
+Figure 2View largeDownload slide Step responses of reservoir and surroundings (effects of τa)Figure 2View largeDownload slide Step responses of reservoir and surroundings (effects of τa) Close modal
+
+
+Fig. 2b shows the dynamics in the case of an order-of-magnitude reduction in τa, while other parameters are kept constant. With a significant reduction in τa (i.e., τa/τr=10.0⁠), a sharp improvement in the response speed of T'a is observed, but the dynamics of T'r remain unchanged. Following a close evaluation of Figs. 2a and 2b, the ratio τa/τr appears to have major influence on the timescales, hence transfer lag, of T'r and T'a responses. The results in Fig. 2 are consistent with the expected asymptotic behaviours and steady states of the output.
+
+
+For the same step change in T'f⁠, transient behaviours of T'r and T'a are shown in Fig. 3 for sensitivities conducted on τr. In Fig. 3a, the case of τa/τr=0.1 yields behaviours of T'r and T'a that are generally comparable. Although not readily visible on the current display, a close examination of the early-time profiles in Fig. 3a would reveal a time-delay of about 320 days before T'r attains 20% of its final stabilized value. This response time is about 100 days slower than that taken by T'r to reach the same 20% state. The maximum response speed of the leading subsystem (reservoir) occurs at time t = 0, while the minimum response of the lagging subsystem (surroundings) is observed at the same time t = 0.
+
+
+All the results in Fig. 3 assume that the time-constant of the reservoir is 10% of the surrounding's. This suggests that, if they were to be considered as individual first-order systems, the surrounding should exhibit a much faster response than the reservoir under the same conditions. However, as an integrated system, the reservoir's behaviour is first order while the surrounding is second order. Accordingly, even if the surrounding is characterized by much lower time-constant on its own, its response will always lag that of the reservoir through which any perturbation on the heating fluid is transported to the surroundings. This inherent transfer lag explains the sluggishness of the surrounding relative to the reservoir in the cases presented in Fig. 3. The importance of reservoir characteristics to the dynamics of surrounding rock is underscored by Figs. 2b and 3b. Although these plots are based on same values of τa, R, and βr, reducing the ratio τa/τr by a factor of 1,000 prolongs the steady-state time of the lagging T’a by a factor of 10 i.e., from ~5,500 to ~54,000 days.
+
+
+Figure 3View largeDownload slide Step responses of reservoir and surroundings (effects of τr)Figure 3View largeDownload slide Step responses of reservoir and surroundings (effects of τr) Close modal
+
+
+While keeping τa, τr, and βr at the reference states, Figs. 4a and 4b are graphical displays of the simulated transient behaviours for R = 0.1 and 10.0, respectively. At R = 0.1, in which the thermal resistance of the reservoir-surrounding interface is just 10% of the corresponding resistance of the surrounding-ultimate sink boundary, response of T'a is much faster than the case of R = 10.0. However, despite a two-order of magnitude difference in the R values, speed of response in T'r does not exhibit significant difference in the two cases of R. Within the parameter space explored, these results suggest that the parameter R has strong effects on the transient response of T'a⁠, but not quite on that of T'r⁠.
+
+
+Figure 4View largeDownload slide Step responses of reservoir and surroundings (effects of R)Figure 4View largeDownload slide Step responses of reservoir and surroundings (effects of R) Close modal
+
+
+Figs. 5a and 5b are the step responses of T'r and T'a to βr states of 0.1 and 10.0, while keeping the other parameters at their reference states. Again, the results are consistent with the behaviours seen in the earlier parametric tests. Specifically, T'a response remains slower than the corresponding T'r response. Furthermore, the quantities 1/βr and 1/Rβr remain the primary controls on the final stabilized states of T'r and T'a⁠, respectively.
+
+
+Figure 5View largeDownload slide Step responses of reservoir and surroundings (effects of βr)Figure 5View largeDownload slide Step responses of reservoir and surroundings (effects of βr) Close modal
+
+
+Although τa/τr ratio influences the dynamics of T'a and T'r⁠, it is noteworthy that these dynamics are also sensitive to the absolute values of τa and τr. This point is reinforced with the cases in Table 2. Fig. 6 compares the times for T'a and T'r to reach their respective steady-state responses to a step change in input signal for these three cases characterized by the same τa/τr=5.0⁠, βr = 1.0 and R = 1.0, but different magnitudes of τa and τr.
+
+
+Figure 6View largeDownload slide Estimated times for T'r and T'a to reach steady states for the cases in Table 2 Figure 6View largeDownload slide Estimated times for T'r and T'a to reach steady states for the cases in Table 2  Close modal
+
+
+Table 2Input data for additional sensitivity tests Case
+            . τa (s)
+            . τr (s)
+            . τa/τr
+            . τr
+            . R
+            . A 1.0 × 107 2.0 × 106    B 4.3 × 106 8.6 × 105 5.0 1.0 1.0 C 2.5 × 106 5.0 × 105    Case
+            . τa (s)
+            . τr (s)
+            . τa/τr
+            . τr
+            . R
+            . A 1.0 × 107 2.0 × 106    B 4.3 × 106 8.6 × 105 5.0 1.0 1.0 C 2.5 × 106 5.0 × 105    View Large
+
+
+Conclusion
+
+
+A lumped-parameter heat balance model has been derived and solved to describe temperature behaviour of a non-interacting reservoir-surrounding system in a thermal flood. It is shown that a time-delay (transfer lag) exists between the response of a reservoir and its surrounding formation during a thermal flood. The dynamics of both subsystems and their transfer lag are fully characterized by the parameters τr, τa, R and βr. These parameters are functions of the petrophysical, transport and thermophysical properties of the heating medium, reservoir, and the adjoining formation.
+
+
+The reservoir exhibits first-order behaviour; hence it does not have a transfer lag. In essence, the maximum rate of change of the reservoir response occurs immediately (at t = 0) after the step change is induced. Conversely, the surrounding is characterized by a second-order behaviour and has a transfer lag, hence the slope of its response curve is always minimal at t = 0. It is worthy of note that neither the reservoir nor the surroundings exhibit oscillatory responses.
+
+
+In addition, though both the reservoir and surrounding temperatures respond asymptotically to a perturbation in the heating fluid, the steady states of these responses are predictable and limited to the numerical values of the quantities 1/βr and 1/Rβr respectively. In essence, though the surrounding always lags the reservoir in terms of response speed, it is possible to have the surrounding achieve a steady-state response that exceeds that of the reservoir under the limiting scenario of negligible interaction between these subsystems.
+
+
+For proper design and management of thermal floods, it is crucial to have a good understanding of the relative impacts of τr, τa, R and βr on the overall system dynamics on a case-by-case basis. Given the cumbersome and expensive nature of thermal-simulation projects, such understanding would be relevant in evaluating the requirements to include the adjacent formations in the scope of detailed thermal numerical-simulation models.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion cpspecific-heat capacity, J kg−1 K−1 hanet thickness of surrounding formation, m hrnet reservoir thickness, m Rratio of Rr to Ra, dimensionless Rathermal resistance of surrounding-ultimate sink boundary, m2 K W−1 Rrthermal resistance of reservoir-surrounding boundary, m2 K W−1 rr/aratio of reservoir response time to that of surroundings, dimensionless T’temperature in deviation form, K Tasurrounding temperature, K Tfinjection (heat source) temperature, K Trreservoir temperature, K vfinflow velocity of heating fluid, m s−1 zpthickness of injector-reservoir boundary, m zuthickness of caprock (reservoir-surrounding boundary), m βrratio of conductive heat outflow to convective heat inflow into the reservoir, dimensionless κpthermal-conductivity of injector-reservoir boundary, W m−1 K−1 κuthermal-conductivity of caprock (reservoir-surrounding boundary), W m−1 K−1 ϕaporosity of surrounding formation, fraction ϕrreservoir porosity, fraction ρabulk density of surrounding formation, kg m−3 ρrreservoir bulk density, kg m−3 τasurroundings time constant, s τrreservoir time constant, s
+
+
+References
+
+
+AbrahamT, AfacanA, DhandhariaP, ThundatT (2016). "Conduction and dielectric relaxation mechanisms in Athabasca oil sands with application to electrical heating", Energy Fuels30, 5630–5642.Google ScholarCrossrefSearch ADS  ChoJ, AugustineC and ZerpaLE (2015). "Validation of a numerical reservoir model of sedimentary geothermal systems using analytical models". Paper SGP-TR-204 presented at 40th workshop on geothermal reservoir engineering, Stanford Univ., Stanford, 26-28 Jan.Google Scholar CoughanowrDR (1991). Process Systems Analysis and Control, 2nd ed., McGraw-Hill Inc., New York.Google Scholar CovingtonMD, LuhmannAJ, GabrovšekF, SaarMO and WicksCM (2011). "Mechanisms of heat exchange between water and rock in karst conduits", Water Resources Research47, W10514, 1–18.Google ScholarCrossrefSearch ADS  DoanQT, FarouqAli SM and TanTB (2019). "SAGD performance variability – analysis of actual production data for 28 Athabasca oil sands well pairs". SPE paper 195348 presented at SPE Western Regional Meeting, San Jose, 23-26 Apr.Google Scholar HansamuitV, Abou-KassemJH and Farouq AliSM (1992). "Heat loss calculation in thermal simulation", Transp. Porous Med. 8, 2, 149–166.Google ScholarCrossrefSearch ADS  MarlinTE (1995). Process Control: Designing Processes and Control Systems for Dynamic Performance, McGraw-Hill Inc., New York.Google Scholar SharmaJ, DeanJ, AljaberiF, AltememeeFN (2021). "In-situ combustion in Bellevue field in Louisiana – History, current state and future strategies", Fuel284, 118992.Google ScholarCrossrefSearch ADS  VinsomePKW and WesterveldJ (1980). "A simple method for predicting cap and base rock heat losses in thermal reservoir simulators", J. Can. Pet. Tech. 19, 3, 87–90.Google ScholarCrossrefSearch ADS  WangZ, GaoD, DiaoB, TanL, ZhangW, LiuK (2019). "Comparative performance of electric heater vs. RF heating for heavy oil recovery", Applied Thermal Engineering160, 114105.Google ScholarCrossrefSearch ADS  ZargarZ and FarouqAli SM (2018). "Analytical modelling of steam chamber rise stage of steam-assisted gravity drainage (SAGD) process", Fuel233, 1, 732–742.Google Scholar LawalKA (2011). Alternating injection of steam and CO2 for thermal recovery of heavy oil. PhD dissertation, Imperial College London.Google Scholar LawalKA (2020). "Applicability of heat-exchanger theory to estimate heat losses to surrounding formations in a thermal flood", J Petrol Explor Prod Technol10, 1565–1574.Google ScholarCrossrefSearch ADS  LawalKA, TendoF (2015). "Steam-alternating-CO2 for heavy-oil recovery". SPE paper 178356 presented at SPE Nigeria Annual International Conference and Exhibition, Lagos, 4-6 Aug.Google Scholar ZargarZ and FarouqAli SM (2017). "Analytical treatment of steam-assisted gravity drainage: old and new", SPE J. 23, 1, 117–127.Google ScholarCrossrefSearch ADS  
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211976-MS
+
+
+

files/2022/Economic Advantages of Emerging Indigenous Participation in Exploration and Production Operations in the Oil Gas Industry.txt

@@ -0,0 +1,225 @@
+----- METADATA START -----
+Title: Economic Advantages of Emerging Indigenous Participation in Exploration and Production Operations in the Oil & Gas Industry
+Authors: Evelyn Bose Ekeinde, Adewale Dosunmu, Diepiriye Chenaboso Okujagu
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211930-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The study is a combination of survey and exploratory design. It utilized primary data (questionnaire) and secondary data (as journal, articles, industry reports and newspapers) with relative contents to the topic of discuss. The questionnaire was distributed to E&P workers in three LOCs, SEPLAT, Famfa oil and Yinka Folawiyo Petroleum. IOCs respondents from Shell, Total and Exon Mobil. Ten 10 questionnaires were distributed to each making 60 and 51 questionnaires retrieved and completed. The data gathered was presented using tables and analyzed using simple percentages, frequency and mean. The study concluded that participation of indigenous companies in exploration and production activities in the country has several economic advantages which both individuals and the government can benefit from. This includes increased production (barrels), increased Gross Domestic Product of the nation, job creation/ reduction of unemployment in the country and improved human resources due to training of indigenous worker. It recommended that the local content policy of 2010 should be taken more seriously by the government and more indigenous companies should be encouraged to go into oil exploration and production to increase the availability of crude oil products in the market which will automatically lead to better GDP, reduction in capital flight and increase in individual company income.
+
+
+
+
+Keywords:
+strategic planning and management,
+indigenous participation,
+upstream oil & gas,
+participation,
+questionnaire,
+asset and portfolio management,
+emerging indigenous participation,
+contractor,
+indigene,
+production operation
+
+
+Subjects: 
+Asset and Portfolio Management,
+Strategic Planning and Management,
+Professionalism, Training, and Education,
+Exploration and appraisal strategies
+
+
+
+
+Introduction
+
+
+Around 90% (90%) of Nigeria's annual federal government revenue is derived from the crude oil and natural gas (CONG) business, but only 38% of the country's GDP is generated by this sector (GDP). While exploration and production have traditionally been viewed as a "reserve economy," they have really had little impact on the rest of society. In the first three months of 2015, the oil industry accounted for 74.4 percent of Nigeria's exports and generated 14.2 billion dollars in income, according to the Nigeria National Bureau of Statistics (2015b). More than three-quarters of the country's gross domestic product is generated by this source of revenue (GDP). There has been little impact on the country's economy despite the country's abundant oil resources due to the fact that foreign oil firms handle a large amount of the sector's activity (Ihua, et al., 2013). Petroleum exploration and production in Brazil, Indonesia, Norway, and Venezuela is optimised by utilising local resources and capabilities to their full potential. Because they began oil exploration and production later than Nigeria, these countries’ attempts to develop local content in this critical industry have been tremendously successful, a task that Nigeria faces after more than four decades of oil exploration and production. Nigeria remains highly ranked in terms of poverty and technological backwardness in the world in spite of her growing profile and wealth as one of the highly gifted oilfield country. This is ultimately owing to the wealth not yet transforming to better welfare (Atakpu, 2013). One reason for this is that in excess of 90% of the annual Exploration and Production industry outflows escape the national economy as capital flight. The emergence of indigenous oil industry owing to the enacted 2010 Local content policy has since seen a change in the order of things, with the vast majority of the population now enjoying the economic benefit of the oil resource (Bello, 2010). In context, Indigenous E & P industry is an industry which is registered in Nigeria with Nigerians holding 60 to 100% of the shares, and possessing appropriate expertise, couple with financial sustainability, and ability to operate, it has shown to be a significant contributor to economic growth. Since 2010, the sector has generated at least 30,000 direct and indirect jobs in Nigeria (Ebiri, 2012). Numerous indigenous exploration and production companies, such as Seplat, have significantly influenced the development of indigenous content on their properties. Adeola's given name is Adeola (2018). Currently, over 80% of senior management positions in the company are held by Nigerians, while 99 percent of the company's overall staff is made up of Nigerians. In compliance with the provisions of the NOGICD Act, 98 percent of Seplat's subcontractors are Nigerian firms (Adepetun, 2010). In the previous three years alone, Nigerian enterprises have received contracts totalling $1 billion. Indigenous Participation makes worthy Business Intelligence – the use of highly trained Human Resources will eventually lead to lesser operating costs and hence enhanced cost-effectiveness for the E & P industry's assets. Industries’ bottom line will without doubt become better in the long run and are also able to engender the trust and good will in their areas of operations, what also transforms to long term value-added profitability. Coker (2008), Nigeria aspiration is to set apart a considerable sum of the average Eighteen (18) billion dollars’ yearly exploration and production (E&P) expenditure and halt the flow of capital flight that has rendered her an inferior partner in her joint venture arrangements with international oil companies over the centuries (IOCs). If this aspiration is to be attained, indigenous participation must further be strengthened by the arms of the local content policy.
+
+
+The study targets to inspect the benefits of the emerging indigenous Exploration and production industries on Nigeria economy.
+
+
+Background of study
+
+
+Conceptual framework
+
+
+The development of local content laws has given way for indigenous oil and gas exploration and production companies to emerge. Without the implementation of the Local Content Act, companies like SEPLAT, Savannah Energy Plc., Network Exploration & Production, Frontier Oil, Aieteo Eastern Exploration & Production would not be in existence today.
+
+
+Olorunshola, (2010) views local content as any organisation having a non-temporal functioning administrative centre in a specific geographical region. It has been defined based on the worth it gives to the country's’ economy by patronising locally produced goods and services. "Local value-added" is the fortune indigenous organisations accrue as a result of transforming raw materials to completed goods and generating revenue from internal services rendered (Esho, 2006). In computing it, the formula is derived as ‘the organisations production worth take away every product or service not purchased within the country (comprised of raw materials, energy, contractor services, and rents). According to the International Petroleum Industry Environmental Conservation Association (IPIECA), local content is defined as the additional benefit brought to a host country (or area or locality) through the development of indigenous employees in various industries and the funding of supplier development (improving and purchasing indigenous materials and facilities).
+
+
+Indigenous oil exploration companies are upstream organisation springing up within the nation and utilising indigenous employees and resources to enable exploration and production of oil (Akinpelu, Omole, and Falode, 2010). Indigenous oil exploration and production companies are owned completely by Nigerians / the Nigerian state.
+
+
+Emerging Indigenous Oil Exploration Companies
+
+
+Emerging indigenous oil companies are those who are (i) recorded to have 60-100% shareholding in Nigeria (ii) have adequate knowledge and funding abilities and (iii) been able to add to the growth of the nation where it is situated (Nwasike, 2002). Nevertheless, selected individuals views, indigenous E&P companies as the ‘the opposite of international oil companies like Shell, Chevron, Mobil, Addax Petroleum, Conoco Energy and so forth’ (Tilije, 2002). Disparate international oil companies, local suppliers of emerging indigenous oil E & P companies regrettably currently do not have a high equity-base, which is usually controlled by its owner and do not have the ability of meeting the huge monetary burden which it involves.
+
+
+Aneke (2012) never the less in his opinion suggested that small and medium organisations embrace the benefits which the privilege in E&P industry provides thus increasing the value added to the county through local content policies.
+
+
+Benefits of Emerging Oil Exploration and Production Companies to the Nigerian Economy
+
+
+Indigenous local entrepreneurs’ participation in important monetary and production-related activities such as exploration and mining benefits to a nation's advancement and helps sustain its economy (Nwosu, et. al., 2006).
+
+
+The oil and gas industry significantly contributes to the nation's economy, accounting for more than 90% of foreign exchange earnings from the sale of crude oil. Continuous involvement and contribution activities of rising indigenous E&P enterprises can help increase the local economy by creating jobs, improving social amenities, and raising the standard of living (Nwosu, et.al.2006). This breakthrough significantly reduces capital flight; instead of paying expatriates, supporting training and development programmes outside Nigeria, purchasing equipment from other countries, and importing software and data-monitoring systems used by the business (Agusto, 2009). Indigenous Participation is a wise business strategy –utilizing resources grown and produced locally especially well trained human resources will enhance lesser purchase and running cost thus increasing the profit made. The main point is that firms will unarguably get better in the long run. Services rendered by organizations can be tested and trusted leading to increased income (Akinpelu and Falode, 2010).
+
+
+Challenges Faced by Indigenous E&P Companies
+
+
+There is a notion in Nigeria that financing E&P by local companies involves very large capital and high technology which discourages indigenous contractors from delving into the industry (Nwosu, et al, 2006). According to the majority of major international oil firms, ‘indigenous companies (and individuals) do poorly’ (Tilije, 2002). This low performance rate leads to (i) lesser opportunities presented to indigenous entrepreneurs (ii) Multinationals get more contracts and this increases their earnings which flows out of Nigeria (iii) Lack of job opportunities’ for citizens because the process has been seen to be insincere, non-transparent and compromise on the part of major stakeholders of the industry.
+
+
+Regardless of the large employment of local workers by indigenous companies, they face several challenges such as agitation by the youths, conflicts, disruptions, kidnapping, and stealing of tools used for work. This can however not be compared to the level of up heal experienced by multinationals from indigenes of the region (Nwasike, 2002). The above mentioned disadvantages brings about delay in implementation of planned industrial activities especially in the Niger Delta. This is often caused by inequity in distribution of national resources, environmental degradation and oil pollution. The emergence of indigenous E&P companies is also challenged by lack of a stimulating governmental supervisory structure, insufficient monetary records, especially audited accounts, dishonesty and maladministration or hazy accountability, constantly changing unstable political and economic environments in Nigeria.
+
+
+Through education and training, indigenous E&P enterprises may be better managed and updated; local content in the E&P industry would benefit the Nigerian economy significantly. It would be an excellent technique of empowering local contractors and might serve as a motivating factor for the industry's necessary advancements (Nwasike, 2002). According to (Nwosu, et al, 2006), emerging indigenous E&P companies can overcome the challenge of capital by:
+
+
+Consortium financing: a group of financial institutions led by an agent bank that comes together to provide the required funding. Term loan: a single loan for a specified length of time or a series of loans at specified dates.Credit line: a one-year loan that must be repaid or renewed.Advances: Loans of up to 90 days with a maximum term of 90 days.Overdrafts: cheques on funds that don't exist are honoured by banks in this situation.Invoice discounting: nonrecourse sale of receivables to a financial institution.Equipment leasing: the proprietor of a particular equipment enters an agreement with the lesee in a bid to overcome the monetary burden of associated with purchasing capital intensive equipment's used in the oil industry.
+
+
+Overview of Nigeria's exploration and production activity
+
+
+In order to boost the value-added, it appears that all stakeholders are paying attention to the necessity for resource-rich Nigeria to take control of oil and gas exploration, exploitation, and production activities and to harness the potential of this most tactical industry. The inability of Nigeria to utilise the materials wealth at her disposal to advance the nation and eradicate poverty has been a major issue facing administrations regardless the 37 billion barrels in her reserve and more than forty years in the oil and gas exploration and production industry (Balouga, 2012).
+
+
+Since 1960, wealth derived from oil production has aided economic growth in Nigeria. In 1970, total oil export revenue was $718 million. When crude oil was sold at roughly US $90 a barrel in 2012, revenues climbed from US$47.9 billion in 2005 to US$94.6 billion in 2012. (OPEC, 2013). Despite this, the country's oil reserves have had little impact on the general well-being of its citizens because a large number of industrial events, primarily service contracts, are handled by foreign oil companies (Ihua et al., 2011).
+
+
+It's widely held that the oil industry's capital and technology-intensive activities, particularly those of the newest oil-producing nations, are detrimental to their economies (Ovadia, 2013; Heum et al., 2003). A UNCTAD report (2010) stated that employment can be generated by the oil sector by permitting participation of more indigenous companies. According to the report thee economy can be transformed as a result. In a bid to solve the problem, LC policy was put in place in 2001 and 10 years later, passed into law. It is structured in a manner that improves the abilities of indigenous companies and giving them more leverage to partake in oil and gas transactions. The federal government of Nigeria aimed to introduce 45% local content in 2007 and in 2010 raise it to 70% (Ihua et. al., 2011). Forward and backward connections are anticipated to be created by the policy in the area of purchase and use of input materials made within the country which ensures that jobs are made available (Esteves et al., 2013; Ihua et al, 2011). The government yearly funding in the oil industry has risen from US$8billion to about US$20billion simply by making activities oil and gas trading bigger to accommodate local companies (Ovadia, 2013; INTSOK, 2012, Ihua et. al., 2011). Intense involvement of the indigenous people and firms is needed to ensure investment in local companies continues over a long period of time. When policies as this is not put in place, privileges for investing is often capitalised majorly by foreign firms. The present amount of local companies’ investment in the oil and gas industry in Nigeria is 18 billion every year. This trend in investment will be in practice even after 2012. This is a proof that the creation of the support fund for local content came at the right time. Capital need to work by indigenous companies, a free industry to operate and financing that helps reduce production cost are all contained in the LC policy.
+
+
+Materials and method
+
+
+The paper is a mix of survey and exploratory study in terms of design. The study utilized both primary and secondary data. The study also utilized qualitative secondary data such as journal, articles, industry reports e.t.c. which have relative contents to the topic of discuss. The questionnaire was distributed to E&P workers in three LOCs, SEPLAT, Famfa oil and Yinka Folawiyo Petroleum. IOCs respondents from Shell, Total and Exon Mobile. Ten 10 questionnaires were distributed to each making 60 and 51 questionnaire retrieved and completed. Data were analysed using simple percentages, frequency and mean. Table were used to present the data gathered.
+
+
+Results and discussion
+
+
+Data from table 1 shows four (4) selected emerging Indigenous Exploration and Production (E&P) companies and the crude oil produced in barrel per day (bpd). It shows that Seplat Petroleum Development Company PLC contributes 47,163 barrels per day of oil to the Nigerian economy, with Famfa oil, Frontier oil limited and Yinka Folawiyo Petroleum producing 1601.6bpd, 1000bpd and 70,000bpd respectively.
+
+
+Table 1Selected emerging Indigenous E&P industry Indigenous E& P Industry
+            . Oil produced (bpd)
+            . Seplat Petroleum Development Company PLC 47,163 Famfa oil 1601.6 Frontier oil Limited 1000 Yinka Folawiyo Petroleum 70,000 Total 119764.6 Indigenous E& P Industry
+            . Oil produced (bpd)
+            . Seplat Petroleum Development Company PLC 47,163 Famfa oil 1601.6 Frontier oil Limited 1000 Yinka Folawiyo Petroleum 70,000 Total 119764.6 Source:Akinpelu, and Falode, 2010;Adeola, 2018. **bpd denotes barrel per dayView Large
+
+
+As seen from the mean scores in the table 2 above, both upstream and downstream workers are of the opinion that the enactment of Local content policy has greatly impacted on the Nigerian Economy as ‘more grounds are given to the indigenous Exploration and Production companies to operate’, ‘Job creation for the indigenes of Nigeria’, as well as ‘increased opportunity for indigenous E & P’. The table as well shows that more grounds are given to the indigenous Exploration and Production companies to operate’ had the highest mean set of 3.070, with ‘increased opportunity for indigenous E & P’ coming second with a mean set of 3.020, meanwhile, ‘Job creation for the indigenes of Nigeria’ is perceived to next impact of implementing the Local content policy in the upstream (Exploration and production) and downstream. Both upstream and downstream workers had low perception of the policy greatly impacting on ‘Reducing more than half of the country's unemployment problems’ and ‘also stimulated knowledge and technology transfers as well as capacity building’ as both had the lowest mean set that far below the Criterion, they both have mean set 2.040 and 2.090 respectively. As seen in the table 2, ‘Increased opportunities to indigenous E & P’ and ‘Reduced more than half of the country's unemployment problems’ mean are higher at the upstream as compared to the downstream, while the downstream had superior mean in comparison to upstream worker's perception in the following variables: ‘more grounds are given to the indigenous Exploration and Production companies to operate’, ‘Job creation for the indigenes of Nigeria’ and ‘Reducing unemployment by more than half, as well as stimulating the transfer of knowledge and technological capacity building.
+
+
+Table 2Impact of Local content on Nigerian Economy S/No
+            . Impact of LC policy on Nigerian Economy
+            . Upstream workers
+            . Downstream workers
+            . Mean set
+            . Criterion
+            . 
+            . 
+            . Mean
+            . SD
+            . Mean
+            . SD
+            . 1 More grounds are being given to ingenious E & P industry to operate. 2.980 1.690 3.150 1.740 3.070 2.500 2 Job Creation for the Indigenes 2.880 1.690 3.050 1.740 2.970 2.500 3 More nearly halved the unemployment rate in the country. 2.130 1.390 1.940 1.380 2.040 2.500 4 It also aided in the dissemination of information and the improvement of skills. 2.000 1.400 2.180 1.440 2.090 2.500 5 Increased opportunities for Indigenous E & P 3.080 1.750 2.960 1.740 3.020 2.500   13.070 7.920 13.280 8.040   S/No
+            . Impact of LC policy on Nigerian Economy
+            . Upstream workers
+            . Downstream workers
+            . Mean set
+            . Criterion
+            . 
+            . 
+            . Mean
+            . SD
+            . Mean
+            . SD
+            . 1 More grounds are being given to ingenious E & P industry to operate. 2.980 1.690 3.150 1.740 3.070 2.500 2 Job Creation for the Indigenes 2.880 1.690 3.050 1.740 2.970 2.500 3 More nearly halved the unemployment rate in the country. 2.130 1.390 1.940 1.380 2.040 2.500 4 It also aided in the dissemination of information and the improvement of skills. 2.000 1.400 2.180 1.440 2.090 2.500 5 Increased opportunities for Indigenous E & P 3.080 1.750 2.960 1.740 3.020 2.500   13.070 7.920 13.280 8.040   Source:19Ocheni, 2019**LC denotes Local content **SD is Standard DeviationView Large
+
+
+The general outcome from table 2 tells us that unemployment problems in the country has not been reduced by more than half, and also not enough stimulated knowledge and technology transfers and capacity building has been observed. Although it was observed that there are more than enough opportunities for indigenes to be key players in the E&P phase of the oilfields. While unemployment has not been cut to half in the country, ample jobs have been created for the indigenes to tap into.
+
+
+Table 3 shows the socio-demographic data of the selected International and indigenous oil companies for the survey on the economic advantages of indigenous participation in the Exploration and Production (E&P) industry. From the table 3, 88.2% of the respondents were male, with 11.8% of the respondents being female. The Age range with the highest distribution of respondents was (35-44) years having 39.2% of the total respondents, whereas age range (45-54) years have the lowest percentage of the respondents with only 15.7%. The survey sees 52.9% of the respondents being indigenous oil industry workers, while just 47.1% of the respondents being international oil industry workers. With regards to years of experience at their current companies, 62.7% representing the highest percent of the respondents have had (6-11) years of work experience at their current industry, and only 7.9% of the respondents have had above 17years of experience.
+
+
+Table 3Socio-demographic distribution of Respondent S/No
+            . Variables
+            . Frequency(F)
+            . Percentage (%)
+            .  Number of Participants = 51(100%) 1. Sex    Male 45 88.2  Female 6 11.8 2. Age Range (Years)    25-34 11 21.6  35-44 20 39.2  45-54 8 15.7  Above 54 12 23.5 3. Category of workers    International Oil company 24 47.1  Indigenous Oil company 27 52.9 4. Work experience (Years)    Less than 6 5 9.8  6-11 32 62.7  12-17 10 19.6  Above 17 4 7.9 Total 51 100 S/No
+            . Variables
+            . Frequency(F)
+            . Percentage (%)
+            .  Number of Participants = 51(100%) 1. Sex    Male 45 88.2  Female 6 11.8 2. Age Range (Years)    25-34 11 21.6  35-44 20 39.2  45-54 8 15.7  Above 54 12 23.5 3. Category of workers    International Oil company 24 47.1  Indigenous Oil company 27 52.9 4. Work experience (Years)    Less than 6 5 9.8  6-11 32 62.7  12-17 10 19.6  Above 17 4 7.9 Total 51 100 Source: Researcher's Survey, 2020View Large
+
+
+Table 4 shows the results of survey conducted on the impact of participation of emerging indigenous Exploration and Production (E&P) companies on Nigerian economy. On if ‘local content policy has positively affected indigenous E&P industry’, highest percentage of the respondents (45.0%) strongly agree with just 9.9% of the respondents strongly disagree. 33.3% and 31.4% of the respondents strongly agree and agree respectively to ‘number of barrels produced in the country increasing as a result of participation of indigenous E&P Companies’, while only 13.7% strongly disagree. Most of the respondents (39.1%) ‘Strongly agree’ that ‘various opportunities have been created as a result of indigenous E&P participation in the industry’, while 9.9% disagree. Highest member of the E&P workers (50.9%) recruited for this survey ‘strongly agree’ that ‘Nigerian Gross Domestic Profit (GDP) has experienced a rise as a result of indigenous participation in oil and gas E&P industry’, meanwhile, 3.2% ‘disagree’. The highest percentage of respondents (47.1%) ‘Strongly disagree’ that ‘Production cost has drastically reduced due to the employment of local contractors in E&P’ with 3.2% respondents representing lowest percentage ‘strongly agree’. Majority of the respondents representing 52.9% ‘strongly agree’ that ‘more jobs have been created and the human resource of the country has been improved upon due to training of indigenous workers’, while 1.9% of the respondents ‘strongly disagree’.
+
+
+Table 4Economic impact of participation of Indigenous Company in E&P S.no
+            . Questions
+            . Strongly Agree
+            . Agree
+            . Disagree
+            . Strongly Disagree
+            .   Number of Participants = 51(100%) 1. Local content policy has positively affected indigenous E & P industry. 23(45.0) 14(27.5) 9(17.6) 5(9.9) 2. The number of barrels produced in the country has been increased as a result of participation of indigenous E&P Companies. 17(33.3) 16(31.4) 11(21.6) 7(13.7) 3. Various opportunities have been created as a result of indigenous E&P participation in the industry. 20(39.1) 12(23.5) 5(9.9) 14(27.5) 4. Local participation in oil and gas exploration and production has enhanced Nigeria's GDP. 26(50.9) 18(35.3) 2(3.9) 5(9.9) 5. Production cost has drastically reduced due to the employment of local contractors in E&P. 2(3.9) 10(19.6) 15(29.4) 24(47.1) 6. More jobs have been created and the human resource of the country has been improved upon due to training of indigenous workers. 27(52.9) 18(35.3)  5(9.9) 1(1.9) Total 51(100) S.no
+            . Questions
+            . Strongly Agree
+            . Agree
+            . Disagree
+            . Strongly Disagree
+            .   Number of Participants = 51(100%) 1. Local content policy has positively affected indigenous E & P industry. 23(45.0) 14(27.5) 9(17.6) 5(9.9) 2. The number of barrels produced in the country has been increased as a result of participation of indigenous E&P Companies. 17(33.3) 16(31.4) 11(21.6) 7(13.7) 3. Various opportunities have been created as a result of indigenous E&P participation in the industry. 20(39.1) 12(23.5) 5(9.9) 14(27.5) 4. Local participation in oil and gas exploration and production has enhanced Nigeria's GDP. 26(50.9) 18(35.3) 2(3.9) 5(9.9) 5. Production cost has drastically reduced due to the employment of local contractors in E&P. 2(3.9) 10(19.6) 15(29.4) 24(47.1) 6. More jobs have been created and the human resource of the country has been improved upon due to training of indigenous workers. 27(52.9) 18(35.3)  5(9.9) 1(1.9) Total 51(100) Source: Researcher's Survey, 2020. **GDP means Gross Domestic ProfitView Large
+
+
+Discussion Of Results
+
+
+On the basis of the survey, it appears that the implementation of the 2010 local content policy has had a favourable impact on indigenous participation in the economy. Exploration and Production (E&P) industry that was greatly dominated by the international oil companies. There is increase in production of oil (barrel per day) owing to the emergence of indigenous companies participating in the Exploration and Production (E&P); from the table 1, we can see that only four (4) out of the emerging indigenous E&P companies contributes 119764.6bpd of crude oil on daily basis, confirming this part of findings from the survey. OPEC (2020), recent report from OPEC indicates that by 2021 there will be an increase in demand of crude oil, with this in mind it is very imperative that more indigenous company emerge to rise the production of oil in Nigeria. Numerous opportunities have been created as a result of indigenous E&P participation in the industry; this goes in tandem with findings from table 2. Due to indigenous participation in the E&P business, GDP has increased. This is a good sign that local content policy is working, and it could be because rising indigenous industries are regulating capital flight flow. However, despite all of the aforementioned beneficial impacts, the survey results suggest that there has been no significant reduction in the cost of crude oil production despite the use of local contractors for exploration. Lastly, job opportunities have been created for the indigenes due to the emergence of the indigenous companies, statistics from Seplat Petroleum Development Company PLC showed that more than 30,000 jobs were created with 99% of those job going to the indigenes; reducing unemployment which is one of Nigeria major problems.
+
+
+Conclusion and Recommendations
+
+
+From the examination conducted above and the result that has been shown, we therefore conclude that participation of indigenous companies in exploration and production activities in the country has several economic advantages which both individuals and the government can benefit from. This includes increased production (barrels), increased Gross Domestic Product of the nation, job creation/ reduction of unemployment in the country and improved human resources due to training of indigenous workers.
+
+
+The study further recommends the following:
+
+
+The local content policy of 2010 should be taken more seriously by the government and more indigenous companies should be encouraged to go into oil exploration and production to increase the availability of crude oil products in the market which will automatically lead to better GDP, reduction in capital flight and increase in individual company income.The government should make and implement friendlier policies that will help emerging indigenous companies thrive in the face of competition with International Oil Companies.The quality of products produced by emerging indigenous E&P companies must be checkmated and standardized to ensure that it meets the same quality produced by IOCs, this will ensure the safety of consumers and also increase consumers trust in the product being purchased thereby motivating them to purchase more from indigenous companies. This in the long run leads to increased profit.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Adeola. A. (2018). Local content development in the Nigeria oil and gas industry. Seplat Petroleum Development Company Plc Annual Report and Accounts 2018.Google Scholar Adepetun, S. (2010): "Nigerian Content Act: Thoughts for Consideration", Nigeria's Oil and Gas (NOG), August, pp. 24–27.Google Scholar Agusto, O. (2009). The Nigerian Downstream Oil Sector: a market study report, Conducted by Alliance Consulting.Google Scholar Akinpelu, L.O., Omole, O. A., and Falode, O. A (2010). Exploring Opportunities for Indigenous Participation in the Implementation of the Nigerian Gas Master Plan. SPE International.Google Scholar Aneke, P. (2012). The role of major operators in the development of local content in the Nigerian oil and gas industry. A paper delivered during a national seminar on the dynamics of equipment leasing and contract financing for local contractors in the oil and gas sector, Port Harcourt, Nigeria.Google Scholar Atakpu, L. (2013). Resource-based conflicts: Challenges of Oil Extraction in Nigeria; paper presented at the European Conference hosted by the German EU Council Presidency (March 29 and 30), Berlin, Germany.Google Scholar Ebiri, K. (2012): "Govt to Create 300,000 Jobs through Local Content Implementation". The Guardian newspaper, Thursday 28 June 2012.Google Scholar Esho, B. (2006). Local Content Policy, Best Thing to Happen to Oil and Gas Sector, The Sun Newspaper, November23.Google Scholar Esteves, M., Barclay, M., (2011). Enhancing the Benefits of Local Content: Integrating Social and Economic Impact Assessment into Procurement Strategies. Impact Assess. Proj. Apprais. 29 (3), 205–215.Google ScholarCrossrefSearch ADS  Heum, P., Quale, C., Karlsen, J.E., Kragha, M., and Osahon, G., (2003). Enhancement of Local Content in the Upstream Oil and Gas Industry in Nigeria: A Comprehensive and Viable Policy Approach. A Joint Study by Institute for Research in Economics and Business Administration, Rogaland Research and Kragha and Associates, April2003.Google Scholar Ihua, U.B., Olabowale, O.A., Eloji, K.N., Ajayi, C., (2011). Entrepreneurial implications of Nigeria's oil industry local content policy: perceptions from the Niger Delta Region. J. Enterp. Communities: People Places Glob. Econ. 5(3), 223–241INTSOK. 2012. Annual Report.Google ScholarCrossrefSearch ADS  Balouga, J. (2012). Nigerian local content: challenges and prospects. International Association for Energy Economics, Third Quarter, pp 23–26. Retrieved 20th September, 2014 fromhttps://www.iaee.org/en/publications/newsletterdl.aspx?id=176.Google Scholar Bello, O. (2010): "Local content: Firms risk losing over $5bn to lack of patronage", Business Day, 6September, pp.1, 4, 6.Google Scholar Coker, C. (2008): "Local content as the springboard for sustainable economic transformation of Nigeria", NOG, October, pp.28–29.Google Scholar INTSOK. (2012). Annual Report.Nigeria National Bureau of Statistics (NBS); 2015b. Foreign Trade Statistics for first Quarter of 2015.Nwasike, O.T. (2002): "Nigerian oil-industry: opportunities and challenges for local contractors", A paper presented at a 2-day seminar on the dynamics of equipment leasing and contract financing for local contractors in the oil and gas industry, Port Harcourt.Google Scholar Nwosu, H.U., Nwachukwu, I.O., OgajiS.O. T, and Probert, S.D. (2006). Local involvement in harnessing crude oil and natural gas in Nigeria. Applied Energy.Google Scholar Ocheni, S (2019). Local Content Policy, Best Thing to Happen to Oil and Gas Sector. Journal of Small Business and Entrepreneurship Development June 2019, Vol. 3, No. 2, pp. 69–80. Published by American Research Institute for Policy Development DOI: 10.15640/jsbed.v3n1a7 URL: http://dx.doi.org/10.15640/jsbed.v3n1a7Google Scholar Olorunshola, J. A. (2010): "Problems and Prospects of Small and Medium-Scale Industries in Nigeria". Being A Paper at CBN Seminar on Small and Medium Industries Equity Investments. Lagos. August, No. 4, pp. 34–49.Google Scholar OPEC, (2013). Annual Statistical Bulletin.OPEC, (2020). Annual Statistical Bulletin.Ovadia., J.S., (2013). The Nigerian "one percent" and the management of national oil wealth through Nigerian content. Sci. Soc. 77(3), 315–341.Google ScholarCrossrefSearch ADS  Tilije, F.C. (2002): "Financing local contractors in the Nigerian oil and gas industry", A paper presented at a 2-day seminar on the dynamics of equipment leasing and contract financing for local contractors in the oil and gas industry, Port Harcourt.Google Scholar United Nations Conference Trade and Development (UNCTAD), (2010). Creating Business Linkages: A Policy Perspective, New York, Geneva. http://unctad.org/en/Docs/diaeed20091_en.pdf.
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211930-MS
+
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+

files/2022/Energy Transition Implications Considerations and Roadmap Development for Sub-Saharan Africa.txt

@@ -0,0 +1,180 @@
+----- METADATA START -----
+Title: Energy Transition: Implications, Considerations, and Roadmap Development for Sub-Saharan Africa
+Authors: Igolo Nonye Aniebo, Joseph Samuel Akpan
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211990-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Climate change is no longer a myth and is evident worldwide in different alterations of the weather. Over the past two decades, scientists worked to find alternate sources of energy that were not fossil-based. Success in this endeavour led to what is now termed as a global transition to renewable and sustainable energy. In most climes, this marks a new beginning filled with opportunities and a chance to allow the earth to heal. Highly developed economies like Norway and some member countries of the European Union, being the driver of the transition, have begun to make policies that align with the new age, setting themselves to have a stake in the profit. The African continent, which has abundant energy resources but is neither a major contributor nor a driver of climate change but suffers just as much as other continents in the world from its effects, is not in a position to take any corrective action, showing the incapacity to participate in any potential economic gain. This paper studies the Sub-Saharan landscape by visiting the histories of the nations in question based on the energy resources within their borders and the current state of energy transition development. It examines the freedom of said countries to create policies that work for them, indicating the implications of lack of planning and the taint it may have on the new trend in the countries of interest. Efforts would also be made in highlighting a roadmap that can serve as a knowledge base to facilitate the implementation of best strategies in enabling successful energy transition in Sub-Saharan Africa.
+
+
+
+
+Keywords:
+united states government,
+sustainable development,
+sustainability,
+africa,
+social responsibility,
+climate change,
+upstream oil & gas,
+north america government,
+retrieved,
+energy transition
+
+
+Subjects: 
+Environment,
+Sustainability/Social Responsibility,
+Climate change,
+Sustainable development
+
+
+
+
+Introduction
+
+
+The discussion about climate change and its effects is no longer the preserve of panels filled with experts. It has become commonplace in conversations between everyday people. Its destructive effect has been far-reaching, from the melting ice caps in Antarctica to the change in the seasons in Africa. The United Nations defines Climate Change as a continuing shift in temperatures and weather conditions caused majorly by the burning of fossil fuels which produces emissions containing greenhouse gases that prevent the heat from the sun from escaping, thereby increasing the Earth’s temperature (United Nations, 2022).
+
+
+The fear the Earth may give up on the human race led scientists to conclude that the only way to reduce further damage to the Earth was for the world to change the type of energy harnessed for humanity’s basic needs. The term for the change is "Energy transition," which has been defined by the International Renewable Energy Agency (IRENA) as the "pathway towards the transformation of the global energy sector from fossil-based to zero-carbon by the second half of this century, with the need to reduce energy-related CO2 emissions to limit climate change." (IRENA, 2022). Another definition states Energy transition as "a mixture of changes to the production, distribution, and consumption energy models to make them more sustainable" (Collins, 2022).
+
+
+The United Nations created an entity called The United Nations Framework Convention on Climate Change (UNFCCC), which made an international environmental treaty to fight humans’ harmful tampering with the climate system (UNFCCC, 2022). The treaty stipulated continuous scientific research and routine meetings, talks, and future policy agreements devised to enable the ecosystems acclimatize to climate change, guarantee that food production is secure, and the economy’s development continues in a viable way (Wikipedia contributors, 2022). The yearly conference held under the framework of the UNFCCC is called The United Nations Climate Change Conference. It enables the parties of the UNFCCC to gather, commonly known as the Conference of Parties (COP) (Wikipedia contributors, United Nations Climate Change conference, 2022). The 1997 Kyoto Protocol and the 2015 Paris Agreement, which are under the UNFCCC parent treaty, have the final objective to regulate greenhouse gas concentrations in the atmosphere and maintain the average temperature globally to as close to 1.5°C as possible (UNFCCC, 2022).
+
+
+The African continent is blessed with bountiful resources and a teeming population that can be harnessed to grow the economy with the right policies and a suitable environment that encourages growth in the different forms of businesses. According to the United Nations Geoscheme, the continent is divided into five geographical regions (UNSD, 2022) as shown in Figure 1 
+
+
+Figure 1View largeDownload slideMap of Africa showing the geographical regions and the countries (WorldAtlas, 2022)Figure 1View largeDownload slideMap of Africa showing the geographical regions and the countries (WorldAtlas, 2022) Close modal
+
+
+The Geoscheme has two main categories; Northern Africa and Sub-Saharan Africa, where Sub-Saharan Africa (SSA) comprises Western, Eastern, Central (Middle), and Southern Africa. A brief overview of the regions shows us that while Northern Africa is the third most populous area in Africa, it has historically been able to achieve reasonable electrification for its populace due to its location geographically, which has made it partner naturally with the European Union (Dadush, Demertzis, & Wolff, 2017). SSA, on the other hand, has the majority of Africa’s landmass with the two most populous regions (Eastern and Western Africa) within its boundaries, varied geographical features including tropical rainforests, savannahs, and the Sahel region, to mention a few (Shvili, 2021) and unfortunately inability to cater for its population due to poor governance, poor management of economics, irregular foreign direct investment. Figure 2 shows rates in different regions.
+
+
+Figure 2View largeDownload slideElectrification rates in geographical regions (PwC, 2021)Figure 2View largeDownload slideElectrification rates in geographical regions (PwC, 2021) Close modal
+
+
+Overview of Energy Transition / Energy Transition and its implication
+
+
+In 2015 the United Nations member states adopted the 2030 Agenda for Sustainable Development which gives a model to achieve prosperity and peace now and hereafter for the people and the planet (United Nations, Sustainable Development Goals, 2022). At the core of the model are the 17 Sustainable Development Goals (SDGs), which range from "no poverty" to "partnership for the goals" and are a call for action for developed and developing countries (United Nations, Sustainable Development Goals, 2022).
+
+
+Goal 13 backs the drive for an energy transition which states that countries should "Take urgent action to combat climate change and its impacts" (United Nations, Goal 13, 2022). This call for action has spurred countries on to, for example, sign the Paris Agreement, and the recently concluded COP 26, which was held in Glasgow in 2021, saw some countries, including Vietnam, Ukraine, Indonesia, and Canada, promise to halt building and issuance of permits for new coal plants (Plumer & Friedman, 2021). Important to note are the other goals that should concern African countries since they classify as developing nations. We have:
+
+
+Goal 7 aims to "Ensure access to affordable, reliable, sustainable and modern energy for all" and one of its targets is "By 2030, increase substantially the share of renewable energy in the global energy mix." (United Nations, Goal 7, 2022)Goal 8 aims to "Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all" and one of its targets is "Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7% gross domestic product growth per annum in the least developed countries." (United Nations, Goal 8, 2022)Goal 10 aims to "Reduce inequality within and among countries" with targets like "Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreements." (United Nations, Goal 10, 2022).
+
+
+The African Union (AU), in its "Agenda 2063: The Africa we want", commits its member states to "speed up actions to finance and implement the major infrastructure projects in energy, harnessing all African energy resources to ensure modern, efficient, reliable, cost-effective, renewable and environmentally friendly energy to all African households, businesses, industries, and institutions, through building the national and regional energy pools and grids, and Programme for Infrastructure Development in Africa (PIDA) energy projects." (African Union Commission, 2015)
+
+
+It would seem that although the goals of the United Nations are in line with the agenda of the African Union, very little planning is being done to reach them. Statistically, as seen in Figure 3, the emission contribution by Africa has consistently remained low over the years despite the exploration and use of fossil fuels, and this can be attributed to the fact that it is still a developing continent. By contrast, the emissions from emerging economies have risen mainly due to their Gross Domestic Product (GDP) growth, as seen in Figure 4.
+
+
+Figure 3View largeDownload slideAnnual share of global CO2 emissions (Ritchie & Roser, 2020)Figure 3View largeDownload slideAnnual share of global CO2 emissions (Ritchie & Roser, 2020) Close modal
+
+
+Figure 4View largeDownload slideCO2 emissions of different economies in tCO2 per capita (IEA, 2022)Figure 4View largeDownload slideCO2 emissions of different economies in tCO2 per capita (IEA, 2022) Close modal
+
+
+Renewable Energy Implications for Sub-Saharan Africa
+
+
+In a rush to heal the world, the world seems to have forgotten the goals it signed for the inclusion of all countries, especially the developing nations. It is no secret that the countries in Africa are blessed with varied resources, but to have energy access for all, its economy has to thrive. For countries that are blessed with resources that are in high demand, for example, crude oil, gas, cobalt, to name a few, it would be logical that the said countries will be able to sell their resources to generate the funds needed to create a suitable environment for growth.
+
+
+Worldwide, as a clampdown to reduce fossil fuel dependence, some foreign countries and development institutions such as The United States of America, Italy, Canada, and the European Investment Bank promised to reduce or stop investments in new projects altogether (Plumer & Friedman, 2021). The lack of investments will spell disaster for countries dependent on exporting their crude and gas, such as Nigeria. It is also a challenge for countries such as Mozambique, which recently started developing a gas field to help improve the statistics of having just 31% of the population with access to electricity((Nakanwagi, 2021). Uganda, which found oil in commercial quantities in 2006, has decided in its "Vision 2040" to put the industry at the lead of its growth plan to help the country achieve a middle-income economy and improve its electrification figure, which stands to stand at 41.3% of the population. The Ugandan government signed an investment agreement to commence the East African Crude Oil Pipeline Project (EACOP) (Nakanwagi, 2021). For renewable energy development to pick up the pace, the funding given to Africa should be commensurate with the need, and it can be seen in Figure 5 that this is not the case.
+
+
+Figure 5View largeDownload slideOverall renewable energy investment globally and in Africa (IRENA and AfDB, 2022)Figure 5View largeDownload slideOverall renewable energy investment globally and in Africa (IRENA and AfDB, 2022) Close modal
+
+
+Figure 6 shows the region’s annual investments, excluding large hydropower (above 50 MW) and country-wise main beneficiaries. It shows the majority of funding going to Northern Africa, which has been able to create favourable policies and financial processes that work for them, unlike SSA, which categorically needs the funding but has countries that are susceptible to political, financial, and policy risks to name a few (IRENA and AfDB, 2022).
+
+
+Figure 6View largeDownload slideAnnual renewable energy investment by region(left) and the top beneficiaries for 2010-2020 (IRENA and AfDB, 2022)Figure 6View largeDownload slideAnnual renewable energy investment by region(left) and the top beneficiaries for 2010-2020 (IRENA and AfDB, 2022) Close modal
+
+
+Case Studies
+
+
+The countries in SSA cannot be said to have the same obstacles to surmount. Still, a common characteristic can be exploiting its resources, poor or inexistent policies, and bad governance. We shall examine the Democratic Republic of Congo, Mozambique, and Nigeria.
+
+
+DEMOCRATIC REPUBLIC OF CONGO (DRC)
+
+
+Located in Central Africa, the Democratic Republic of Congo, by area, is the second-largest country in Africa. It is one of the countries in Africa that has had a troubled past due to wars, instability, poor infrastructure, and the exploitation of its resources with little benefit for the citizens.
+
+
+One of the many resources within the borders of DRC is cobalt, which is a key mineral in the production of lithium-ion batteries (Hafner, Tagliapietra, & de Strasser, 2018). The DRC is the biggest cobalt supplier making up more than 63% of the global supply (Sachs, et al., 2021), but since the mining industry is a mix of artisanal mining and some companies, this did not favour the country. The instability of its government and possibly the lack of know-how has prevented the country from making the right policies for years to benefit correctly from their resources. Abuse of human rights and damage to the environment are common in the mines, but buyers are willing to turn a blind eye because of the rarity of the resource (Hafner, Tagliapietra, & de Strasser, 2018).
+
+
+The government made a move in 2018 to reform the 2002 mining policy and has increased tax, state equity, royalties, a committee to commercialize Artisan mining, and a reduction in stabilization clauses (Sachs, et al., 2021). With this and further policies to tighten the economy, consideration can be given to developing its renewable energy sector apart from hydropower, or investments could be made.
+
+
+MOZAMBIQUE
+
+
+Mozambique’s proven gas reserves is approximately 100 tcf and has a recent find in the Cabo Delgado province. It has attracted the major players to its country and is thriving in the gas market, with South Africa taking 81% of its export (Nakanwagi, 2021). The COVID-19 pandemic halted operations on the development of the new area, followed by an insurgency by the military on the indigens of Delgado, who felt disenfranchised by the lack of care or equality shown to them by the government. The fight between the people and the government has left that area unsafe for operations to continue for the time being. Lack of understanding, which can also be seen in the Nigerian case, has caused a project to boost the economy and improve the electrification of the country to be paused indefinitely.
+
+
+NIGERIA
+
+
+Nigeria is nicknamed the giant of Africa because of its teeming population. Unfortunately, due to inadequate know-how on the development of policies that would have helped the country get the most out of its black gold, the country is still managing years after many international firms have set up affiliates on its shores. Most of the region’s oil has been drilled is faced with polluted rivers and lands, which has led to the loss of lives and livelihoods. Different forms of uprisings have occurred, from protests to breaking pipelines and causing companies to declare force majeure. The lack of policies to ensure companies are held accountable has allowed polluted lands to stay polluted. Nigeria is also blessed with a proven reserve of 209.5 tcf of gas as of January 2022, increasing from its former value of 206.53tcf (Wasilat, 2022). We are yet to harness the full potential of our gas for domestic use and export. In the Northern part of the country, the Artisinal mining is a known issue as people mine the gold and sell it at lower prices than when a company sets up business here. A clamp down is needed on the people who believe it is their right to take any minerals and sell them. Proper policies and adequate checks will ensure that things like this are not left to worsen.
+
+
+Energy Planning: Development for Sub-Saharan Africa
+
+
+Statistically, the challenge with the transition for SSA specifically is not the lack of natural resources to generate energy (Figure 7) but the lack of financial help, access to energy, and the making of sound policies amongst others.
+
+
+Figure 7View largeDownload slideEstimated renewable resources for Africa (Sachs, et al., 2021)Figure 7View largeDownload slideEstimated renewable resources for Africa (Sachs, et al., 2021) Close modal
+
+
+Work has begun to achieve an energy transition that is tailor-made for Africa, the following include projects that have already started and ideas that can catapult Africa to the position it is meant to occupy.
+
+
+A partnership between The African Union Commission, United Nations Economic Commission for Africa, African Development Bank, and the NEPAD Planning and Coordinating Agency led to the completion of the plans for the Programme for Infrastructure Development in Africa (PIDA). This initiative is to address the deficit on infrastructure within Africa. The goal is to start and finish projects in the energy, ICT, transport, and transboundary water networks sector, improving trade, creating jobs, and providing growth opportunities. Figure 8 shows the planned projects, some of which are ongoing (African Union, AfDB, NEPAD, 2011).Improved electricity grid: this will ensure energy access for all and be in line with Goal 7 of the United Nations’ SDGs and the African Union Agenda. The way to achieve this is by building or renovating the transmission system in countries, creating transaction portals for countries that generate more than the demand, digitalizing the grid, and developing off-grid generation for rural areas. Many projects such as the "Africa 2030: Roadmap for a Renewable Future by IRENA", "Power Africa Transmission Roadmap to 2030 by the United States Agency for International Development (USAID)," and "The Roadmap by the United States Agency for International Development (USAID)" have been created to see electrification for all being achieved.To achieve Goals 10 and 8 of the United Nations’ SDGs, it would be pertinent to allow SSA to use fossil fuels to grow and stabilize its economy without future investment being withdrawn and assistance from foreign countries to develop the other renewable sources.
+
+
+Figure 8View largeDownload slideClockwise (a)PIDA energy impact (b)PIDA Transport impact (c)PIDAWater impact (d)PIDA ICT impact (African Union, AfDB, NEPAD, 2011)Figure 8View largeDownload slideClockwise (a)PIDA energy impact (b)PIDA Transport impact (c)PIDAWater impact (d)PIDA ICT impact (African Union, AfDB, NEPAD, 2011) Close modal
+
+
+Conclusion
+
+
+The result of not having proper structures, bad governance, and diversity in the population of SSA has hampered its development. The high-risk profiles of various countries have made investors shy away, taking the much-needed Foreign Direct Investment (FDI) to countries that do not need it as much.
+
+
+The mix of fossil fuels and renewables is the only way for SSA to transition to a clean and green environment, with a mandate on oil and gas platforms to develop means to achieve a zero-target flare on onshore and offshore sites.
+
+
+The world will still depend on fossil fuels in the nearest future as transportation of people and property still requires a constant burning source. Hence, SSA can supply the needs of both renewable and non-renewable activities while developing its economy and eventually embarking on net-zero emissions.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+African Union Commission. (2015). Agenda 2063; The Africa we want. Addis Ababa: African Union.African Union, AfDB, NEPAD. (2011, September). Programme for Infrastructure Development In Africa: Interconnecting, integrating and tranforming a continent. Retrieved from AfDB: https://www.afdb.org/fileadmin/uploads/afdb/Documents/Project-and-Operations/PIDA%20note%20English%20for%20web%200208.pdfCollins, P. (2022, January25). Energy transition in the UK: definition, challenges and the law. Retrieved from Climate consulting by Selectra: https://climate.selectra.com/en/environment/energy-transition#what-is-the-energy-transition-and-why-is-it-importantGoogle Scholar Dadush, U., Demertzis, M., & Wolff, G. (2017). Europe's role in North Africa: development, investment and migration. Belgium: Bruegel.Google Scholar Hafner, M., Tagliapietra, S., & de Strasser, L. (2018). Prospects for Renewable Energy in Africa. In M.Hafner, S.Tagliapietra, & L.de Strasser, Energy In Africa (pp. 47-75). ChamSpringer.Google ScholarCrossrefSearch ADS  IEA. (2022, March08). CO2 emissions per capita by region, 2000-2021. Retrieved from IEA: https://www.iea.org/data-and-statistics/charts/co2-emissions-per-capita-by-region-2000-2021Plumer, B., & Friedman, L. (2021, November06). Over 40 Countries Pledge at U.N. Climate Summit to End Use of Coal Power. The New York Times, pp. https://www.nytimes.com/2021/11/04/climate/cop26-coal-climate.html.Google Scholar PwC. (2021). Africa Energy Review.Ritchie, H., & Roser, M. (2020). CO2 and Greenhouse Gas Emissions. Our world in Data, https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions.Google Scholar Sachs, J. D., Toledano, P., Brauch, M. D., Mebratu-Tsegaye, T., Uwaifo, E., & Sherril, B. M. (2021, September). Roadmap to Zero-Carbon Electrification of Africa by 2050: The Green Energy Transition and the Role of the Natural Resources Sector (Minerals, Fossil Fuels, and Land). Columbia Center on Sustainable Investment (CCSI) Working Paper Commissioned by and prepared for the African Natural Resources Centre, African Development Bank. New York:CSSI.Google Scholar Shvili, J. (2021, October28). Sub-Saharan Africa. Retrieved from WorldAtlas: https://www.worldatlas.com/regions/sub-saharan-africa.htmlGoogle Scholar IRENA. (2022, April02). Energy Transition. Retrieved from IRENA: https://www.irena.org/energytransitionIRENA and AfDB. (2022). Renewable Energy market Analysis: Africa and Its Regions. Abu Dhabi and Abidjan: International Renewable Energy Agency and African Development Bank.Nakanwagi, S. (2021, October30). Navigating the energy Transition in Africa The Fate of Nascent Petroleum Economics in an Accelerating Global Transition. Centre for Energy, Petroleum and Mineral Law and Policy.Google Scholar UNFCCC. (2022, April04). About the Secretariat. Retrieved from UNFCCC: https://unfccc.int/about-us/about-the-secretariatUnited Nations. (2022, April09). Goal 10. Retrieved from Sustainable Development Goals: https://sdgs.un.org/goals/goal10United Nations. (2022, April08). Goal 13. Retrieved from Division of Sustainable Development Goals: https://sdgs.un.org/goals/goal13United Nations. (2022, April09). Goal 7. Retrieved from Sustainable Development Goals: https://sdgs.un.org/goals/goal7United Nations. (2022, April09). Goal 8. Retrieved from Sustainable Development Goals: https://sdgs.un.org/goals/goal8United Nations. (2022, April08). Sustainable Development Goals. Retrieved from Division for Sustainable Development Goals: https://sdgs.un.org/goalsUnited Nations. (2022). What is Climate Change. Retrieved March 30, 2022, from United Nations: https://www.un.org/en/climatechange/what-is-climate-changeUNSD. (2022, April06). Methodology M49 standard. Retrieved from United Nations Statistics Division: https://unstats.un.org/unsd/methodology/m49/Wasilat, A. (2022, March2). NMDPRA: Nigeria's natural gas reserves hit 209.5tcf-up by 1.4% in one year. TheCable.Google Scholar Wikipedia contributors. (2022, March9). United Nations Climate Change conference. Retrieved from Wikipedia, The Free Encyclopedia.: https://en.wikipedia.org/w/index.php?title=United_Nations_Climate_Change_conference&oldid=1076166783Wikipedia contributors. (2022, March19). United Nations Framework Convention on Climate Change. Retrieved from Wikipedia, The Free Encyclopedia: https://en.wikipedia.org/w/index.php?title=United_Nations_Framework_Convention_on_Climate_Change&oldid=1078009669WorldAtlas. (2022, April6). Regions of Africa. Retrieved from WorldAtlas: https://www.worldatlas.com/geography/regions-of-africa.html
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211990-MS
+
+
+

files/2022/Enhanced Rheological and Filtration Properties of Water-Based Mud Using Iron Oxide and Polyanionic Cellulose Nanoparticles.txt

@@ -0,0 +1,297 @@
+----- METADATA START -----
+Title: Enhanced Rheological and Filtration Properties of Water-Based Mud Using Iron Oxide and Polyanionic Cellulose Nanoparticles
+Authors: Soroush Kachoyan, Shaikh Nihaal, Jeffrey Oseh, Mohd Noorul Anam, Afeez Gbadamosi, Augustine Agi, Radzuan Junin
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211924-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+The unstable wellbore created by the infiltration of drilling fluids into the reservoir formation is a great challenge in drilling operations. Reducing the fluid infiltration using nanoparticles (NPs) brings about a significant improvement in drilling operation. Herein, a mixture of iron oxide nanoparticle (IONP) and polyanionic cellulose nanoparticle (nano-PAC) additives were added to water-based mud (WBM) to determine their impact on rheological and filtration properties measured at 80 °F, 100 °F, and 250 °F. Polyanionic cellulose (PAC-R) was processed into nano-PAC by wet ball-milling process. The rheological behaviour, low-pressure low-temperature (LPLT), and high-pressure high-temperature (HPHT) filtration properties performance of IONP, nano-PAC, and IONP and nano-PAC mixtures were compared in the WBM. The results showed that IONP, nano-PAC, and synergy effect of IONP and nano-PAC in WBM at temperatures of 80 °F and 250 °F improved the density, 10-s and 10-min gel strength (10-s Gs and 10-min GS), plastic viscosity (PV), and the yield point (YP), while the pH was constant at 9.0. The mixture of 1.5 wt.% IONP + 0.25g nano-PAC in the WBM unveiled the most promising and optimal properties. At LPLT, the mixture improved the YP by 11% and reduced the LPLT fluid loss volume (FL) by 32.4%. At HPHT, the mud density increased by 3%, 10-s GS by 56%, 10-min GS by 52%, and the YP by 33.3%, while the HPHT FL decreased by 21%. With 1.0 g concentration at 100 °F, the nano-PAC achieved the greatest reduction in the FL of the WBM by 63%, followed by PAC-R by 57% before IONP that showed 36% reduction. Overall, the impact of IONP and nano-PAC in the WBM is evident and while the IONP showed more improved PV, the nano-PAC is more desirable for fluid loss control when 1.0 g at 100 °F was used. The use of combined IONP and nano-PAC could be beneficial for mitigating fluid loss and averting wellbore problem.
+
+
+
+
+Keywords:
+ionp,
+drilling fluids and materials,
+ionp 0,
+fluid loss control,
+drilling fluid chemistry,
+pac-r,
+reduction,
+drilling fluid formulation,
+drilling fluid selection and formulation,
+mud
+
+
+Subjects: 
+Drilling Fluids and Materials,
+Drilling fluid selection and formulation (chemistry, properties)
+
+
+
+
+Introduction
+
+
+The exploration of oil and gas has gotten to new heights where challenging drilling environments are met and drilling in these challenging environments, such as deeper, shaly, and harsher formations are more difficult than ever before. Oil and gas industry as well as oil service companies are continuously seeking more efficient means to confront the harsh environment to drill and produce oil and gas in a viable and safer way. The major difficulties include seepage of liquid phase of the drilling fluid into the reservoir formation, inefficient cuttings lifting, degradation of fluid properties, such as viscosity and gel strength, and fluid flocculation (Blkoor et al., 2021; Boa et al., 2019; Caenn et al., 2017)
+
+
+Drilling fluid execute many important functions in drilling processes and these functions include contolling formation pressure, cooling and lubricating the drill bits and strings, clearing the hole of drilled cuttings, preventing fluid loss, reduction of formation damage, maintaining hole integrity, aiding well logging and pipe tripping, enhancing drilling rate, minimizing pipe sticking, torque, drag, and drill and casing string corrosion (Caenn et al., 2017; Darley and Gray, 1988). Thus, in the event of drilling fluid degradation or difficulty to perform optimally, so many problems that could be damaging to the workers or crew, company, and the environment occur. Drilling fluid formulation must be of great importance to ensure that problems are averted. The rheological properies of drilling fluids must be continuously checked and regulated to predict and prevent any potential loss accurately. Also, the temperature and pressure of the drilled formation must be monitored since the flow of fluids through the pipes in the well is highly affected by temperature, pressure, and time (Oseh et al., 2020a, b; Vryzas et al., 2015). All these parameters result in significant changes in the properties of the drilling fluid. Tracking and regulating drilling fluid properties as an inseparable part of drilling operation become difficult while drilling. Hence, an understanding of drilling fluid behaviour is required, including the contribution of the related microstructure or molecular mechanisms of the drilling fluid flow properties and fluid loss behaviour (Blkoor et al., 2022; Vryzas et al., 2016).
+
+
+Most oil and gas wells are drilled with greater wellbore pressures than formation fluid pressures. During this operation, mud filtrate invades the formation, especially in productive zones which have higher permeability (Oseh et al., 2020b; Taha and Lee, 2015). When the mud filtrate enters the formation, it causes damage to the formation, resulting in expensive treatments and loss of crude oil outputs. This phenomenon is another aspect that needs to be cautiously handled to facilitate safe and efficient drilling process. A standard formulated drilling mud can stabilize and strengthen the wellbore against fluid loss and lost circulation. A thin and low permeable mud cake and low fluid loss are desired to prevent or lessen formation damage and unstable wellbore (Gbadamosi et al., 2019a, b; Mahmoud et al., 2016).
+
+
+Mud additives play a crucial role in the composition of drilling fluid. Based on the type of performance desired from the drilling fluid and the problems faced during drilling, specific additives can be added. Conventional drilling fluids, especially the water-based mud (WBM) is mainly formulated with salts, bentonite clay, and polymers added into the continuous liquid (water) phase, but these additivies find its dufficult to preserve the rheological characteristics of the WBM throughout the drilling operation because of the large modifications in pressure and temperature while drilling (Darley and Gray, 1988). When drilling deeper, the downhole environments become hotter and more complicated, and these conditions make drilling fluid not to execute its functions effectively. Thus, the fluid carrying capacity is reduced and high loss of the fluids into the drilled formation occur (Oseh et al., 2019a, b; Saboori et al., 2018).
+
+
+Recently, nanoparticles (NPs) have been utilised as a new addition in drilling mud to enhance the mud's properties. NPs by virtue of their high area to volume ratio and extreme small size can interact effectively with other mud's particles and their surrounding wellbore matrix to enhance the mud's functionality (Gbadamosi et al., 2022; Blkoor et al., 2021; Agi et al., 2020; Amanullah et al., 2011). Different types of NPs, such as carbón-based, inorganic-based, organic-based, composite-based, and metal oxides-based had been used to prepare nanofluid for enhanced oil recovery (EOR) or added into drilling fluids for drilling operations and remarkeable achievements were achieved (Oseh et al., 2020c, d; Vryzas and Kelessidis, 2017; Abduo et al., 2016; Mahmoud et al., 2016; Vryzas et al., 2014). These NPs include carbón nanotubes (CNTs), titania or titanium dioxide (TiO2), Zinc oxide (ZnO), nanosilica (SiO2), copper oxide (CuO), aluminium oxide (Al2O3), iron oxide (Fe2O3), etc. The functions of NPs materials are dependent on the reason that a high surface area per mass of smaller particles overcome the driving force for phase separation, such as packing entropy and gravity (Amanullah et al., 2011).
+
+
+Iron oxide nanoparticle (Fe2O3 NP or IONP) has a great potential for field application. Henceforth, IONP is used to indicate iron oxide nanoparticle used in this study. Previous research on the use of IONP at laboratory scale for both WBM and oil-based mud (OBM) reported promising results (Mahmoud et al., 2016; Vryzas et al., 2015; Vryzas et al., 2014; Nwaoji et al., 2013; Jung et al., 2011). IONPs are particles of iron oxide with diameters range from 1 nm to 100 nm. The two key types are magnetite (Fe3O4) and its oxidized nature maghemite (γ- Fe2O3) (Jung et al., 2011). Mahmoud et al. (2016) carried out research with IONP, SiO2 NP, and a mixture of both IONP and SiO2 NP in WBM. They find out that 0.5 wt.% of pure IONP in the drilling mud showed the best results compared to those of SiO2 NP and the IONP and SiO2 NP mixture. In this regard, 61% increase in yield stress was achieved with IONP compared with the WBM, wheras SiO2 NP and the mixture of IONP and SiO2 NP recorded 22% and 49%, respectively, over the WBM. Also, the IONP reduced the filter cake thickness of the WBM by 28% and the cumulative fluid loss volume by 21%. The increase in rheological properties by the IONP was attributed to the increase frictional resistance of the particles, while the formation of an extra layer by the IONP on the drilling mud reduced the filtration properties (Mahmoud et al., 2016).
+
+
+Moreover, in controlling fluid loss, conventional polyanionic cellulose (PAC) has been utilized to form a thin and low permeable mud cake (Fereydouni et al., 2012). PAC additive is a broadly used material in drilling fluid. It has the same molecular structure to conventional carboxy methyl cellulose (CMC). Nevertheless, it functions better than CMC in reduction in filtration, anti-collapse, anti-salt, and at high-temperature environment. It can be used up to a temperature of 150 °C (Fereydouni et al., 2012). PAC as a dispersible additive can significantly reduce the API filtaration rate wih an improvement in rheological properties of drilling mud. PAC is introduced to drilling muds and other chemical mixtures to help in many ways, including formation cuttings suspensión, help movement of the surface, aid the cooling and lubrication of drill bits, and formation pressure control all through the drilling process (Darley and Gray, 1988). Addition of PAC prevents any harmful conditions, such as hole collapse or wellbore blockage during drilling operation by causing the drilling fluid to become rigid. It has good low water loss carácter that help to decrease the quantity of water required to seep into the production zone. PAC is used to modify the level of mud's viscosity and can also be applied as a portion of a mixture to modify the level of pH (Fereydouni et al., 2012).
+
+
+A study conducted by blending nano-PAC wih WBM to study the rheological improvement of the WBM showed that the nano-PAC reduced the API fluid loss volume and cake thickness by 20% and 47%, respectively (Fereydouni et al., 2012). Although so many previous studies have reported the use of pure IONP, pure PAC-R, and nano-PAC in drilling mud design, the synergy effect of using IONP and nano-PAC mixture for fluid loss reduction in WBM has not been examined to comprehend the fluid behaviour of IONP and nano-PAC blend. Therefore, in this paper, the effects of synrergy of IONP and nano-PAC mixture to enhance the rheological and filtration characteristics of WBM was extensively studied and it was compared with pure IONP, PAC-R, and nano-PAC.
+
+
+Materials and methods
+
+
+Materials
+
+
+Bentonite, xanthan gum (XG), caustic soda (NaOH), calcium carbonate (CaCO3), PAC-R, resinated lignite, barite, and IONP. IONP has a size of 30 nm with 99.9% purity and a density of 5.30 g/cm3 and is insoluble in water. These materials were bought from Shanghai Xinglu Chemical Technology Co., Ltd (China).
+
+
+Methods
+
+
+Preparation of polyanionic cellulose nanoparticle
+
+
+Nano-PAC was prepared by using the wet ball-milling process. The amount of energy in the wet ball-milling depends upon the slipping speed, size, and quantity of the balls, and the duration of residence. The stress generated by the balls onto the PAC-R particles crushes it before seiving using shale shaker (Figure 1) to make them nanosize (nano-PAC). The sieving process was conducted as follows: the pure PAC-R was sieved for 30 minutes by using a sieve shaker. The pure PAC-R was crushed and mixed with tap water at 15% controlled concentration and sieved for 30 minutes. The sieved PAC-R powder solution was further crushed with a wet grinder at a rotary speed of 10 rpm for 20 minutes. The sieved sample was sun-dried for 24 hours and labelled nano-PAC. Next, the particle size distribution (PSD) of the PAC-R before and after processing (nano-PAC) was determined by feeding 0.2 g each into Mastersizer Malvern Instrument for analysis. The outcome was shwn automatically on the printable monitor and registered.
+
+
+Figure 1View largeDownload slideShale shakerFigure 1View largeDownload slideShale shaker Close modal
+
+
+Preparation of complex water-based mud
+
+
+The base mud (or complex WBM) was formulated with 350 ml of deionized water. 15 g of bentonite was gradually introduced for 20 minutes to the solution using a mud mixer through mechanical stirring. Then, 0.2 g of XG and 0.3 g of PAC-R were added and each was blended for 10 minutes. Thereafter, 1.5 g of resinated lignite and 1.0 g of NaOH were simultaneously mixed and added. Lastly, 15 g of CaCO3 and 20 g of barite were blended seperately for 10 minutes before adding them to the solution. The laboratory formula and mixing times used are summarized in Table 1 and it is equivalent to 350 ml laboratory barrel of drilling mud.
+
+
+Table 1Laboratory formula used to prepare the equivalent of 1 barrel of WBM Additive
+            . Description/Function
+            . Quantity
+            . Mixing Time
+            . Deionized water Base liquid 350 ml – Bentonite Viscosity and filtrates control 15 g 20 minutes XG Viscosifier 0.2 g 7 minutes PAC-R Filtrate loss control 0.3 g 7 minutes Resinated lignite Thinner 1.5 g 10 minutes NaOH pH agent 1.0 g 5 minutes CaCO3 Weighting and bridging material 15 g 10 minutes Barite Weighting material 20 g 20 minutes Additive
+            . Description/Function
+            . Quantity
+            . Mixing Time
+            . Deionized water Base liquid 350 ml – Bentonite Viscosity and filtrates control 15 g 20 minutes XG Viscosifier 0.2 g 7 minutes PAC-R Filtrate loss control 0.3 g 7 minutes Resinated lignite Thinner 1.5 g 10 minutes NaOH pH agent 1.0 g 5 minutes CaCO3 Weighting and bridging material 15 g 10 minutes Barite Weighting material 20 g 20 minutes View Large
+
+
+Formulation of drilling muds using nanoparticles
+
+
+To prepare the NPs drilling muds, pure IONP and nano-PAC at different concentrations were used. The concentrations of the NPs were added into the base mud (WBM) formulated in Table 1. In this formulation, the procedures used and the descriptions of the six mud types formulated are represented in Table 2.
+
+
+Table 2Composition of drilling muds containing nanoparticles Number
+            . Depiction
+            . Compositions
+            . Mud #1 Base mud WBM Mud #2 3.5 wt.% IONP WBM + 3.5 wt.% IONP + 0.0 g nano-PAC Mud #3 2.5 wt.% IONP + 0.2 g nano-PAC WBM + 2.5 wt.% IONP + 0.2 g nano-PAC Mud #4 1.5 wt.% IONP + 0.25 g nano-PAC WBM + 1.5 wt.% IONP + 0.25 g nano-PAC Mud #5 0.5 wt.% IONP + 0.3 g nano-PAC WBM + 0.5 wt.% IONP + 0.3 g nano-PAC Mud #6 0.35 g nano-PAC WBM + 0.0 wt.% IONP + 0.35 g nano-PAC Number
+            . Depiction
+            . Compositions
+            . Mud #1 Base mud WBM Mud #2 3.5 wt.% IONP WBM + 3.5 wt.% IONP + 0.0 g nano-PAC Mud #3 2.5 wt.% IONP + 0.2 g nano-PAC WBM + 2.5 wt.% IONP + 0.2 g nano-PAC Mud #4 1.5 wt.% IONP + 0.25 g nano-PAC WBM + 1.5 wt.% IONP + 0.25 g nano-PAC Mud #5 0.5 wt.% IONP + 0.3 g nano-PAC WBM + 0.5 wt.% IONP + 0.3 g nano-PAC Mud #6 0.35 g nano-PAC WBM + 0.0 wt.% IONP + 0.35 g nano-PAC View Large
+
+
+pH and density measurements
+
+
+Digital pH meter model cyber scan pH 510 from Eutech instruments was used to measure the pH value of the drilling fluid sample. A mud balance was used to determine the density of the mud. The measurements were recorded before and after hot rolling tests. The balance cup was filled with the mud to be tested and covered with the lid. The mud balance was then placed on the base, and the rider was adjusted to get the mud balance. Then, the density was noted from the left-side of the rider.
+
+
+Rheological properties measurement
+
+
+In this research, a Rotating 8 – Speed Brookfield Viscometer (BF45, Middleboro, MA, USA) was used to determine the plastic viscosity (PV) and yield point (YP) using Equations (1) and (2) following the shear stresses (τ) under stabilized shear rates (γ) data from 511 s−1 to 1022 s−1. These were obtained from two different dials 300 rpm and 600 rpm at ambient temperature of 80 ºF without aging and temperature of 250 ºF after termal aging in a 4-roller oven. Thereafter, 10-seconds and 10-minutes gel strength (10-s GS and 10-min GS) measurements were performed next before the filtration properties tests.
+
+
+PVcP =θ600−θ300(1)
+
+
+YP (lb/100ft2)=θ300−PV(2)
+
+
+θ is the viscometer dial reading (°); θ600 is dial reading at 600 rpm; and θ300 is dial reading at 300 rpm.
+
+
+Low Pressure Low Temperature (LPLT) fiter press measurement
+
+
+After the formulation of the muds, they were poured into the filter press cell with screen, filter paper, and rubber gasket. The cell was then fixed firmly to make sure that no pressure finds its way out during the test. A pressure of 100 psi was applied for 30 minutes, after which the filtrate loss volume (LPLT FL) was recorded, and the mud cake thickness (LPLT MCT) was taken.
+
+
+High pressure high temperature (HPHT) filter press measurement
+
+
+For the HPHT test, the mud was poured into the HPHT cell after hot rolling for 16 hours in a 4-roller oven cell. The cell was heated up to 250 °F in a heating jacket at a pressure of 600 psi applied on top of the cell and a pressure of 100 psi at the bottom of the cell making the differential pressure within the cell to be 500 psi. The timer was started and the HPHT FL was collected after 30 minutes; thereafter, the corresponding HPHT MCT was registered.
+
+
+Results and Discussion
+
+
+Particle size distribution of PAC-R and nano-PAC
+
+
+The results of PSD analysis for PAC-R before processing and after processing (nano-PAC) are shown in Figures 2 and 3. The size of anionic cellulosic polymers is essential in investigating the suitability of cellulose for certain uses. This could also help to minimize breakage and enhance or increase the binding of final products (Agi et al., 2020). According to Figure 2, PAC-R before wet ball-milling process shows a wider PSD than nano-PAC after ball milling process (Figure 3). The PAC-R before processing is mainly distributed between the size range of 140 μm and 700 μm with a corresponding peak frequency of 290 μm, while the processed nano-PAC size range is mainly distributed between 50 nm and 130 nm and its corresponding peak frequency occurred at 70 nm (Figure 3). The produced size range of nano-PAC when added to the base mud is sufficiently small to induce large specific surface área that can cause significant changes in the properties of the base mud. This confirms that the wet ball-milling process used to modify the size of PAC-R to nanosize is successful and promising.
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+Figure 2View largeDownload slideSize of PAC-R before wet ball-milling processFigure 2View largeDownload slideSize of PAC-R before wet ball-milling process Close modal
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+Figure 3View largeDownload slideSize of PAC-R after wet ball milling process (nano-PAC)Figure 3View largeDownload slideSize of PAC-R after wet ball milling process (nano-PAC) Close modal
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+Mud density
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+Figure 4 shows the density of complex WBM systems with different concentrations of IONP and nano-PAC measured at 80 °F before aging and 250 °F after thermal aging process. The density of the base mud (Mud #1) before and after aging is constant at 8.9 ppg and it changed with the additions of IONP and nano-PAC. Mud #2 containing 0.35 wt.% of IONP in the base mud shows the largest mud density of 9.2 ppg and 9.3 ppg before and after aging, respectively. At ambient conditions, the mixture of IONP and nano-PAC increases the density of the mud at changing mixture concentrations, but after thermal aging process, the increasing trends continue from Mud #2 (0.35 wt.% IONP) to Mud #4 (1.5 wt.% IONP + 0.25 g nano-PAC). However, these increasing trends were no longer sustained at 0.5 wt.% IONP + 0.3 g nano-PAC (mud #5), which showed a reduction in density from 8.9 ppg (Mud #1) to 8.4 ppg (Mud #5), but the density of the base mud (Mud #1) remain equal at 0.35 g nano-PAC (Mud #6). From this analysis, Mud #6 containing 0.35 g nano-PAC (without IONP) is more sensitive to temperature, while the density of Mud #2 (3.5 wt.% IONP without nano-PAC) is less affected by temperature. Generally, the mixture of IONP and nano-PAC tends to affect the density of the base mud (Mud #1). More solids accumulation in the base mud by the presence of IONP and nano-PAC could have led to the changes in the density of the base mud (Fattah and Lashin, 2016).
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+Figure 4View largeDownload slideMud density of drilling muds before and after hot rolling testsFigure 4View largeDownload slideMud density of drilling muds before and after hot rolling tests Close modal
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+Mud pH
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+The results of the pH of complex WBM systems with different concentrations of IONP and nano-PAC measured at 80 °F before aging and 250 °F after thermal aging process are shown in Figure 5. The pH reveals the acidic or alkaline level of a material. All the drilling mud samples at both temperaure conditions indicate a constant pH of 9.0, which is moderately alkaline on the pH meter scale. By this result, the introduction of IONP and nano-PAC has no effect on the pH of the base mud (Mud #1) since IONP is a neutral compound and nano-PAC is not a surfactant (Oseh et al., 2020a). Moreover, the pH level exhibited by all the mud samples is within the recommended range between 8.0 and 10 required to control the rheological properties of drilling fluids (Singh and Dutta, 2018).
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+Figure 5View largeDownload slideMud density of drilling muds before and after hot rolling testsFigure 5View largeDownload slideMud density of drilling muds before and after hot rolling tests Close modal
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+Plastic viscosity
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+PV helps to support the prevention of fluid seepage into the drilled formation (Darley and Gray, 1988). Figure 6 shows the PV of complex WBM systems with different concentrations of IONP and nano-PAC measured at 80 °F before aging and 250 °F after thermal aging process. At ambient conditions, the PV dropped for all the mud samples, except the sample with 3.5 wt.% IONP, which has equal value of 15 cP with the base mud. As the concentration of IONP decreases and nano-PAC increases in the mixture, the PV of the base mud decreases with a maximum drop of 40% for the 0.35 g nano-PAC, while the mixture viscosity reduced accordingly to 12 cP, 11 cP, and 10 cP. This results imply that instead of increase in drilled solids in the mud, the IONP and nano-PAC in the mud were not able to contribute to the solids build-up for increase in PV. For the part of nano-PAC, it seems the applied 0.35 g nano-PAC concentration was not enough to cause the carboxyl groups in the nano-PAC molecules to provide strong dispersión in water that could have led to frictional increase between the particles (Fereydouni et al., 2012).
+
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+However, after hot rolling, the PV trends changed. The PV of the mud samples was strongly affected by temperature towards an increasing trends over the mud samples before aging. The mixture of IONP and nano-PAC has no effect on the PV of the base mud as the values are constant at 16 cP but with 3.5 wt.% IONP (without nano-PAC) and 0.35 g nano-PAC (without IONP), the PV of the base mud of 16 cP increased to 22 cP by 37.5% and 18 cP by 12.5%, respectively. It can be observed that as the concentration of IONP in the mixture decreases and nano-PAC concentration increases in the mixture, the PV of the base mud remained unchanged. For the base mud, the higher temperature of 250 °F makes bentonite clay to flocculate, leading to high PV. Bentonite has a negative charge and iron (Fe) in IONP has a positive charge, and they are attracted towards each other and it makes the mud stable and well dispersed, hence increase in PV at high temperature (Vryzas et al., 2016). Also, the presence of IONP in bentonite suspension or drilling mud containing bentonite at high temperature can result in bentonite clays and IONP dispersion leading to constant thickening of the drilling mud, hence the increase in PV (Vryzas et al., 2015).
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+Figure 6View largeDownload slidePlastic viscosity of drilling muds before and after hot rolling testsFigure 6View largeDownload slidePlastic viscosity of drilling muds before and after hot rolling tests Close modal
+
+
+Yield point
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+
+Figure 7 presents the YP of complex WBM systems with different concentrations of IONP and nano-PAC measured at 80 °F before aging and 250 °F after thermal aging process. A YP increase could imply that some chemicals are degraded or contaminated (Kelessidis and Maglione, 2008). At ambient conditions, there was increase in YP for all the mud samples with the highest margin of increase of 36.8% recorded by 0.5 wt.% IONP + 0.3 g nano-PAC and the lowest increasing margin of 10.5% for 1.5 wt.% IONP + 0.25 g nano-PAC. 3.5 wt.% IONP (without nano-PAC) and 0.35 g of nano-PAC (without IONP) improved the YP of the base mud by 26.3% and 31.6%, respectively. After hot rolling, three mud samples showed a decrease in YP as compared to the base mud and the highest increase in YP of 33% was achieved by the 1.5 wt.% IONP + 0.25 g nano-PAC and the lowest increase of 7% was achieved by 2.5 wt.% IONP + 0.2 g nano-PAC. Moreover, by using the IONP and nano-PAC alone, the YP of the base mud reduced by 26.7% and 20%, respectively. The improvement in the YP of the base mud by the mixture of IONP and nano-PAC before and after hot rolling is due to the the compabitibility of the chemical contaminants in the mud, such as NaOH that helped the particles to interact effectively with the molecules of WBM (Nasiri and Jafari, 2016). In general, the YP results indicate that the mixture of IONP and nano-PAC in the base mud is optimal and more stable compared to IONP and nano-PAC alone.
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+Figure 7View largeDownload slideYield point of drilling muds before and after hot rolling testsFigure 7View largeDownload slideYield point of drilling muds before and after hot rolling tests Close modal
+
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+10-s gel strength and 10-min gel strength
+
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+The attitude of drilling fluid when drilling operation is halted or paused is dictated by its 10-s and 10-min GS. Thus, an optimal gel level is the basis to suspend drilled cuttings effectively and avoid sagging of weighted material (Darley and Gray, 1988). The effect of adding different concentrations of IONP and nano-PAC to dtermine 10-s and 10-min GS of the WBM at 80 °F and 250 °F temperatures are shown in Figures 8 and 9, respectively. At ambient conditions, 10-s and 10-min GS were substantially higher compared to the base mud. For the 10-s GS, the highest gel showed 46% increment over the base mud and it occurred with the 3.5 wt.% IONP mud (without nano-PAC) and the lowest margin of increment of 8% was achieved by the 0.35 g nano-PAC mud (without IONP). The nature of the gels are jelly-like as the IONP and nano-PAC increased the attraction between the particles. After aging, the GS of all the mud samples assumed reducing trends over those without aging and they were significantly affected by temperature, especially the 0.35 g nano-PAC. At high temperature, the behaviour of the GS is similar to that before aging. An increase of 78% was seen for both 3.5 wt.% IONP and 2.5 wt.% IONP + 0.2 g nano-PAC, whereas a decrease of 11% and 44% by the 0.5 wt.% IONP + 0.3 g nano-PAC and 0.35 g nano-PAC, respectively, was observed.
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+Figure 8-10-sView largeDownload slideGel strenth before and after hot rolling testsFigure 8-10-sView largeDownload slideGel strenth before and after hot rolling tests Close modal
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+Figure 9 shows the 10-min GS. For the 10-min GS, the presence of IONP and nano-PAC modified the GS of the base mud. A margin of 24% and 3% increase was achieved by 3.5 wt.% IONP (without nano-PAC) and 0.35 g nano-PAC (without IONP), respectively. The IONP and nano-PAC mixture (Mud #5) containing 0.5 wt.% IONP + 0.3 g nano-PAC has higher gel than other IONP and nano-PAC mixtures (Mud #3 and Mud #4) and it improved the GS of the base mud by 17.6%. After hot rolling, two samples (Mud #5 and Mud #6) showed a drop in the 10-min GS over the base mud, while the other remaining three samples showed an increase in GS of the base mud. An increase of 52% was recorded by the 3.5 wt.% IONP and 1.5 wt.% IONP + 0.25 g nano-PAC, whereas a decrease of 5% and 29% was noted with 0.5 wt.% IONP + 0.3 g nano-PAC and 0.35 g nano-PAC, respectively. These modifications in the GS of WBMs is again attributed to the stable and well dispersed particles (Aftab et al., 2016). The GS of the base mud was strengthened by the inclusion of IONP and nano-PAC, which led to increased linking with the particles within the 10-s and 10-min period to create a stiff network. This surely enhanced the gel property (Oseh et al., 2019b).
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+Figure 9View largeDownload slide10-min gel strength of drilling muds before and after hot rolling testsFigure 9View largeDownload slide10-min gel strength of drilling muds before and after hot rolling tests Close modal
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+Filtrate loss volumen under LPLT and HPHT
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+
+Drilling fluid filtration properties hinge on the nature and amount of colloids interaction in the fluid. Thus, better fuid loss control agent can be obtained when a considerable dosage of colloids is introdueced in the mud. Figure 10 presents the LPLT FL and HPHT FL of complex WBM systems with different concentrations of IONP and nano-PAC measured at 80 °F before aging and 250 °F after thermal aging process. At LPLT conditions, the FL of the base mud of 10.2 ml was decreased by 36% to 6.5 ml and 32.4% to 6.9 ml by 2.5 wt.% IONP + 0.2 g nano-PAC (Mud #3) and 1.5 wt.% IONP + 0.25 g nano-PAC (Mud #4), respectively. It also decreased by a very small amount of 7.0% and 2.0% by 3.5 wt.% IONP and 0.35 g nano-PAC, respectively. However, with a mixture of 0.5 wt.% IONP + 0.3 g nano-PAC, it increased to 10.5 ml from 10.2 ml (Mud #1) by 3%. It is observed that both IONP and nano-PAC has little impact in the LPLT FL of the base mud possibly due to their very low concentration in the base mud. However, when the mixture concentrations contain both the IONP and nano-PAC in the base mud, the FL of the base mud was better reduced.
+
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+At HPHT conditions, the FL reducing strength of IONP and nano-PAC reduced and the HPHT FL of all the mud samples increased. Nevertheless, the presence of IONP and nano-PAC decreased the HPHT FL but the highest reduction was achieved by 0.5 wt.% IONP + 0.3 g nano-PAC (Mud #5), which reduced the HPHT FL of the base mud (Mud #1) of 17.6 ml to 11 ml by 37.5%. The minimum drop in the HPHT FL of 21% over the base mud was achieved by 1.5 wt.% IONP + 0.25 g nano-PAC (Mud #4). The IONP and nano-PAC when used alone in the mud at HPHT conditions recorded 20.5% and 26.4%, reduction in HPHT FL, respectively. From this result, it appears that the very low dosage of both the IONP and nano-PAC was not able to efficiently interconnect seamlessly with each other to create a stiff film that can prevent fluid leakage into the drilled formation (Oseh et al., 2019a). For a drilling fluid to be desired, the filtration control agent applied should confer fluid loss volume below 15 ml over 30 minutes period (API RP 13B-1., 2017). Thus, the use of pure IONP, nano-PAC, and IONP and nano-PAC mixture under LPLT and HPHT conditions for reduction in fluid loss is desirable.
+
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+Figure 10View largeDownload slideCumulative filtrate volume of drilling muds before and after hot rolling testFigure 10View largeDownload slideCumulative filtrate volume of drilling muds before and after hot rolling test Close modal
+
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+Mud cake thickness under LPLT and HPHT
+
+
+Figure 11 presents the LPLT MCT and HPHT MCT of complex WBM systems with different concentrations of IONP and nano-PAC measured at 80 °F before aging and 250 °F after thermal aging process. For the LPLT MCT, there was no change in the MCT of the base mud with 3.5 wt.% IONP, 1.5 wt.% IONP + 0.25 g nano-PAC, and 0.5 wt.% IONP + 0.3 g nano-PAC. However, there was a 50% and 100% increment in LPLT MCT of 2.5 wt.% IONP + 0.2 g nano-PAC and 0.35 g nano-PAC mud samples, respectively. All the LPLT MCT are found not to be more than 2.0 mm, indicating a low and thin pearmeable cake desirable for fluid loss control (API RP 13B-1, 2017).
+
+
+Under HPHT conditions, the maximum drop in MCT of 33% was observed with the 0.35 g nano-PAC, followed by a drop of 17% by 3.5 wt.% IONP, and then 0.5 wt.% IONP + 0.3 g nano-PAC. For the remaining two samples, there was no change in the HPHT MCT. The improvement in fitration properties with IONP and nano-PAC is because with the right size of NPs (30 nm size of IONP) together with a correct filtration control additive, such as PAC-R (nano-PAC size range of 50 – 130 nm), the particles can reduce the interaction between the rock and fluid. The IONP and nano-PAC worked by pracically shutting off the movement of water between the wellbore and the formation (Oseh et al., 2020a). In this case, an extra layer is formed by the IONP on the mud cake, which prevents further loss of water (Mao et al., 2015).
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+Figure 11View largeDownload slideMud cake thicness of drilling muds before and after hot rolling testFigure 11View largeDownload slideMud cake thicness of drilling muds before and after hot rolling test Close modal
+
+
+Compairing between the mud properties of IONP, PAC-R, and nano-PAC
+
+
+It was observed that the low concentration of 3.5 wt.% IONP and 0.35 g PAC-R seemed not to affect the filtration properties of the base mud, especially the LPLT FL and HPHT FL. Therefore, another three mud samples using 1.0 g concentration of each of IONP, PAC-R, and nano-PAC was added separately into the base mud (Mud #1) to identify their impact in the mud solution. PV, YP, 10-s and 10-min GS, and FL at 100 °F were measured without thermal aging process and the results from these measurements are presented in Figure 12.
+
+
+It can be seen that the presence of IONP, PAC-R, and nano-PAC improved both the rheological and filtration properties of the base mud. The PV of the IONP is the highest compared to the other two mud samples of PAC-R and nano-PAC, but it has the lowest YP compared to the two anionic cellulosic polymer. PAC-R has a more improved PV than nano-PAC but its YP is lower to that of nano-PAC. Both the 10-s and 10-min GS of the IONP and anionic cellulose polymers (PAC-R and nano-PAC) followed the same increasing trends over the base mud. The most improved gel is recorded by the nano-PAC both at 10-s and 10-min follwed by PAC-R before IONP.
+
+
+For the FL, nano-PAC achieved the greatest reduction in the FL of the base mud by 63%, followed by PAC-R by 57% before IONP that showed 36% reduction. The improvement in the PV of WBM with 1.0 g of PAC-R is due to the ability of the carboxyl groups in the PAC molecules to endow strong dispersión in water that led to increase in friction between the particles (Fereydouni et al., 2012). PAC is used as the main filtration control additive in most WBMs. Water soluble polymers, such as PAC are good filtration agents as they have the ability to form a desirable thin-low mud cake that can effectively block the water movement between the formation and the wellbore (Darley and Gray, 1988). The presence of nano-PAC of nanosize between 50 and 130 nm in the mud was able to increase the absorption capacity of water and cause strong water dispersión through its carboxyl groups that increased the friction between the particles for the viscosity increment (Fereydouni et al., 2012). Thus, the nano-PAC has good low water loss feature that helped to minimize the volume of water required to seep into the productive zone (Fereydouni et al., 2012). The viscosity increment by the IONP is due to its ability to induce increased friction between the particles (Vryzas et al., 2016). Further, the IONP extreme small size of 30 nm is well dispersed in the mud and it provided a good overlap of particles that support in shutting off the movement of water between the wellbore and the formation, thereby reducing the FL at 100 °F (Blkoor et al., 2022).
+
+
+Overall, the impact of IONP and nano-PAC in the base mud is evident and while the IONP showed more improved PV, the nano-PAC is more desirable for fluid loss control. It is also observed that for máximum or optimum performance of IONP and nano-PAC, their concentrations must be up to 1.0 g, as their lower concentrations did not show any significant reduction in fluid loss and good enhancement properties of the drilling mud.
+
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+Figure 12View largeDownload slideComparison between the PV, YP, 10-s GS, 10-min GS, and FL of 1.0 g each of IONP, PAC-R, and nano-PAC at a constant temperature of 100 °F without termal aging.Figure 12View largeDownload slideComparison between the PV, YP, 10-s GS, 10-min GS, and FL of 1.0 g each of IONP, PAC-R, and nano-PAC at a constant temperature of 100 °F without termal aging. Close modal
+
+
+Conclusions
+
+
+This work investigated the effect of adding IONP, PAC-R, and nano-PAC to complex WBM for filtration properties control. The rheological and filtration properties of the WBM with different amounts of IONP, nano-PAC, and mixture of IONP and nano-PAC were examined at 80 °F and 250 °F temperatures. Thereafter, 1.0 g concentration each of IONP, PAC-R, and nano-PAC was separately added into the complex WBM (Mud #1) to examine their PV, YP, 10-s GS, 10-min GS, and FL integrity at 100 °F. Follwing the study goal, the following conclusions are reached:
+
+
+The wet ball-milling process was effective in processing PAC-R of size range 140 µm – 700 µm to nano-PAC of size range between 50 nm and 130 nm diameter.The addition of IONP, nano-PAC, and mixture of IONP and nano-PAC improved the rheological and filtration properties of the WBM, in which the greatest rheological and filtration control properties improvement is found within the IONP and nano-PAC mixture.At high temperature, the mixture of IONP and nano-PAC has no effect on the PV of the WBM as the values are constant at 16 cP but with 3.5 wt.% IONP (without nano-PAC) and 0.35 g nano-PAC (without IONP), the PV of the WBM of 16 cP increased to 22 cP by 37.5% and 18 cP by 12.5%, respectively.wt.% IONP + 0.25 g nano-PAC is the most promising composition among all the mud samples formulated. This is because it had an even distribution of both the NPs and nano-PAC in the mud, and it also provide consistent and desirable results.At this concentration, the LPLT FL of the WBM of 10.2 ml was decreased to 6.9 ml by 32.4%. However, the concentration of 2.5 wt.% IONP + 0.2 g nano-PAC exhibited the largest reduction in LPLT FL to 6.5 ml by 36%.With 1.0 g at 100 °F, the IOPN, PAC-R, and nano-PAC improved both the rheology and fluid loss of the base mud. The nano-PAC achieved the greatest reduction in the FL of the base mud by 63%, followed by PAC-R by 57% before IONP that showed 36% reduction.The impact of IONP and nano-PAC in the base mud is evident and while the IONP showed more improved PV, the nano-PAC is more desirable for fluid loss control when 1.0 g at 100 °F was used.Overall, for máximum or optimum performance of IONP and nano-PAC, their concentrations must be up to 1.0 g, as lower concentrations of these particles did not show any significant reduction in fluid loss and good enhancement properties of drilling mud.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgements
+
+
+The authors wish to thank the Ministry of Higher Education Malaysia (MOHE) and Universiti Teknologi Malaysia Research Management Centre f(UTM-RMC) for funding this project under the Fundamental Research Grant Scheme (FRGS) (Ref. No: FRGS/1/2019/TK05/UTM/02/20)
+
+
+Nomenclature
+
+
+NomenclatureAbbreviationExpansion 10-min GS10-minute gel strenth CaCO3Calcium carbonate CMCCarboxy methyl cellulose CNTCarbon nanotube CuOCopper oxide FLFluid loss volumen HPHTHigh pressure high temperature HPHTFL High pressure high temperature fluid loss IONPIron oxide nanoparticle LPLTFL Low pressure low temperature fluid loss LPLTLow pressure low temperature MCTMud cake thickness Mud#1 Base mud NaOHCaustic soda NPsNanoparticles OBMOil-based mud PAC-RPolyanionic cellulose reagent PVPlastic viscosity SiO2NPNanosilica WBMWater-based mud XGXanthan gum YPYield point ZnOZinc oxide EOREnhanced oil recovery Al2O3NPAluminium oxide nanoparticle Fe2O3NPIron oxide nanoparticle Mud #2WBM + 3.5 wt.% IONP + 0.0 g nano-PAC Mud #3WBM + 2.5 wt.% IONP + 0.2 g nano-PAC Mud #5WBM + 0.5 wt.% IONP + 0.3 g nano-PAC Mud #4WBM + 1.5 wt.% IONP + 0.25 g nano-PAC Mud #6WBM + 0.0 wt.% IONP + 0.35 g nano-PAC 10-s GS10-second gel strenth
+
+
+References
+
+
+Abduo, M.I., Dahab, A.S., Abuseda, H., Abdulaziz, M.A., Elhossieny, M.S., (2016). Comparative study of using Water-Based mud containing Multiwall Carbon Nanotubes versus Oil-Based mud in HPHT fields. Egyptian Journal of Petroleum, 25(4), pp.459–464.Google ScholarCrossrefSearch ADS  Aftab, A., Ismail, A.R., Ibupoto, Z.H., (2016). Enhancing the rheological properties and shale inhibition behavior of water-based mud using nanosilica, multi-walled carbon nanotube, and graphene Nano platelet. Egyptian Journal of Petroleum. 10.1016/j.ejpe.2016.05.004.Google Scholar Agi, A., Junin, R., Abdullah, M.O., Jaafar, M.Z., Arsad, A., Wan Sulaiman, W.R., Norddin, M.N.A.M., Abdurrahman, M., Abbas, A., Gbadamosi, A., Oseh, J., (2020). Application of polymeric nanofluid in enhancing oil recovery at reservoir condition. J. Pet. Sci. Eng. 2020, 194, 107476. https://doi.org/10.1016/j.petrol.2020.107476.Google ScholarCrossrefSearch ADS  Amanullah, M., AlArfaj, M.K., Al-abdullatif, Z. A., (2011). 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Nanoparticle-based drilling fluids for minimizing formation damage in HP/HT applications. In Proceedings of the International Conference and Exhibition on Formation Damage Control, 24-26 February, Lafayette, LA, USA.Google Scholar Mao, H., Qiu, Z., Shen, Z., Huang, W., (2015). Hydrophobic associated polymer based silica nanoparticles composite with core-shell structure as a filtrate reducer for drilling fluid at ultra-high temperature, Journal of Petroleum Science and Engineering, 129, pp.1–14.Google ScholarCrossrefSearch ADS  Nasiri, M., Jafari, I., (2016). Investigation of drilling fluid loss and its affecting parameters in one of the Iranian gas. Bulgarian Chemical Communications, 48(D), pp.306–312.Google Scholar Nwaoji, C.O., Hareland, G., Husein, M., Nygaard, R., Zakaria, M.F., (2013). Wellbore strengthening-nano-particle drilling fluid experimental design using hydraulic fracture apparatus. In Proceedings of the SPE/IADC Drilling Conference and Exhibition, 5-7 March, Amsterdam, The Netherlands.Google Scholar Oseh, J.O., Norddin, M.N.A., Ismail, I., Gbadamosi, A.O., Agi, A., Ismail, A.R., (2020b). Experimental Investigation of Cuttings Transportation in Deviated and Horizontal Wellbores using Polypropylene-Nanosilica Composite Drilling Mud. J Pet Sci Eng., 189, 106958.Google ScholarCrossrefSearch ADS  Oseh, J.O., Norddin, M.N.A., Ismail, I., Gbadamosi, A.O., Agi, A., Ismail, A.R., (2020c). Study of Cuttings Lifting with Different Annular Velocities using Partially Hydrolyzed Polyacrylamide and Enriched Polypropylene-Nanosilica Composite in Deviated and Horizontal Wells. Applied Nanoscience, 10, pp.971–993Google ScholarCrossrefSearch ADS  Oseh, J.O., Norddin, M.N.A., Ismail, I., Gbadamosi, A.O., Agi, A., Muhammed, H.N., (2019a). A Novel Approach to Enhance Rheological and Filtration Properties of Water-Based Mud using Polypropylene-Silica Nanocomposite. J Pet Sci Eng., 181, 106264.Google ScholarCrossrefSearch ADS  Oseh, J.O., Norddin, M.N.A., Ismail, I., Ismail, A.R., Gbadamosi, A.O., Agi, A., (2020a). Effect of the Surface Charge of Entrapped Polypropylene at Nanosilica composite on Cuttings Transport Capacity of Water Based Muds. Applied Nanoscience, 10, pp.61–82.Google ScholarCrossrefSearch ADS  Oseh, J.O., Norddin, M.N.A., Muhammed, H. N, Ismail, I, Gbadamosi, A.O., Agi, A., Ismail, A.R., Blkoor, S.O., (2020d). Influence of (3-Aminopropyl) triethoxysilane on entrapped polypropylene at nanosilica composite for shale swelling and hydration inhibition. J Pet Sci Eng., 194, pp.1–16. https://doi:10.1016/j.petrol.2020.107560Google Scholar Oseh, J.O., Norrdin, M.N.A.M., Farooqi, F., Ismail, A.R., Ismail, I., Gbadamosi, A.O., Agi, A., (2019b). Experimental investigation of the effect of henna leaf extracts on cuttings transportation in highly deviated and horizontal wells. J Petrol Explor Prod Technol. 9, pp.2387–2404. https://doi.org/10.1007/s13202-019-0631-zGoogle ScholarCrossrefSearch ADS  Saboori, R., Sabbaghi, S., Kalantarias, A., Mowla, D., (2018). Improvement in filtration properties of water-based drilling fluid by nanocarboxymethyl cellulose/polystyrene core-shell nanocomposite. Journal of Petroleum Exploration and Production Technology. 21, pp.43–52.Google Scholar Singh, R., Dutta, S., (2018). Synthesis and characterization of solar photoactive TiO2 nanoparticles with enhanced structural and optical properties. Adv. Powder Technol. 9(2), pp.211–219. https://doi.org/10.1016/j.apt.2017.11.005Google ScholarCrossrefSearch ADS  Taha, N.M., Lee, S., (2015). Nano graphene application improving drilling fluids performance. In Proceedings of the International Petroleum Technology Conference (IPTC 18539), 6-9 December, Doha, Qatar.Google Scholar Vryzas, Z., Arkoudeas, P., Kelessidis, V.C., (2014). Improvement of drilling fluid flow parameters using nanoparticles for optimization of drilling process. In Proceedings of the International Conference on Safe and Sustainable Nanotechnology, 14-17 October, Phitsanulok, Thailand.Google Scholar Vryzas, Z., Arkoudeas, P., Mahmoud, O., Nasr-El-Din, H.A., Kelessidis, V. C., (2015). Utilization of IONP in drilling fluids improves fluid loss and formation damage characteristics. In Proceedings of the First EAGE Workshop on Well Injectivity & Productivity in Carbonates (WIPIC), 31 March-1 April, Doha, Qatar.Google Scholar Vryzas, Z., Kelessidis, V.C., (2017). Nano-based drilling fluids: A review. Energies, 10(4). https://doi.org/10.3390/en10040540Google Scholar Vryzas, Z., Wubulikasimu, Y., Gerogiorgis, D., Kelessidis, V.C., (2016). Understanding the temperature effect on the rheology of water-bentonite suspensions. In Proceedings of the Nordic Polymer Days and Nordic Rheological Conference, 30 May-1 June, Helsinki, Finland.Google Scholar 
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211924-MS
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files/2022/Enhancing Reservoir Stimulation through Mathematical Remodeling of Pre-Flush Acidizing Volume Algorithm for Different Reservoir Flow Geometries.txt

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+----- METADATA START -----
+Title: Enhancing Reservoir Stimulation through Mathematical Remodeling of Pre-Flush Acidizing Volume Algorithm for Different Reservoir Flow Geometries
+Authors: Justice Chidera Osuala, Daniel Ikechukwu Egu, Anthony John Ilozobhie, Blessing Ogechi Nwojiji
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211916-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Studies show that an average of 35% of reservoir acid stimulation operations executed every year fails because of limited knowledge of downhole acid placement. Existing models designed for acid pre-flush volumes are limited to Linear, Radial and Ellipsoidal reservoir geometries, therefore, do not account for geological drifts of a typical heterogenic reservoir. This can be erroneous while estimating acid placement volumes as reservoirs can deviate from defined flow geometries due to their dynamic and heterogeneous nature, thereby challenging to estimate acid volumes precisely for stimulations. This study aims to foster sustainability in reservoir flow engineering by deriving a mathematical model that evaluates volumes for reservoirs with flow geometries that deviate from linear and radial. This was established to help introduce a new geometry contributing to the accountability of complex and heterogeneous reservoirs. Sensitivity analysis and investigation using reservoir core data from SPDC Petroleum Chemistry Laboratory were carried out to understand the relationship between Linear, Radial and Modified flow geometries. Analytical results for linear, radial and the fied were generated. These results proved the precision of the modified equation for calculating pre-flush acid volume for reservoir acid stimulation operation.
+
+
+
+
+Keywords:
+drillstem testing,
+result,
+production enhancement,
+downhole intervention,
+upstream oil & gas,
+geometry,
+accuracy,
+regression,
+parameter,
+radial flow geometry result
+
+
+Subjects: 
+Formation Evaluation & Management,
+Acidizing,
+Drillstem/well testing,
+Well Intervention,
+Well Operations, Optimization and Stimulation
+
+
+
+
+Introduction
+
+
+Any unintended impedance to the flow of fluids into or out of a wellbore is referred to as Formation Damage (Mukul, 2007). These damages cause skin production (Amanat, 2004 & Egu, 2014). Well stimulation is a technique used to improve fluid flow from the reservoir and enhance well productivity by dissolving the rock or creating new channels around the wellbore (Schechter, 1992; Schlumberger, 2000 & Crowe et al., 1992).
+
+
+The most applied stimulation techniques are.
+
+
+❖Matrix treatment (Acidizing):❖Hydraulic fracturing (Halliburton, 2000).
+
+
+In carrying out Matrix Treatment Stimulation process, HF acid mixture is used to clear the wellbore of skins. Smith and Hendrickson (1965) illustrated the reactive nature of HF acid with silica which makes it exceptional in the application of sandstone acidizing. Hydrochloric, sulfuric, and nitric acids do not react effectively to the sandstone formation, thereby causing precipitates. Interestingly, this can further cause reduced permeability (Gomez, 2006 & Mohammed et al., 2020). Dowell Division of Dow Chemical discovered that sandstone formation particles such as sand grains, feldspar and clays react with HF acid which results to precipitate production that poses as wellbore damages (USOSTI, 1965 & Frenier and Hill, 2002). To this effect, the need to prepare the wellbore with precipitate-control mixtures known as Pre- flush Acids is important to avoid further damage during acid stimulation. Pre-flush containing approximately 15%wt of HCL acid is usually injected into a well before the HF/HCL mixture is injected for sandstone acidizing treatment (Abdelmoneim and Nasr-El-Din, 2015). This is used to prevent the reaction of hydrofluoric acid (HF) with formation (Al-Harthy, 2008). The stages of safe acid placement for matrix acid stimulation are Pre-flush, Main Acid, and Post-Flush (Gomaa et al., 2015). In placing pre- flush acids, the knowledge of the reservoir geometry is usually needed in calculating the volume of acid to be injected the and injection rate (Boyun, et al., 2007 & Egu and Ilozobhie, 2021). Existing volume calculation assumes linear, radial, and ellipsoidal reservoir geometries. Investigations prove that reservoir geometry varies because of geological deformations that change the geo-orientation of a reservoir (Hussein, 2016 & Ilozobhie and Egu, 2020). To this effect, the available calculation models designed based on assumptions that the reservoir flow geometry is either linear, radial or ellipsoidal can increase errors in estimating pre-flush volume and injection rates. A new model for linear-Radial flow geometry is developed in this paper for the estimation of pre-flush volume and flow rate for reservoirs with linear- Radial (Modified) flow geometry. This will reduce errors in volume estimation.
+
+
+This study aims to design and comparatively evaluate the volumes of pre-flush acids for acid stimulation and develop a model for a reservoir with flow geometry that deviates from linear or radial flow profile assuming a liner-radial flow geometry (modified flow geometry).
+
+
+To achieve this aim, the following objectives are considered.
+
+
+To design a suitable and convenient mathematical algorithm for linear-radial flow deviation geometry of a typical Niger-delta reservoir.To carry out sensitivity analysis and investigate comparatively the relationship between linear, radial, and Modified flow geometries.
+
+
+Materials and Description of Processes Materials
+
+
+Microsoft Office Excel.Statistical sensitivity analysis software (SPSS)Reservoir core data from Shell Petroleum Development Company of Nigeria Ltd. (SPDC) Petroleum Chemistry Laboratory.
+
+
+Processes Developing Average Acid Volume for Linear Geometry
+
+
+An equation for calculating acid volume for linear flow geometry is given and contains eight parameters with varying values. These parameters are shown in Table 1 below.Parameters as shown in Table 1 were entered into the Acid volume calculator for linear flow for different values assigned to each of them. Results were entered into the Microsoft Office Excel spreadsheet following the procedures below.Each of the linear flow geometry parameters contains nine values.Taking porosity for example, each value for porosity was varied as other parameters were kept constant.Acidvolumewasgeneratedforeachvaluebeenvariedasotherparameterswerekeptconstant.This gave a total of nine results of acid volume for a particular value of porosity.This is repeated for other values of porosity.An average is taken for the nine results of acid volume for all varying values of porosity and plots with mathematical equations were generated.The result of the average acid volume for each porosity value is entered in a separate column of the excel spreadsheet and an average of these results was taken; this was now called "Average Acid Volume While Varying Porosity".This was repeated for all the parameters in the linear flow geometry.After which, a sheet was created where all the average acid volume while varying porosity was entered. This was used to generate a plot.
+
+
+Table 1Parameters for Acid Volume Estimation in Linear Flow Geometry   View Large
+
+
+Developing Average Acid Volume for Radial Geometry
+
+
+An equation for calculating acid volume for radial flow geometry is given and contains seven parameters with varying values. These parameters are shown in Table 2 belowParameters as shown in Table 2 were entered into the Acid volume calculator for radial flow for different values assigned to each of them. Results were entered into the Microsoft OfficeExcell spreadsheet following the procedures below.Each of the radial flow geometry parameters contains seven values.Taking wellbore radius for example, each value for wellbore radius was varied as other parameters were kept constant.Acid volume was generated for each value been varied as other parameters were kept constant. This gave a total of seven results of acid volume for a particular value of porosity.This is repeated for other values of wellbore radius.An average is taken for the seven results of acid volume for all varying values of wellbore radius and plots with mathematical equations were generated.The result of the average acid volume for each wellbore radius value is entered in a separate column of the excel spreadsheet and an average of these results was taken; this was now called "Average acid volume while varying wellbore radius.This was repeated for all the parameters in the radial flow geometry After which, a sheet was created where all the average acid volume while varying wellbore radius was entered. This was used to generate a plot.
+
+
+Table 2Parameters for Acid Volume Estimation in Radial Flow Geometry   View Large
+
+
+Developing Acid Volume for Modified Geometry
+
+
+The acid volume estimation considering modified flow geometry was created using the mathematical method of a Combination of Equations. The linear and radial flow geometry equations for acid volume estimation were combined to generate an equation for the modified flow equation.
+
+
+An equation for calculating acid volume for modified flow geometry is generated and contains ten parameters with varying values. These parameters are shown in Table 3 below.Parameters as shown in Table 3 were entered into the Acid volume calculator for modified flow for different values assigned to each of them. Results were entered into the Microsoft Office Excel spreadsheet following the procedures below.Each of the modified flow geometry parameters contains ten values.Taking acid flux for example, each value for acid flux was varied as other parameters were kept constant.Acid volume was generated for each value been varied as other parameters were kept constant. This gave a total of ten results of acid volume for a particular value of acid flux.This is repeated for other values of acid flux.An average is taken for the ten results of acid volume for all varying values of acid flux and plots with mathematical equations were generated.The result of the average acid volume for each acid flux value is entered in a separate column of the excel spreadsheet and an average of these results was taken; this was now called "Average acid volume while varying acid flux.This was repeated for all the parameters in the modified flow geometry.After which, a sheet was created where all the average acid volume while varying acid flux was entered. This was used to generate a plot.
+
+
+Table 3Parameters for Acid Volume Estimation in Modified Flow Geometry   View Large
+
+
+Comparative Analysis of Acid Volume for Modified, Radial and Linear Flow Geometries
+
+
+Related parameters in acid volume for linear, radial, and modified flow geometries were entered into a separate excel spreadsheet with corresponding values of average acid volume gotten when those parameters were varied for each of the flow geometriesRelated parameters in acid volume for linear and modified flow geometries were entered in a separate excel spreadsheet with corresponding values of average acid volumeRelated parameters in acid volume for radial and modified flow geometries were entered in a separate excel spreadsheet with corresponding values of average acid volumeGraphical plots were generated for each case to compare the variations in existing flow geometries (linear & radial) with the modified flow geometry.
+
+
+Results and Discussion Results
+
+
+The resulting information from the analysis estimation for pre-flush acidization volume for different flow reservoir geometries was conclusive as different parameters were varied to estimate the volume of acid. The modified flow geometry was further analyzed and was discovered that it added value to the method for the volume of acid estimation.
+
+
+Results of Acid Stimulation for Linear Flow Geometry Results of average acid volume with Specific Surface Area of the reservoir
+
+
+This gave a linearly increasing profile from 55.53gal/ft at a specific surface area of 11.1 to a maximum value of 160.98gal/ft at a specific area of 19. The mathematical modeled result is given as; y = −0.023x6 + 2.103x5 – 77.85x4 + 1525x3 – 16678x2 + 96531x – 23099; with a square regression of 0.996, this indicates an appreciable degree of accuracy as shown in Figure 1.
+
+
+Figure 1View largeDownload slideAverage Volume of Acid Varying Specific Surface Area in Linear Flow GeometryFigure 1View largeDownload slideAverage Volume of Acid Varying Specific Surface Area in Linear Flow Geometry Close modal
+
+
+Results of average acid volume with Acid Flux (U)
+
+
+This gave a linearly decreasing profile from 9.37484E+15gal/ft at an acid flux of 0.011ft/min to a minimum value of 24352.1735gal/ft at an acid flux of 0.03ft/min. It maintained an insignificant declination from an acid flux of 0.03 ft/min to 0.09 ft/min at an average acid volume of 243252.1735gal/ft to a minimum value of87.22975637gal/min. The mathematicamodelleded result is given as. Y = 24x6 + 24x5 + 23x4 + 22x3 + 20x2 + 18x + 16; with a square regression of 0.996, this proves a good degree of accuracy as shown in Figure 2.
+
+
+Figure 2View largeDownload slideAverage Volume of Acid Varying Acid Flux in Linear Flow GeometryFigure 2View largeDownload slideAverage Volume of Acid Varying Acid Flux in Linear Flow Geometry Close modal
+
+
+Results of average acid volume with Penetrating Distance in the reservoir
+
+
+This generated an increasing profile of acid volume from 64.516gal/ft to a maximum value of 152.514gal/ft with an increase in penetrating distance from 0.26ft to 0.34ft. The mathematical model obtained from the plot is given as. Y = 25014x3 – 16400x2 + 4146x – 3445. With a squared regression of 1, this indicates a high level of accuracy as shown in Figure 3.
+
+
+Figure 3View largeDownload slideAverage Volume of Acid Varying Penetrating Distance in Linear Flow GeometryFigure 3View largeDownload slideAverage Volume of Acid Varying Penetrating Distance in Linear Flow Geometry Close modal
+
+
+Results of average acid volume with Porosity of the reservoir
+
+
+This showed an increase in the volume of acid from190.88gal/ft to a maximum value of 132.09gal/ft as porosity was increased from 20% to 43%. The mathematical model obtained is given as. Y = − 8662x4 + 13478x3 – 7576x2 + 1554x + 89.06; with a squared regression of 1indicating high level of accuracy as shown in Figure 4.
+
+
+Figure 4View largeDownload slideAverage volume of acid varying porosity in linear flow geometryFigure 4View largeDownload slideAverage volume of acid varying porosity in linear flow geometry Close modal
+
+
+Results of average acid volume with Volume Fraction
+
+
+This showed an increasing profile from 27.65gal/ft to a maximum value of 67380.2gal/ft at a volume fraction of 0.0006 to 0.08. The mathematical modeled result is given as; Y = 4E+16x5 – 7E+14x4 + 4E+12x3 – 1E+10x2 + 1E+07x – 5153; with a squared regression of 0.999, this indicates an allowable degree of accuracy as shown in Figure 5.
+
+
+Figure 5View largeDownload slideAverage Volume of Acid Varying Volume Fraction in Linear Flow GeometryFigure 5View largeDownload slideAverage Volume of Acid Varying Volume Fraction in Linear Flow Geometry Close modal
+
+
+Results of average acid volume with Length of Core
+
+
+This gave a constant acid volume of 123.39gal/ft at an increasing value of the length of the core from 0.137ft to 0.9ft as shown in Figure 6.
+
+
+Figure 6View largeDownload slideAverage Volume of Acid Varying Length of Core in Linear Flow GeometryFigure 6View largeDownload slideAverage Volume of Acid Varying Length of Core in Linear Flow Geometry Close modal
+
+
+Results from acid volume with Reaction Rate Constant
+
+
+This gave a curved increasing profile from 46.29gal/ft at a reaction rate of 7 to a maximum value 181.01gal/ft at a reaction rate of 15. The mathematical modelled result is given as; y = 0.135x3– 2.740x2+25.92x–47.44; with a squared regression of 1 indicating a high degree of accuracy as shown in Figure 7.
+
+
+Figure 7View largeDownload slideAverage Volume of Acid Varying Reaction Rate Constant in a Linear Flow GeometryFigure 7View largeDownload slideAverage Volume of Acid Varying Reaction Rate Constant in a Linear Flow Geometry Close modal
+
+
+Results of average acid volume with Acid Capacity Number (NAC,f)
+
+
+This gave a declining profile from 1137.79gal/ft with an acid capacity of 0.0071 to a minimum value of 90gal/ft with an acid capacity of 0.09. The mathematical modeled result is given as; y = −4E+9x5 + 1E+9x4 – 1E+8x3 + 6E+6x2 – 16970x + 2063. The degree of accuracy was measured with a squared regression of 0.999 indicating as shown in Figure 8.
+
+
+Figure 8View largeDownload slideAverage Volume of Acid Varying Acid Capacity Number in a Linear Flow GeometryFigure 8View largeDownload slideAverage Volume of Acid Varying Acid Capacity Number in a Linear Flow Geometry Close modal
+
+
+Results of Acid Stimulation for Radial Flow Geometry Results of acid stimulation with Acid Capacity (Nac,F)
+
+
+This gave a declining profile from 2295.4gal/ft at an acid capacity of 0.007 to a minimum value of 187.8gal/ft at an acid capacity of 0.09. The mathematical modelled result is given as. y = −7E+09X5 + 2E+09X4 – 2E+08X3 + 1E+07X2 – 34013X + 4129; with a squared regression of 0.999, this indicates a good degree of accuracy as shown in Figure 9.
+
+
+Figure 9View largeDownload slideAverage Volume of Acid Varying Acid Capacity Number in a Radial Flow GeometryFigure 9View largeDownload slideAverage Volume of Acid Varying Acid Capacity Number in a Radial Flow Geometry Close modal
+
+
+Results of acid volume with Damkohler's number
+
+
+This gave an increasing profile from 309.61gal/ft with Damkohler's number of 0.46 to a maximum value of 467.9gal/ft with a Damkohler's number of1.2. The mathematical modeled result is given as, y = 76.92x2 + 85.52x + 254.2. With a squared regression of 1, this indicates a high level of accuracy as shown in Figure 10.
+
+
+Figure 10View largeDownload slideAverage Volume of Acid Varying Damkohler's Number in a Radial Flow GeometryFigure 10View largeDownload slideAverage Volume of Acid Varying Damkohler's Number in a Radial Flow Geometry Close modal
+
+
+Results of acid volume with Acid Flux
+
+
+This gave a smooth curve of an increasing profile of 282.1gal/ft at an acid flux of 0.0114gal/min to a maximum value of 1367.0gal/ft at an acid flux of0.09gal/min. The mathematical modelled result while varying acid flux for radial flow is given as. y = −2E+07x4 – 2E+06x3 + 14630x2 + 478.9x + 260.8; with a squared regression of 1, this indicates a high degree of accuracy as shown in Figure 11.
+
+
+Figure 11View largeDownload slideAverage Volume of Acid Varying Acid Flux in a Radial Flow GeometryFigure 11View largeDownload slideAverage Volume of Acid Varying Acid Flux in a Radial Flow Geometry Close modal
+
+
+Results from acid volume with Length Of Core
+
+
+This gave a smooth curve declining profile from 583.3gal/ft at the core length of 0.5ft to a minimum value of 322.9gal/ft at the core length of1.3ft. The mathematical modelled result is given as. y = − 2254x5 – 11564x4 – 23764x3 + 24676x2 – 13196x + 3330, having a squared regression of 1; this indicates a high level of accuracy as shown in Figure 12.
+
+
+Figure 12View largeDownload slideAverage Volume of Acid Varying Length of Core in a Radial Flow GeometryFigure 12View largeDownload slideAverage Volume of Acid Varying Length of Core in a Radial Flow Geometry Close modal
+
+
+Result of acid volume while varying Wellbore Radius
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+
+This gave an almost linearly increasing profile from 220.1gal/ft to a maximum value of 720.4gal/ft at a wellbore radius of 0.3ft to 1.1ft. The mathematical equation generated is given as; y = 242x2 + 283.8x + 114.2. It generated a squared regression of 1 showing that the prediction is accurate as shown below in Figure 13.
+
+
+Figure 13View largeDownload slideAverage Volume of Acid Varying Wellbore Radius in a Radial Flow GeometryFigure 13View largeDownload slideAverage Volume of Acid Varying Wellbore Radius in a Radial Flow Geometry Close modal
+
+
+Result of acid volume with Acid Injection Rate
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+
+This gave a sharp declining profile from 54383.4gal/ft to 1420.7gal/ft at an acid injection rate of 0.1gal/min to 0.2gal/min respectively and maintained an insignificant declination from 626.7gal/ft to a minimum value of 308.5gal/ft with an acid injection rate of 0.3gal/min to0.9gal/min. The mathematical equation generated while varying acid injection rate in a radial flow is given as. y = 2E+07x6 – 6E+07x5 + 8E+07x4 −6E+07x3 + 2E+07x2 – 4E+06x + 28507. A squared regression of 0.99 was generated indicating that the prediction is accurate as shown below in Figure 14.
+
+
+Figure 14View largeDownload slideAverage Volume of Acid Varying Acid Injection Rate in a Radial Flow GeometryFigure 14View largeDownload slideAverage Volume of Acid Varying Acid Injection Rate in a Radial Flow Geometry Close modal
+
+
+Result of acid volume with Porosity
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+
+This gave a linearly increasing profile from 251.6gal/ft to a maximum value of 540.9gal/ft at a porosity of 20% to43%. The mathematical equation generated is given as. y = 2E-09x3 – 3E-09x2 + 1258x – 2E-10. A squared regression of 1 was generated indicating that the prediction is accurate as shown below in Figure 15.
+
+
+Figure 15View largeDownload slideAverage Volume of Acid Varying Porosity in Radial Flow GeometryFigure 15View largeDownload slideAverage Volume of Acid Varying Porosity in Radial Flow Geometry Close modal
+
+
+Results of Acid Stimulation for The Modified Flow Geometry Results of average acid volume with Volume Fraction
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+
+This gave a curved declining profile from 5949.5gal/ft to a minimum value of 2925.8gal/ft at Vs of 0.0006 to 0.003 and began to increase from 2925.8gal/ft to a maximum value of 4862.8gal/ft at Vs of 0.006 to 0.008. The mathematicmodelledled result is given as. y = 1E+18x6 – 4E+16x5 + 4E+14x4 – 3E+12x3 + 8E+09x2 – 1E+07x + 11176. The plot generated a squared regression of 0.998 indicating a high level of accuracy as shown in Figure 16 
+
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+Figure 16View largeDownload slideAverage Volume of Acid Varying Volume Fraction in Modified Flow GeometryFigure 16View largeDownload slideAverage Volume of Acid Varying Volume Fraction in Modified Flow Geometry Close modal
+
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+Results of average acid volume with the Rate of Reaction
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+
+This gave a declining profile from 4147.5gal/ft to a minimum value of 3382.6gal/ft at a reaction rate of 7min to 15min. The mathematical modelled result is given as. y = 0.130x4 – 7.115x3 + 154.0x2 – 1576x + 9759. The plot gave a squared regression of 1 indicating a high level of accuracy as shown in Figure 17 
+
+
+Figure 17View largeDownload slideAverage Volume of Acid Varying Reaction Rate Constant in Modified Flow GeometryFigure 17View largeDownload slideAverage Volume of Acid Varying Reaction Rate Constant in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with Specific Surface Area
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+
+This gave a declining profile from 4108.9gal/ft to a minimum value of 3536.6gal/ft at a specific surface area of 11.1 to a maximum value of 19. The mathematical modelled result is given as. y = −0.536x3 + 33.26x2 – 701.1x + 8524. The plot gave a squared regression of 1 indicating a high accuracy as shown in Figure 18.
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+
+Figure 18View largeDownload slideAverage volume of acid varying specific surface area in modified flow geometryFigure 18View largeDownload slideAverage volume of acid varying specific surface area in modified flow geometry Close modal
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+Results of average acid volume with Porosity
+
+
+This gave an increasing profile from3441.1gal/ft to a maximum value of 8654gal/ft at a porosity of 0.2 to 0.43. The mathematical modelled result is given as. y = 39182x3 – 14799x2 + 20120x – 297.2. The plot gave a squared regression of 1 indicating a high level of accuracy as shown in Figure 19.
+
+
+Figure 19View largeDownload slideAverage Volume of Acid with Porosity in Modified Flow GeometryFigure 19View largeDownload slideAverage Volume of Acid with Porosity in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with Length of Core
+
+
+This gave a curved declining profile from 15682.7gal/ft to a minimum value of 1334.8gal/ft at the length of core of 0.137ft to a maximum value of 0.9ft. The mathematical modelled result is given as. y = 2E+06x6 −7E+06x5 +9E+06x4 – 7E+06x3 +3E+06x2 – 61398x + 61928. The plot gave a squared regression of 0.99 indicating a good level of accuracy as shown in Figure 20.
+
+
+Figure 20View largeDownload slideAverage Volume of Acid with Length of Core in Modified Flow GeometryFigure 20View largeDownload slideAverage Volume of Acid with Length of Core in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with Acid Flux
+
+
+This gave a sharp decline from 312713gal/ft to 15349gal/ft and insignificantly dropped to the minimum value of 12632.5gal/ft at an acid flux of 0.0114ft/min to a maximum value of0.09ft/min. The mathematical modelled result is given as. y = 1E+14x6 – 5E+13x5 + 6E+12x4 – 4E+11x3 + 2E+10x2 −3E+08x + 2E+06. The plot gave a squared regression of 0.997 indicating a good level of accuracy as shown in Figure 21.
+
+
+Figure 21View largeDownload slideAverage Volume of Acid with Acid Flux in Modified Flow GeometryFigure 21View largeDownload slideAverage Volume of Acid with Acid Flux in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with an Acid Capacity Number
+
+
+This gave a declining profile from 19459gal/ft at the acid capacity of 0.0071 to a minimum value of 1535gal/ft at an acid capacity of0.09. The mathematical modeled result is given as; y = −6E+10x5 + 2E+10x4 –2E+09x3 + 1E+08x2 – 3E+6x + 35293. The plot gave a squared regression of 0.99 indicating a good level of accuracy as shown in Figure 22.
+
+
+Figure 22View largeDownload slideAverage Volume of Acid with Acid Capacity Number in Modified Flow GeometryFigure 22View largeDownload slideAverage Volume of Acid with Acid Capacity Number in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with Acid Injection Rate
+
+
+This gave an insignificantly declining profile from 4502.4gal/ft at an acid injection rate of 0.1gal/min to a minimum value of 4254gal/ft at an acid injection rate of0.9gal/min. The mathematical modelled result is given as. y = −12677x5 + 36775x4 – 41170x3 + 2228x2 – 5925x + 4909. Squared regression was 0.998 indicating a good degree of accuracy as shown in Figure 23.
+
+
+Figure 23View largeDownload slideAverage Volume of Acid with Acid Injection Rate in Modified Flow GeometryFigure 23View largeDownload slideAverage Volume of Acid with Acid Injection Rate in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with Penetrating Distance
+
+
+This gave a linearly increasing profile from 3756.7gal/ft at a penetrating distance of 0.26ft to a maximum value of 5142.8gal/ft at a penetrating distance of0.34ft. The mathematical modelled result is given as. y = 1102.1x2 + 10709x + 227.5. Squared regression was 1 indicating a high degree of accuracy as shown in Figure 24.
+
+
+Figure 24View largeDownload slideAverage Volume of Acid with Penetrating Distance in Modified Flow GeometryFigure 24View largeDownload slideAverage Volume of Acid with Penetrating Distance in Modified Flow Geometry Close modal
+
+
+Results of average acid volume with Wellbore Radius
+
+
+This gave an increasing profile from 2380.7gal/ft at a wellbore radius of 0.3ft to a maximum value of 25310.3gal/ft at the wellbore radius of 1.1ft. The mathematical modelled result is given as. y = −6938x3 + 28075x2 + 665.4x – 164.4. Squared regression was 1 indicating a high degree of accuracy as shown in Figure 25 
+
+
+Figure 25View largeDownload slideAverage Volume of Acid Varying Wellbore Radius in Modified Flow GeometryFigure 25View largeDownload slideAverage Volume of Acid Varying Wellbore Radius in Modified Flow Geometry Close modal
+
+
+Derived Mathematical Equations for Acid Volume Assuming Modified Flow Geometry (Linear – Radial Flow Geometry)
+
+
+Damkohler's Number linear – Radial flow geometry NDa,saverage=Vs∗Ef,s∗Ss∗0.5UL−1+0.0891∗3.143∗r2∗qi−1Dimensionless acid penetration distance linear – Radial flow geometry ELR=0.5XL−1+0.5r2rW−0.5Dimensionless acid volume linear – Radial flow geometry Θave=expNDa,saverage*ELR−1+ELR÷(NAC,F)∗NDa,saverageVolume of acid for linear – Radial flow geometry ΘaveL+3.143∗ h∗r2
+
+
+Evaluating Results for Acid Volume in Linear Flow Geometry
+
+
+Averaged acid volume took a drastic increase from 96.412gal/ft to a value of 1.04165E+15gal/ft and an instant drop to a low value of 102.7gal/ft then a minor variation in the values was maintained. The mathematical modeled result is given as y = −4E+12x6 + 1E+14x5 – 1E+15x4 + 7E+15x3 – 2E+16x2 + 3E+16x -2E+16. The plot showed a squared regression of 0.983 as indicated in Table 4 and Figure 26.
+
+
+Table 4Evaluating Results for Acid Volume in Linear Flow Geometry   View Large
+
+
+Figure 26View largeDownload slideEvaluating Results for Acid Volume in Linear Flow GeometryFigure 26View largeDownload slideEvaluating Results for Acid Volume in Linear Flow Geometry Close modal
+
+
+Evaluating Results for Acid Volume in Radial Flow Geometry
+
+
+The average volume of the acid plot gave a minor variation of increase and decrease in values of acid volume b ut took a sharp increase to 6510.50gal/ft due to high injection rate but dropped drastically to 399.76gal/ft due to a stable value of porosity. The mathematical modeled result is given as; y = − 44.51x6 + 961.2x5 – 8113x4 + 34069x3 – 74458x2 + 79384x – 31209. The plot showed a squared regression of 1 as indicated in Table 5 and Figure 27.
+
+
+Table 5Evaluating Results for Acid Volume in Radial Flow Geometry   View Large
+
+
+Figure 27View largeDownload slideEvaluating Results for Acid Volume in Radial Flow GeometryFigure 27View largeDownload slideEvaluating Results for Acid Volume in Radial Flow Geometry Close modal
+
+
+Evaluating Results for Acid Volume in Modified Flow Geometry
+
+
+The estimated volume of acid in the modified flow geometry had a minor variation of increase and decrease but took a sharp increase to 44380.7gal/ft due to high acid flux but dropped drastically to 4302gal/ft due to a controlled value of acid injection rate. The mathematical modelled result for acid volume in modified flow geometry is given as; y = 18.42x6 – 548.5x5 + 6204x4 – 33596x3 + 90762x2 – 11425x + 55186. The plot showed a squared regression of 0.474 as indicated in Table 6 and Figure 28.
+
+
+Table 6Evaluating Results for Acid Volume in Modified Flow Geometry   View Large
+
+
+Figure 28View largeDownload slideEvaluating Results for Acid Volume in Radial Flow GeometryFigure 28View largeDownload slideEvaluating Results for Acid Volume in Radial Flow Geometry Close modal
+
+
+Discussion
+
+
+Comparative Analysis of Linear, Radial, and Modified Flow
+
+
+The modified flow equation has been developed and was compared to other existing flow geometries of linear and radial nature. Related parameters in each of the flow geometries were used to make these comparisons.
+
+
+Comparing Related Parameters in All Flow Geometries
+
+
+A plot as shown in Figure 29 generated from Table 7 showed an obvious deviation in the linear flow from that of radial and modified flows. In Petroleum Engineering, acid volume estimation considers radial flow geometry because of the heterogeneity of the reservoir making the flow in the reservoir almost impossible to flow in a direction as in the case of the linear flow. Figure 29 indicates a high level of accuracy in the modified flow consideration as it coincides with the radial flow geometry.
+
+
+Table 7Comparing Related Parameters in all Flow Geometries   View Large
+
+
+Figure 29View largeDownload slideComparing Related Parameters in All Flow GeometriesFigure 29View largeDownload slideComparing Related Parameters in All Flow Geometries Close modal
+
+
+Comparing Related Parameters in Linear and Modified Flows
+
+
+Even though the related parameters in linear and modified flow geometries are more than that of radial and modified flow as indicated in Table 8, the degree of difference between the linear from the modified is high as shown in Figure 30 and Table 8. This indicates the level of heterogeneity of the modified flow geometry which is more valued in the estimation of acid volume.
+
+
+Table 8Comparing Related Parameters for Linear & Modified   View Large
+
+
+Figure 30View largeDownload slideComparing Related Parameters in Modified and Linear Flow GeometryFigure 30View largeDownload slideComparing Related Parameters in Modified and Linear Flow Geometry Close modal
+
+
+Comparing Related Parameters in Radial and Modified Flows
+
+
+The plot in Figure 31 generated from Table 9 shows a coincidental degree of the radial and modified flow geometries despite the few numbers of related parameters in the two flow geometries. This indicates that the modified flow geometry adds value to the estimation of acid volume.
+
+
+Figure 31View largeDownload slideComparing related parameters in modified and radial flow geometryFigure 31View largeDownload slideComparing related parameters in modified and radial flow geometry Close modal
+
+
+Table 9Comparing Related Parameters for Radial and Modified   View Large
+
+
+Comprehensive Analytical Results for Linear, Radial and Modified
+
+
+From Figure 32, it was observed that acid volume for linear flow geometry has a high standard error of mean deviation as high as 1.30206E+14 and the predicted acid volume assuming a modified flow geometry has a lower error of 4000.9 which was overlapping with the radial flow assumption indicating a high degree of similarity.Standard deviation from the mean value of the acid volume assuming linear flow geometry gave a high value of 3.65279E+14, a lower value of 12650.7 for the predicted acid volume and a lower level of 2282.9.The variance degree of acid volume for linear flow was indicated to be 1.36E+12; Radial and modified had a lower value of 5211792 and 160039156 respectively. The results, therefore, prove the degree of confidence and accuracy of the radial and modified flow acid volume.
+
+
+Figure 32View largeDownload slideComprehensive analytical results for linear, radial, and modified generated from SPSS.Figure 32View largeDownload slideComprehensive analytical results for linear, radial, and modified generated from SPSS. Close modal
+
+
+Conclusion
+
+
+In conclusion, the modified model for acid volume estimation for acid stimulation jobs should be regarded as a sophisticated acid volume estimation tool as it will be able to account for combined flow geometries at once. The study clearly states that more than 35% of acid stimulation jobs executed every year fail due to limited knowledge of downhole acid placement and flow geometry. The oil and gas industry today estimates acid volume for stimulation jobs considering majorly two types of flow geometries: the linear and radial flow geometries. Study shows that this can lead to erroneous acid volume estimation and unsuccessful acid placement operation due to the heterogeneity of the reservoir. A mathematical model is generated assuming Linear-Radial flow geometry (Modified flow geometry) and it accounts for reservoir flow geometry deviation along a linear to radial profile.
+
+
+Deriving equations for modified flow geometry showed more related parameters with the linear flow geometry compared to that of the radial flow geometry, but the degree of coincidence was very low. However, the modified flow geometry acid volume was coincidental with the radial flow geometry acid volume; this proves the degree of accuracy of the modified flow geometry.
+
+
+The study can therefore conclude that modified flow geometry can be assumed for acid stimulation acid volume estimation for linear flow geometry since they have lots of similar parameters and for radial flow geometry since both acid volume results are coincidental.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Nomenclature
+
+
+   View Large
+
+
+References
+
+
+Abdelmoneim, S.S. and Nasr-El-Din, S. (2015). Determining the optimum HFconcentration for stimulation of high-temperature sandstone formations. Society of Petroleum Engineers. SPE-174203-MS.Google Scholar Al-Harthy, S. (2008). Options for high-temperature well stimulation. Oil Field Review, volume 20, pp 52–62.Google Scholar Amanat, U. C. (2004). Pressure build-up analysis techniques for oil wells. In Oil well testing handbook. Gulf Professional Publishing. 10.1016/B978-0-7506-7706-6.X5089-9.Google Scholar Buyon, G., C., Williams, L. and Ali, G. (2007). Matrix Acidizing in Petroleum Production Engineering, A Computer-Assisted Approach. 1st Edition. Gulf Professional Publishing.Google Scholar Crowe, C., Masmonteil, J., Touboul, E. and Thomas, R. (1992). Trends in matrix acidizing. Oil Field Review4(4):24–40.Google Scholar Ilozobhie, A.J. and Egu, D. I. (2020). Dynamic Reservoir Sand Characterization of an Oil Field in the Niger Delta from Seismic and Well Log Data. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-021-06542-4.Google Scholar Gomaa, A. M., Stolyarov, S. and Cutler, J. (2015). Retarded HF system to deeply stimulate sandstone formation and eliminate the need for pre-flush and post-flush acid stages: experimental and field cases. Paper presented at International Petroleum Technology Conference, Doha, Qatar, 7-9 December.Google Scholar Gomez, J. N. (2006). Design, set up and testing of a matrix acidizing apparatus. Texas A & M University, Texas, United State of America.Google Scholar Halliburton (2000). Hydraulic fracturing best practice series. Halliburton, Houston, U.S.A.Hussein, H. K. (2016). Fundamentals of Engineering Geology. 1st Edition. University of Technology Printing Press Department, University of Technology. Baghdad, Iraq.Google Scholar Mohammed, S., Cairns, A. J. and Qasim, S. (2020). Low Viscosity Acid Platform: Benchmark Study Reveals Superior Reaction Kinetics at Reservoir Conditions. Paper Presented at International Petroleum Technology Conference, Dhahran, Kingdom of Saudi Arabia, January 2020.Google Scholar EguD. I. and Ilozobhie, A. J. (2021). Adumbrative heterodox dictum of wellbore aggregates from sapient recovery factor penchants for honed field praxis. SPE-208223-MS paper virtually presented at the SPE Nigeria Annual International Conference and Exhibition, 2-5 August, Nigeria. https://doi.org/10.2118/208223-MSGoogle Scholar Egu, D.I. (2014). Effective Field Development Management of a gas field in the Niger Delta. SPE-172425- MS paper presented at the 2014 NAICE conference, 5-7 August, Lagos, Nigeria. https://doi.org/10.2118/172425-MS.Google Scholar Frenier, W. and Hill, D. G. (2002). Effect of Acidizing Additives on Formation Permeability During Matrix Treatments. Paper Presented at International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, February2002.Google Scholar Mukul, M. S. (2007). PetroWiki PEH: Formation damage. Last Modified April 26, 2017. Accessed October 21, 2021. https://petrowiki.org/PEH:Formation_Damage.Google Scholar Schechter, R. S. (1992). Oil well stimulation. Prentice-Hall, Englewood Cliffs.Google Scholar Schlumberger (2000). Reservoir stimulation. Wiley, Chichester.Smith, C. F., and Hendrickson, A. R. (1965). Hydrofluoric acid stimulation of sandstone reservoirs. SPE- 980-PA.USOSTI (1965): U.S. Department of Energy Office of Scientific and Technical Information. Dowell introduces alcoholic mud acid treating techniques. Vol. 58:23 of Calif. Oil World Petroleum Industry, United States. 5959583.
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+
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211916-MS
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+

files/2022/Environmentally Sound Technologies for Sustainability and Climate Change in Niger Delta.txt

@@ -0,0 +1,195 @@
+----- METADATA START -----
+Title: Environmentally Sound Technologies for Sustainability and Climate Change in Niger Delta
+Authors: Humphrey Otombosoba Oruwari
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211933-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+This paper examines the concepts of environmentally sound technologies and sustainability. Environmentally sound technologies are potential ways capable of mitigating environmental pollution by adopting the use of energy efficient technologies. While sustainability is a process of change in which technological development and institutional change in which the exploitation of resource, the direction of investment, the orientation of technological development and institutional change are made consistent with future as well as present needs. In a broad sense sustainable development must enhance the long-term productivity of the resource base with acceptable environmental impacts. Using literature review and case studies of Britania U, a marginal oil field operator, Total Energy, and Shell Petroleum Development Company (SPDC). We find that environmentally sound technology can mitigate climate change. The study revealed that Britania used the technology which cleans out poisonous elements and emits smokeless air into the environment thereby mitigating climate change. Also, Total Energy, as part of its drive towards clean energy and reduce carbon emissions embarked on installation of solar energy while SPDC reported 17% decrease in routine flaring in 2020 due to the Southern Swamp Associated Gas Project which captured gas produced alongside oil in the Niger Delta. We find that environmentally sound technologies include all those technologies that reduce the negative impact of products and services on the natural environment. Furthermore, environmentally sound technologies have brought about increased opportunities for energy transition into cleaner forms of energy. We therefore recommend that developing countries try as much as possible to develop the internal capacities and embrace environmentally sound technologies to mitigate the negative consequences of climate change.
+
+
+
+
+Keywords:
+upstream oil & gas,
+environmental law,
+sustainability,
+air emission,
+africa government,
+sustainable development,
+world bank,
+operation,
+climate change,
+society
+
+
+Subjects: 
+Environment,
+Sustainability/Social Responsibility,
+Air emissions,
+Climate change,
+Sustainable development
+
+
+
+
+Introduction:
+
+
+Sustainability framework
+
+
+Although a desirable goal, there are contending versions and perspective of sustainability or sustainable development, sharp divergence regarding priorities, mechanisms and methodologies for its attainment because of heterogeneous cultural patterns, ideological inclinations and development goals (Ibibia 2002). Sustainable development has been defined in different ways, but the most frequently quoted definition is from our common future, also known as Brundtland report of World Commission on the Environment and Development (1987) which define it as: "Sustainable development is that development that meets the needs of the present without compromising the ability of future generation to meet their own needs."
+
+
+However, the question of meeting the current oil and gas demand without compromising that of future generation brings about the issue of contested exhaustibility of oil. Some economists believe that the world's resource of oil and gas are nearly infinite, and that it is just a matter of money and technology to produce them all. They reason that, in regards to the size of distribution of accumulations the resource is almost infinite, with a few large fields and an ever-increasing number of ever-smaller accumulation down to a thimbleful, trapped somewhere out of sight,
+
+
+Oil of course is exhaustible in the sense that society's stock of the reserve could not be physically replenish-able. The difficulty in the concept of exhaustion is that geologist estimate the "stock of oil in terms of recoverable reserves. But what is recoverable depends on the cost of extraction relative to the price of oil. Every increase in the price of oil or decrease in the cost of extraction increases the world's stock of recoverable oil. In addition, an increase in price will lead to more research, and will usually results in increasing reserves. In physical sense, all minerals are limited because on the crust of the earth the difference between them and oil is that at this time it is cheaper to extract and use oil. When oil becomes more expensive than other mineral fuels, we stop searching for it and use substitute before we get to the last drop of oil that the earth contains. Thus, oil is really, exhaustible in a physical sense but for the purpose of economic analysis, exhaustion occurs once it is cheaper to use other source of energy.
+
+
+That technology has perhaps masked depletion in many fields goes without saying. The advances in three dimensional (3-D) seismic and computer based analytic techniques as well as other improvements in the transportation refining and marketing stages have remarkably altered the character and structure of the international petroleum industry as put by (Naimi 1995). The objective of the study is to examine the concept of environmentally sound technology and sustainability on climate change in Niger Delta.
+
+
+Sustainability in petroleum operation
+
+
+There is clearly a need to develop a better management approach in oil and gas development, which will have to be environmentally acceptable, economically profitable, and socially responsible. These problems might be solved by developing new technologies that guarantee sustainability. Recently, Khan et al. (2005) and Khan and Islam (2005) introduced a new approach by means of which it is possible to develop a truly sustainable technology. Under this approach, the temporal factor is considered the prime indicator in sustainable technology development.
+
+
+Sustainable petroleum operations development requires a sustainable supply of clean and affordable energy resources that do not cause negative environmental, economic, and social consequences (Dincer and Rosen 2004, 2005). In addition, it should consider a holistic approach where the whole system will be considered instead of just one sector at a time (Mehedi et al. 2007a, 2007b).
+
+
+Recently, Khan and Islam (2005, 2006) developed an innovative criterion for achieving true sustainability in technological development. This criterion can be applied effectively to offshore technological development. New technology should have the potential to be efficient and functional far into the future in order to ensure true sustainability. Sustainable development is seen as having four elements - economic, social, environmental, and technological. Delivery is the overarching concept that drives both implementation and further strategic development as put by (Khan and Islam 2006).
+
+
+Sustainability policy must foster environmental protection and social equity, identify barrier to sustainability and ways to overcoming them. The instrument to implementing sustainability policy may include:
+
+
+Economic (E.g., taxation)Regulatory (Law, certification and standard)Education, communication, information and training.Institutional change (which is a combination of regulatory and economic).
+
+
+Benefits of Sustainable Drilling and Productions
+
+
+The success of high-risk drilling and production operations depends on the use of appropriate technologies. In this section, many emerging technologies have been proposed for drilling and production operations. The main objectives of these proposed technologies are to make development of oil and natural gas cost effective, more efficient, and more protective of the environment. Uses of appropriate technologies will also help find new reserves, improve drilling efficiency, reduce costs, and increase production. The proposed, emerging technologies have had positive environmental benefits in reducing negative impacts on lands, surface waters and aquifers, wildlife, and air quality. Innovations in drilling technology will significantly reduce the environmental impact.
+
+
+This can be achieved, for example, by using smaller drilling pads, smart wells, and measurement while drilling technologies. Better drilling technology can produce more oil and gas from fewer wells. Fewer wells means less land disturbed by drilling operations and the associated surface infrastructure and transportation systems. Use of sustainable technology to produce oil and to meet environmental regulations has developed new, improved techniques and strategies that accomplish both goals. Sustainable drilling and production operations also make good business sense and help protect the environment.
+
+
+Rapid growth of innovative technologies has significantly impacted on the oil and gas development in the last few decades. These trends which have profound implications for the world economy such that, increasingly, major decision around the world on energy issues are driven by sustainability. Industrialization and economic growth are responsible for many industrial environmental dangers in the developing and developing economies of the world. Air pollution caused by emission from fossil fuel combustion is a growing problem. (UNDP, 2000).
+
+
+Environmentally sound technology
+
+
+The availability of energy is critical for economic and industrial development, and so is the emerging consensus on the role of fossil fuels in promoting global warming. However, years of consumption of fossil fuels over the years have led to several of environmental issues. Some of those issues include global warming and air pollution with their attendant health challenges which impact of the quality of life of the world's peoples (Manisalidis, 2020; Martins et al., 2019). In fact, according to the World Bank's Global Gas Flaring Tracker Report, gas flared from the oil and gas industry releases certain pollutants into the atmosphere which include CO2, methane and black carbon, also known as soot (The World Bank, 2021). The pollutants, particularly CO2 are the biggest contributors of climate change which is now biggest risk facing mankind (Anderson, 2016). For instance, the top seven gas flaring countries of Russia, Iraq, Iran, the United States, Algeria, Venezuela and Nigeria are said to account for about 40% of global oil production and about 65% total gas flared into the atmosphere from their oil and gas activities (The World Bank, 2021). Within the last five years, the top seven countries have flared close 500 billion cubic meters of gas into the atmosphere. See Table 1 for volume of gas flared by the big seven for the last five years
+
+
+Table 1Volume of gas flared by the top seven gas flaring countries for 2016-2020 (billion cubic meters) Country
+            . 2016
+            . 2017
+            . 2018
+            . 2019
+            . 2020
+            . Total
+            . Russia 22.37 19.92 21.28 23.21 24.88 111.66 Iraq 17.73 17.84 17.82 17.91 17.37 88.67 Iran 16.41 17.67 17.28 13.78 13.26 78.4 United States 8.86 9.48 14.07 17.29 11.81 61.51 Algeria 9.10 8.80 9.01 9.34 9.32 45.57 Venezuela 9.35 7.00 8.22 9.54 8.59 42.7 Nigeria 7.31 7.65 7.44 7.83 7.20 37.43 Grand Total      465.94 Country
+            . 2016
+            . 2017
+            . 2018
+            . 2019
+            . 2020
+            . Total
+            . Russia 22.37 19.92 21.28 23.21 24.88 111.66 Iraq 17.73 17.84 17.82 17.91 17.37 88.67 Iran 16.41 17.67 17.28 13.78 13.26 78.4 United States 8.86 9.48 14.07 17.29 11.81 61.51 Algeria 9.10 8.80 9.01 9.34 9.32 45.57 Venezuela 9.35 7.00 8.22 9.54 8.59 42.7 Nigeria 7.31 7.65 7.44 7.83 7.20 37.43 Grand Total      465.94 Source: Extracted fromThe World Bank, 2021.View Large
+
+
+In order to evolve a sustainable pattern, reduce the impact of fossil fuels on the planet ensure long-term development, there is now a major shift towards renewables and improve energy efficiency in all sectors (Martins, 2019; UNDP, 2000). Conversely, achieving sustainable energy systems is herculean task despite the efforts made by several stakeholders like governments, international agencies, etc. (Martins, 2019).
+
+
+Again, the rapid globalisation and rationalisation of the energy industries generally and the petroleum industry in particular, coupled with the giant strides in environmentalism has made in the last two decades have highlighted the importance of the transfer of environmentally sound technology, cooperation, and capacity building. It is suggested that environmentally sound technologies (ESTs) that will lower the rate of air pollution be developed and applied in all industries (Manisalidis et al., 2020). ESTs can reduce environmental pollution by adopting of efficient technologies (Kumar et al., 2020). While there is no universally acceptable definition of environmental sound technology (Robinson, 1992). Beside the environmentally soundness of technologies is a relative rather than an absolute term, as it is dependent on technical and economic conditions as well as the level of environmental standard.
+
+
+It was nevertheless agreed at UNCED that Environmentally sound technologies protect the environment, are less polluting, use resources in a more sustainable manner, recycle more of their waste and product, and handle residual waste in more acceptable manner than the technologies for which they were substitutes (Agenda 21) such technology can be referred to as clean technologies and include air pollution cleaning equipment, others are renewable technologies such as such as solar panels and wind turbines increasing the uptake of those technologies can result in several benefits for the environment (UNEP).
+
+
+In the context of pollution, environmentally sound technologies are processes and "product technologies" which generate low or no waste, in order to prevent pollution. Also included in the concept are the "end of pipe" technologies for treating pollution after its generation. Environmentally sound technologies are more than just individual technologies, but systems which include know-how, procedure, goods and services and equipment including organisational and managerial procedure for
+
+
+The need for favourable access to and transfer of environmentally sound technologies to Nigeria cannot be overemphasized. This is partly because the availability of scientific and technological information and access and transfer of environmentally sound technology are essential requirements for sustainable development.
+
+
+Also, Ibibia (2002) submitted that the increasing spate oil and gas exploration and development in Nigeria have brought considerable strain on the Niger Delta environment in Nigeria and highlighted the urgency for diffusion of clean technologies. Not being unmindful of the growing effect of internation environmental law and the changing standards of environmental performance in the home countries of oil and gas multinational enterprise, Nigerian Government have introduced environmental regulatory pressures to bear on oil and gas industry operation.
+
+
+Furthermore, Ibibia (2002) posited that one possible way by which industry can avoid violating these new standards and yet remain competitive is to take the initiative in introducing clean technologies in all aspects of their operations rather than assume that because of their technological backwardness, Nigeria can serve as a dumping ground for obsolete technologies. But more importantly, government have to chart the way forward.
+
+
+In Nigeria, the awareness by country of the damage of pollution arising from project where oil companies are located has increased their cost to the extent communities are demanding for compensation. According to Falobi (2009) natural gas in Nigeria has not attained its potential as a major source of fiscal revenue in the domestic economy because of inadequate funding for infrastructure development, inept pricing of natural gas for domestic gas policy and regulatory framework and environmental degradation due to gas flaring
+
+
+The combination of these factors sub-optimizes Nigeria competitive position in a rapidly evolving and intensely competitive global gas business. However, the gas infrastructure blueprint aims to address these barriers and leverage a diversified industry player base to actualize this. According to This day report of 30th October (2013): Clear gas development policies can help boost the economy, power generation and improve the standard of living for Nigerians. The report stated further that the federal government of Nigeria should harness the potentials of the nation's enormous gas reserves by establishing a win-win situation that would serve as investments in domestic gas projects. The report also sought the creation of positive incentives to encourage local and international investments in the entire gas value chain, while promoting a willing buyer-willing seller market-driven pricing regime.
+
+
+Methodology.
+
+
+The methodology involves collection of secondary data from literature review and the case study of international and indigenous oil and gas operators in Niger Delta region of Nigeria.
+
+
+Result and discussions
+
+
+According to Omar (2010), the exploration and production of oil and gas has as it main purpose to "Extract (in a cost effective, efficient, safe and as environmentally friendly as reasonable) the hydrocarbons that rely in basins under the soil surface (either in land, fresh water bodies or in the seas) and transport, process and deliver the production to a market". He stated further that: The value chain of oil and gas encompasses the chain of technological solutions that make possible to bring the hydrocarbon products from the reservoir to the final market. It is usually divided in Up‐stream, Mid‐stream and downstream.
+
+
+Effective management in the short, medium and long term is prerequisite for the attainment of sustainable development. The key to effective management in the upstream sector lies in careful planning and formulation of policies, strategies, mechanism and law that will enhance the capacities of the people to solve multifaceted economic, social and environmental problems in an integrated fashion. Effective management of the environmental impact of project is a major concern in the oil and gas industry.
+
+
+According to Herriot watt Institute of petroleum studies (2005): Pollution and other forms of environmental damage are common by products of most industrial activities and classified by economist as "externalities" implying zero financial implication to business. In the absence of effective penalties the avoidance of pollution increases operating cost and is likely to reduce rather than to enhance profitability. Therefore there is no direct, economics incentives for profit maximizing organisation to think about pollution. It is consequently necessary for government to build a framework of legislation for environment protection
+
+
+Ecological analysis is necessary especially with the recent environmental degradation caused by pollution related industry. It is concerned with the analysis of possible damage to a manageable level. No industry that has high environmental and climate change concern around the world than oil and gas. For policy maker it should be noted that environmental considerations and public opposition to oil and gas projects affect opportunities for investment and the cost of projects. Technology is both an input and output of business organisation as well as being an environmental influence on them.
+
+
+Environmentally sound technology encompasses evolving group of method and materials for production of essential nontoxic product by oil and gas operators. Oil and gas development are very important drivers for job creation and also a pillar of economic growth in many oil and gas producing country. They play prominent role in the development of developed countries in terms of creating employment opportunities. The oil and gas operators generate waste and pollution from their practices and business because of their informal nature, lack of regulation and supervision. The pollution produces by the oil and gas have contributed immensely to the global warming and natural resources depletion leading to many economic and social problems
+
+
+Case study of application of environmentally sound technologies by energy companies.
+
+
+According to the investor village report (2011): The company Britania – U a marginal field operator currently produces 2.2mmscfd of gas from its Ajapa field out of which 1.8mmscfd is reserved to power the production system on board the Floating Production, Storage and Offloading owned by the company, while the balance of 400sctd is small to be used for anything, rather the company fixed sonic flare tip, which is the latest technology ever to be used in the country, which cleans out the poisonous element and emits smokeless air into the environment.Also, total energy Nigeria limited, as part of its drive towards clean energy and reduce carbon emissions, the company had embarked on the installation of solar energy at its offices, retail outlets, and project sites adding to the deployment of modern technologies in its operations. TotalEnergies, through its Joint Venture with the Nigerian National Petroleum Corporation (NNPC), earned $1.4 million through the sales of Carbon Credit on the United Kingdom market in 2020.The Southern Swamp Associated Gas Solutions project captures gas produced alongside oil in the Niger Delta to help reduce flaring. The Shell Petroleum Development Company of Nigeria Ltd (SPDC) Joint Venture reported a 17% decrease in routine flaring in 2020. Further associated gas flaring reductions by SPDC are anticipated with the completion of commissioning of the Forcados Yokri gas-gathering project in 2021. This is in line with Vijay (2012), submission that: "By reducing gas flaring, oil producing countries and companies are improving energy efficiencies and mitigating climate change. Instead of wasting this valuable resource, we now need to develop gas market and infrastructure so the associated gas can be utilized to generate electricity and cleaner cooking fuels."
+
+
+Conclusion and recommendations
+
+
+The study set out the one of the strategies and mechanisms for implementing the techniques for managing the environmental aspect of the energy industry. It was revealed that technology may involve, but is by no means restricted to equipment, patients processes and copy rights. It is rather o host of intricate interconnected factors that traverse equipment, patients, processes and copyrights and most importantly knowledge of how to invent, manipulate and use the above-mentioned factors towards the attainment of definite goals.in recent time emphasis has shifted from the transfer of technology perse to environmentally sound technology for climate change mitigation.
+
+
+Recommendations
+
+
+Research and development efforts should be geared towards innovation, dissemination and management of environmentally sound technology.Education and training program should be tailored to meet the need for environmentally sound technologies with interdisciplinary outlook.There should be building of capabilities for craft persons, technicians and middle level managers, scientist, engineers and educators as well as developing their corresponding social and managerial support systems.There should be collaborative efforts of governments, industries and individuals for the implementations of environmentally sound technologies.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Anderson, T.R., Hawkins, E. & Jones, P.D. (2016). CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Calendar to today's Earth System Models. Endeavour, 40(3), 178–187.Google ScholarCrossrefSearch ADS PubMed Falobi, E.O.2009: Economic evaluation of natural gas utilization: Nigeria case study University of Lagos. Federal ministry of finance: Available from:www.oagfnig.com (accessed on 12th December 2010).Google Scholar Ibibia, L. W (2002): Environmental Law and policy of Petroleum Development. Strategies and Mechanisms for Sustainable Management in Africa. Pp 265–267Published by Anpez Center for Environment and Development Port Harcourt.Google Scholar Investor Village report (2011): Nigerian marginal fields: navigating through the financing storms: Available at:www.investorvillage.com/groups.asp (accessed on 30th May 2012)Khan, M.I. and Islam, M.R. (2006) Ecosystem-Based Approaches to Offshore Oil a d Gas Operation: An Alternative Environmental Management Technique. SPE Annual Technical Conference and Exhibition, Denver, USA. October 6-8.Google Scholar KumarR., NandaA.H.G., SharmaP. (2020) Environmentally Sound Technologies for Sustainability and Climate Change. In: LealW.Filho, AzulA.M., BrandliL., Lange SalviaA., WallT. (eds) Partnerships for the Goals. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. DOI:10.1007/978-3-319-71067-9_27-1Google Scholar Mehedi, M., Khan, M.I., Ketata, C., and Islam, M.R. (2007b) A risk management model for the valued ecosystem components in offshore operations, Int. J. Risk Assessment and Management, in press.Google Scholar Naimi, H. E (1995): Oil, Environment and Technology" OPEC Bulletin, October1995, Page 4.Google Scholar OmarR. M (2010): Model for economical analysis of oil and gas deepwater production concepts / Comparisons of Life Cycle Cost of Subsea Production Systems vs. Floating Structures with dry wellheadsa. A master thesis: university of Stavanger 2010.Google Scholar Robinson, N. A (1992): Agenda 21, the united nation procceding vol IV (New: York : Ocean publications, Inc, 1993, p.554Google Scholar The World Bank (2021). The Global Gas Flaring Tracker Report. The World Bank.Manisalidis, I., Stavropoulou, E., Stavropoulos, A. & Bezirtzoglou, E. (2020). Environmental and health impacts of air pollution: A review. Frontiers in Public Health, https://doi.org/10.3389/fpubh.2020.00014.Google Scholar Martins, F., Felgueiras, C., Smitkova, M. & Caetano, N. (2019). Analysis of fossil fuel energy consumption and environmental impacts in European countries. Energies, 12(964), 1–11. DOI:10.3390/en12060964.Google Scholar Mehedi, M.Y., Chhetri, A.B., Ketata, C., and Islam, M.R. (2007a): An Approach for Conflict Resolution in Oil and Gas Operations, Int. J. Risk Assessment and Management, in press.Google Scholar This day report (30th, October2013): Nigeria stunted output from marginal fields. Available athttp://www.hellenicshippingnews.com/News.aspx?ElementId=f0db2f59-d47b-4720-b218-9f5557d78a64 assessed on 2nd November 2013.UNDP2000: United nation development programme, special unit for technical cooperation among developing country.VijayIyer (2012): World Bank see warning sign of gas flaring increase: Available at:https://www.worldbank.org/en/news/press-release/2012/07/03/world-bank-sees-warning-sign-gas-flaring-increase accessed on 14th march 2021.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211933-MS
+
+
+

files/2022/Evaluating Injectivity Index of Niger Delta Reservoirs for CO2 Geological Sequestration.txt

@@ -0,0 +1,199 @@
+----- METADATA START -----
+Title: Evaluating Injectivity Index of Niger Delta Reservoirs for CO2 Geological Sequestration
+Authors: Ifeoluwa Jayeola, Bukola Olusola
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211986-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Underground storage of carbon dioxide (CO2) has been recognized as a viable strategy to reduce CO2 emissions in the atmosphere. In this context, numerical reservoir simulations are routinely implemented to predict the performance of the project under different operational scenarios and uncertainties. However, numerical simulators are intensive in terms of cost, computational time, and data requirement, thus limiting its use for early commercial applications especially for feasibility studies or quick evaluations. This paper presents the application of a simplified modelling approach to predict dimensionless pressure build-up and injectivity index based on an analytical model for reservoirs in Niger Delta. Data from four (4) Niger Delta reservoirs such as relative permeabilities, reservoir pressure, brine viscosity, and injection rate, among others were used in this work. A modified version of the physics-based model of Mishra et. al was used for conducting the studies. Therefore, equations governing the dimensionless pressure build-up and injectivity index were used to investigate the reservoir and operational characteristics of the well injection of CO2 in Niger delta reservoirs as an alternative to full-field numerical simulation. The model approximates the CO2 injection rate for a given target pressure differential or alternatively, the pressure differential that would result from injecting CO2 at a target rate, given the initial permeability, porosity, permeability, and injection rate. The results were used to rank the reservoirs based on suitability to CO2 sequestration, the displacement efficiency of the CO2 and potential storage in the reservoir. This approach is the first one carried out in the Niger Delta and provides the chance to assess the performance of CO2 storage capacity as a strategy to combat global warming from Nigeria.
+
+
+
+
+Keywords:
+fluid dynamics,
+subsurface storage,
+waterflooding,
+air emission,
+reservoir simulation,
+modeling & simulation,
+niger delta,
+sequestration,
+society,
+journal
+
+
+Subjects: 
+Reservoir Fluid Dynamics,
+Improved and Enhanced Recovery,
+Reservoir Simulation,
+Storage Reservoir Engineering,
+Environment,
+Flow in porous media,
+Waterflooding,
+Chemical flooding methods,
+CO2 capture and sequestration,
+Air emissions
+
+
+
+
+Introduction
+
+
+The increasing emissions of carbon dioxide are considered to play a significant role in global warming; therefore, CO2 sequestration has been considered one of the most effective strategies for reducing the effect of climate change. Poor industrial practise like gas flaring has caused Nigeria to be identified as number 43 in the world ranking of carbon emissions from all sources, with estimates suggesting that of the 3.5 billion cubic feet (100,000,000m3) of associated gas (AG) produced annually from Nigeria’s Niger Delta oil-rich region, 2.5 billion cubic feet (70,000,000m3) or about 70% is wasted flaring (Anosike, 2010). With the application of Carbon Capture and storage in Nigeria, there exists the potential of attaining large scale emissions reduction of greenhouse gases and thus, contributing to creating a safe, clean environment (Isehunwa et al., 2006).
+
+
+Over the last decade, a limited number of research has gone into the development and demonstration of geologic sequestration technologies to mitigate greenhouse gas emissions in Nigeria (Yahaya-Shiru et al., 2021). In line with this, numerous geological sinks and sedimentary basins have been identified in the coastal region of the country (Yelebe and Samuel, 2015), and could ultimately lead to the utilization of carbon capture technologies in Nigeria.
+
+
+To achieve the goal of sequestration of CO2 in subsurface reservoirs however, it is imperative that reservoir-seal pair, injectivity, containment and storage capacity of Niger Delta’s sandstone reservoirs provide favourable conditions for the efficacy of geologic carbon storage technology. Developing a suitable strategy to operate CO2 storage sites requires a comprehensive understanding of the storage site behaviour in various operational scenarios (Shokouhi et al., 2021). Multiple technologies related to the evaluation of capacity and injectivity, monitoring of CO2 plume movement, and risk assessment are needed to ensure that the site can meet required storage performance safety criteria.
+
+
+Reliable prediction of the performance of CO2 storage sites is crucial both in optimizing operations and enabling the operators to explore and test the storage sites behaviour during and post injection. However, large-scale high-fidelity reservoir simulations can be a time-consuming and especially expensive process. Several simplified analytical and semi-analytical modelling tools have been developed as alternatives to numerical simulation methods. These newer approaches can also serve to obtain accurate prediction of the CO2 plume migration and pressure build-up over large areas and over long periods of time.
+
+
+The primary motivation for this study is to assess the potential of utilizing one of such simplified modelling approaches that will: a) enable rapid feasibility and risk assessment of CO2 sequestration projects in Niger Delta formations. b) enable integrated system risk assessments to be carried out with robust, yet simple to implement, reservoir performance models, c) allow modellers to efficiently analyse the impact of variable CO2 injection rates on plume migration and trapping for optimal well placement and rate allocation.
+
+
+Background
+
+
+The search for an effective system of carbon storage and capture in Nigeria has been ongoing as a part of Nigeria’s deep carbonization targets by 2050 but has been limitedly researched and practised in the country. Ubani et al. analysed the properties of six wells in a Niger Delta field codenamed, UTI field to ascertain its potential for CO2 sequestration. The wells were accessed, digitized, and analysed based on criteria such as storage capacity, minimum miscibility pressure, reservoir temperature, and reservoir pressure. The reservoir characterization showed that the computed porosity values of the reservoirs range from 0.17 to 0.23 while entire reservoir is observed to have exceedingly good flow capability with permeability values ranging from 782.27mD to 1437.75mD, making it a desirable field for CO2 sequestration. Another work by Odesa et al. focused on the application of carbon capture for enhanced oil recovery in an offshore field in Niger Delta, Okota/Okpoputa field. The carbon-capture based technology achieved an increased level of production while simultaneously reducing CO2 impact on the environment. Identified risk resulting from the consequences of unintended leakage of CO2 from the storage formation (Odesa & Adewale, 2011). In another study to explore the potential of some reservoirs for CO2 sequestration, Yahaya-Shiru et al. worked on characterizing some sandstone reservoirs in Niger Delta via systematic and process-based incorporation of seismic and well logs datasets. Petrophysical analysis, fault modelling as well as geostatistical techniques were used to build facies and property models which enabled a qualitative assessment of the sealing potential of faults associated with the reservoirs based on prediction of key properties such as shale gouge ratio, lithological juxtaposition, fault permeability and fault transmissibility across the fault faces. Nine-water bearing sandstone reservoirs with varying reservoir quality were identified in the field. (Mariam Yahaya-Shiru, Ogbonnaya Igwe, Seyi Obafemi, 2021).
+
+
+Furthermore, Celinah et al., worked on assessing the potential of a field in Niger Delta for CO2 storage and sequestration by evaluating the injectivity, containment and storage capacity of sandstone reservoirs in a field in the Coastal Swamp depobelt of the onshore eastern Niger Delta using wireline logs and seismic data. Comparison of the derived reservoir and seal properties such as porosity, permeability, thickness, and depth with the minimum recommended site selection criteria shows that the reservoirs are potential candidates for CO2 sequestration. (Ojo & Tse, 2016). Bappah Adamu et al also worked on assessing regional and field potentials for geological storage of the Niger Delta Basins. Davies et al worked on analysing data to estimate reservoir capacity and injectivity of reservoirs within the study area with the potential to hold sequestered CO2 (Bappah Adamu Umar, Raoof Gholami, Prasanta Nayak, Afroz A. Shah, Haruna Adamu, 2020). Several research works have also been done addressing the benefits, scopes, challenges, and risk of carbon capture technology in Nigeria (Galadima & Garba, 2008; Isehunwa, Makinde, & Olagimoke, 2006; Yelebe & Samuel, 2015; Adeyanju, Osobajo, Otitoju, & Ajide, 2020).
+
+
+Based on this literature review, while no actual execution of CO2 storage and sequestration in Niger Delta Reservoirs have been recorded, we conclude that it is essential to explore the application of already developed analytical models for prediction of CO2 storage site response.
+
+
+Model Description
+
+
+The study builds on the approach developed by Mishra et al., 2015. The analytical model was developed by running simulations of CO2 injection into a semi-confined cylindrical saline aquifer system for a broad range of reservoir and cap rock properties (Mishra, et al., 2015). Data from this sensitivity analysis exercise were used to develop insights into the relationship between the performance metrics of interest and fundamental reservoir/cap rock properties. The resulting predictive relationships were tested to validate the predictive models using "blind" runs with simulations that were not part of the "training set". The resulting model was defined as:
+
+
+P=10.3+0.59dfgdSg+3.41Vdp+1.23dfgdsg−0.342(dfgdsg)2−8.89(Vdp)2
+
+
+Where Pd is the magnitude of dimensionless pressure jump, dfgdSg is the slope of fractional flow, Vdp is Dykstra-Parson’s coefficient. The resulting model is then used to calculate the injectivity index (q/ΔPjump).
+
+
+Injectivityrate=2×π×k×hpd×v
+
+
+ Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 460 Hr Reservoir Thickness m 55 Vdp Dykstra-Parsons coefficient - 0.55 dfgdSg  - 2 V Viscosity  10 Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 460 Hr Reservoir Thickness m 55 Vdp Dykstra-Parsons coefficient - 0.55 dfgdSg  - 2 V Viscosity  10 View Large
+
+
+The relative permeability curves are developed from the available dataset as shown below (fig 1). In order to develop an fw (water cut) versus Sw (prevailing water saturation) relationship from historical production data, the fractional flow curve is used to describe the immiscible fluid displacement process in Reservoir A. The curve becomes the basis for the characterization of the relative permeability models using the slopes of the tangents to their curves i.e dydx
+
+
+Figure 1View largeDownload slide(a)Fractional flow curve (b)Relative Permeability CurveFigure 1View largeDownload slide(a)Fractional flow curve (b)Relative Permeability Curve Close modal
+
+
+We find that the dimensionless pressure predicted by our model for the first case is 10.5 bbl/day/psi, and the injectivity rate predicted is 13.0279
+
+
+ Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 1860 Hr Reservoir Thickness m 34 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 1860 Hr Reservoir Thickness m 34 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 View Large
+
+
+We find that the dimensionless pressure predicted by our model for the first case is 10.5 bbl/day/psi, and the injectivity rate predicted is 32.564
+
+
+ Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 1400 Hr Reservoir Thickness m 45 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 1400 Hr Reservoir Thickness m 45 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 View Large
+
+
+We find that the dimensionless pressure predicted by our model for the first case is 10.5 bbl/day/psi, and the injectivity rate predicted is 32.44
+
+
+ Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 900 Hr Reservoir Thickness m 60 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 900 Hr Reservoir Thickness m 60 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 View Large
+
+
+We find that the dimensionless pressure predicted by our model for the first case is 10.5 bbl/day/psi, and the injectivity rate predicted is 27.806
+
+
+ Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 1900 Hr Reservoir Thickness m 54 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 Parameter
+            . Description
+            . Unit
+            . Reference value
+            . Kr Reservoir Permeability mD 1900 Hr Reservoir Thickness m 54 Vdp Dykstra-Parsons coefficient - 0.55   - 2 V Viscosity  10 View Large
+
+
+We find that the dimensionless pressure predicted by our model for the first case is 10.5 bbl/day/psi, and the injectivity rate predicted is 52.832
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+View largeDownload slideView largeDownload slide Close modal
+
+
+Conclusion
+
+
+Analytical injectivity simulation models for Niger Delta reservoirs have been presented. This paper summarises the findings of a research project whose objective was to validate the application of a simplified modelling approaches for CO2 sequestration in Niger Delta formations based on simplified physics approximations for predicting: (a) injection well and formation pressure build-up, and (b) lateral and vertical CO2 plume migration. Such computationally efficient alternatives to conventional numerical simulators can be valuable assets during preliminary CO2 injection project screening, serve as a key element of probabilistic system assessment modelling tools, and assist regulators in quickly evaluating geological storage projects.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+Adeyanju, C. G., Osobajo, O. A., Otitoju, A., & Ajide, O. E. (2020). Exploring the potentials, barriers and options for support in the Nigeria renewable energy industry. Discover sustainbility[online].Google ScholarCrossrefSearch ADS  Anosike, C. R. (2010). Unhealthy Effects of Gas Flaring and Wayforward to Actualize the Stopping of Gas Flaring in Nigeria. Society of Petroleum Engineers.Google ScholarCrossrefSearch ADS  Bappah AdamuUmar, RaoofGholami, PrasantaNayak, Afroz A.Shah, HarunaAdamu. (2020). Regional and Field sessments of Potentials for Geological Storage of CO2: A Case Study of the Niger Delta Basin, Nigeria. Journal of Natural Gas Science and Engineering.Google Scholar Galadima, A., & Garba, Z. N. (2008). Carbon Capture and Storage(CCS) in Nigeria: Fundamental Science and Potential Implementation Risks. Science World Journal.Google Scholar Isehunwa, O. S., Makinde, A. A., & Olagimoke, O. (2006). Carbon(IV) oxide Capture and Sequestration in Nigeria: Prospects and Challenges. Society of Petroleum Engineers.Google ScholarCrossrefSearch ADS  MariamYahaya-Shiru, OgbonnayaIgwe, SeyiObafemi. (2021). 3D Structural and Stratigraphic Characterization of X Field Niger Delta: Implication for Co2 Sequestration. Jornal of Petorluem Exploration and Production Technology.Google Scholar Shokouhi, P., Kumar, V., Prathipati, S., Hosseini, S., & Giles, C. L. (2021). Physics-informed deep learning for prediction of CO2 storage site response. Journal of Contaminant Hydrology.Google Scholar Ubani, C. E., & Ikpaisong, U. S. (2019). Sequestration of CO2 in Depleted Reservoirs: A Case Study of a Niger Delta Field. International Research Journal of Advanced Engineering and Science.Google Scholar Yelebe, Z. R., & Samuel, R. (2015). Benefits and Challenges of Implementing Carbon Capture and. The International Journal Of Engineering And Science (IJES).Google Scholar Mishra, S., Ganesh, P. R., Schuetter, J., He, J., Jin, Z., & Durlofsky, L. (2015). Developing and Validating Simplified Predictive Models for CO2 Geologic Sequestration. Society of Petroleum Engineers.Google ScholarCrossrefSearch ADS  Odesa, D., & Adewale, D. (2011). Carbon Capture for Enhanced Oil Recovery in Niger Delta: A Case Study of Okota/Okpoputa Field in Offshore Niger Delta. Society of Petroleum Engineers.Google ScholarCrossrefSearch ADS  Ojo, A. C., & Tse, A. C. (2016). Geological Characterisation of Depleted Oil and Gas Reservoirs for Carbon Sequestration Potentials in a Field in the Niger Delta, Nigeria. Journal of Applied Sciences and Environmental Management.Google Scholar 
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211986-MS
+
+
+

files/2022/Experimental Investigation on Effect of Enzyme and Nanoparticles on Oil-Brine Interfacial Tension.txt

@@ -0,0 +1,167 @@
+----- METADATA START -----
+Title: Experimental Investigation on Effect of Enzyme and Nanoparticles on Oil-Brine Interfacial Tension
+Authors: Tinuola Hannah, Oyinkepreye David
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211913-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Interfacial tension (IFT) is an interfacial phenomenon that commonly exist between immiscible liquids such as oil and brine that are found in the hydrocarbon reservoirs. High IFT in combination with high capillary forces plays a fundamental role in residual oil saturation in the reservoir rock pores. The effects of enzyme and silica nanoparticles on crude oil-water and crude oil-brine interactions were investigated and presented in this study. The potential of individual application of enzyme and silica nanoparticles as well as the combination of both were explored under different salinity conditions. The results of this study showed that the application of silica nanoparticles did not significantly reduce oil-brine IFT under different salinity conditions investigated in this study, although the highest reduction was obtained with low salinity brine. The use of enzyme however significantly reduced oil-brine IFT under varied salinity conditions and better IFT reduction was obtained in brines relative to aqueous solution. Finally, the combination of enzyme with nanoparticles effected better IFT reduction than the application of either of them individually in aqueous solution and it also significantly reduced oil-brine IFT in all salinity conditions. This study is a novel investigation on the potential of enzyme-nanoparticles to modify oil-brine IFT and the result of this study is significant to the design and application of enzyme and nanoparticles enhanced oil recovery processes.
+
+
+
+
+Keywords:
+nanoparticle,
+enhanced recovery,
+structural geology,
+drilling fluid management & disposal,
+application,
+enzyme,
+experimental investigation,
+upstream oil & gas,
+residual oil saturation,
+crude oil-brine interaction
+
+
+Subjects: 
+Drilling Fluids and Materials,
+Reservoir Characterization,
+Improved and Enhanced Recovery,
+Drilling fluid management & disposal,
+Exploration, development, structural geology,
+Reduction of residual oil saturation
+
+
+
+
+Introduction
+
+
+Interfacial tension (IFT) is an interfacial phenomenon that commonly exist between immiscible liquids such as oil and brine that are found in the hydrocarbon reservoirs. High IFT in combination of with high capillary forces plays a fundamental role in residual oil saturation in the reservoir rock pores [1]. Reduction in capillary forces can however be achieved by decrease in IFT between oil and brine thereby promoting mixing and the release of residual oil [2]. This great potential of IFT reduction is the primary mechanism by which surface active compounds like surfactant and enzyme enhance oil recovery [3, 4, 5]. Other studies [6, 7, 8, 9, 10] have also proposed IFT reduction as one of the mechanisms by which nanoparticles enhance oil recovery. Some of these studies however did not carry out IFT test while some did not use fluids that are relevant to hydrocarbon reservoirs. The major mechanism by which surface active compounds reduce IFT is by their interfacial adsorption due to their amphiphilic nature that resulted from the presence of both hydrophobic and hydrophilic groups in their molecules. Their interfacial adsorption causes reduction in the interfacial energy because of their ability to partition at the interface of two immiscible fluids thereby promoting mixing between them [11, 12].
+
+
+The application of nanoparticles in enhanced oil recovery process has evolved over the years due to their numerous advantages such as high surface area per unit volume, ultra-small size (1-100 nm), enhanced strength, high chemical reactivity, and electrical properties [13, 14]. In the recent past, the use of nanoparticles has been optimized through hybridization. Previous studies have showed that combination of surfactant with nanoparticles improved oil production better than using surfactant alone [15, 16]. The continuous application of surfactants can however be environmentally challenging due to their non-degradable nature [17]. Other studies have showed that the use of biologically based surface-active compound such as enzyme that are environmentally friendly can also enhance oil recovery [4, 5, 18]. Hence, this study aimed at investigating the potential of hybrid nanoparticles-enzyme mixture on oil-brine IFT modification relative to the application of each of them separately. To our best knowledge, this area of research has not yet been explored, but it has the potential to optimise oil recovery if found efficient and compatible with relevant reservoir fluids. In this study, reservoir fluids from one of the reservoirs in the Niger Delta part of Nigeria were used as case study. This study is significant to the design and application of enzyme and nanoparticles enhanced oil recovery processes.
+
+
+Material and method
+
+
+Materials and sample preparation
+
+
+Brine
+
+
+The brine used in this study is synthetic formation brine that was prepared based on the composition of the formation brine of the reservoir that was used as case study. The formation brine has a salinity of 32 g/L, of which 98.2% is sodium chloride (NaCl), 0.6% is calcium chloride (CaCl2), 0.8% is magnesium chloride (MgCl2), 0.2% is potassium chloride (KCl) and 0.2 is sodium sulphate (Na2SO4). The salinity effect was investigated using 100% formation brine (FMB), 50% formation brine (50D) and 10% formation brine (90D). The 50D brine was prepared based on 50% dilution of the FMB with deionised water, while the 90D brine was prepared based on 90% dilution of the FMB with deionised water. The compositional breakdown of these brines is presented in Table 1.
+
+
+Table 1The compositional breakdown of brines. Components
+            . FMB (g/L)
+            . 50D Brine (g/L)
+            . 90D Brine (g/L)
+            . NaCl 31.4240 15.7120 3.1424 CaCl2 0.1920 0.0960 0.0192 MgCl2 0.2560 0.1280 0.0256 KCl 0.0640 0.0320 0.0064 Na2SO4 0.0640 0.0320 0.0064 Components
+            . FMB (g/L)
+            . 50D Brine (g/L)
+            . 90D Brine (g/L)
+            . NaCl 31.4240 15.7120 3.1424 CaCl2 0.1920 0.0960 0.0192 MgCl2 0.2560 0.1280 0.0256 KCl 0.0640 0.0320 0.0064 Na2SO4 0.0640 0.0320 0.0064 View Large
+
+
+Crude oil
+
+
+The crude oil used in this study is a dead crude oil from the oilfield that was used as case study. The properties of the crude oil measured at 25 °C are presented in Table 2.
+
+
+Table 2Properties crude oil. Oil properties
+            . Quantity
+            . Density at 25 °C (g/cc) 0.9067 Viscosity at 25 °C (cp) 15.2206 API at 25 °C (°) 24.5673 Oil properties
+            . Quantity
+            . Density at 25 °C (g/cc) 0.9067 Viscosity at 25 °C (cp) 15.2206 API at 25 °C (°) 24.5673 View Large
+
+
+Nanoparticles
+
+
+Commercial silica nanoparticles (SiO2) were used in this study. The nanoparticles are 99.5% non-porous silica with no surface treatment and particle size of 10-20 nm. The nanoparticles were gotten from Skyspring Nanomaterials Inc. Houston, USA.
+
+
+Enzyme
+
+
+The enzyme used in this study is a commercial 100% concentrate produced from the DNA of oil eating microbes and supplied by Biotech Processing Supply Dallas, Texas.
+
+
+Experimental method
+
+
+The experimental investigations of the IFT modification potential of silica (SiO2) nanoparticles, enzyme, and the combination of both were carried out in four phases. In the first phase, the IFT of crude oil and deionised water and crude oil and brines (FMB. 50D and 90D) was measured without the presence of either nanoparticles or enzyme. This was aimed at investigating how the brines and water naturally interact with crude oil and the results of this phase were used as basis for determining the efficiency of SiO2 nanoparticles and enzyme in their respective solutions. In the second and third phases, the effect of nanoparticles and enzyme on crude oil-water and crude oil-brines interactions were investigated respectively. In the final phase, the effect of combine SiO2 nanoparticles and enzyme on crude oil-water and crude oil-brines interactions was investigated. All the tests were conducted using the Du Nouy ring method with the aid of Sigma 703D tensiometer. A fixed concentration of 1 g/L of nanoparticles and enzyme was used for all the tests and the experiments were conducted at ambient temperature. For the combined effect investigation, a ratio of one-to-one nanoparticles to enzyme was used.
+
+
+Results and discussion
+
+
+Figure 1 shows the results of the first phase in which only the crude oil-water and crude oil-brine interactions were investigated. A relatively closed IFT with no significant difference in their IFT was observed for all the solutions, although the highest IFT was observed with crude oil-water interaction. The combination of crude oil with brines (50D, 50D and FMB) slightly reduced the IFT relative to the use of deionised water with all the brines showing similar IFT reduction within experimental errors. This observed reduction in crude oil-brines IFT relative to that of crude oil-water is related to the interfacial modification potential of salt ions in aqueous solutions. The presence of salts ions in aqueous solutions reduce interfacial energy due to their adsorption at the interfacial [12]. Hence, reduction in IFT was observed when the brines were contacted with crude oil relative to the deionised water that was characterised by higher surface energy.
+
+
+Figure 1View largeDownload slideThe crude oil-water and crude oil-brines interactions.Figure 1View largeDownload slideThe crude oil-water and crude oil-brines interactions. Close modal
+
+
+The results of the effect of SiO2 nanoparticles on the crude oil-water and crude oil-brines interactions investigated in the second phase are presented in Figure 2. The use of nanoparticles dispersed in aqueous solution alone (nano) slightly reduced the crude oil-water IFT relative to the used deionised water. This shows that the presence of nanoparticles in the aqueous solution is characterised by reduction in interfacial energy due to their adsorption at the interface and this invariably reduced the IFT of crude oil-water relative to the use of deionised water. Further reduction in IFT was observed with the dispersion of SiO2 nanoparticles in the brines of different salinities relative to the used of the deionised water and nanoparticles aqueous solution. This is also related to reduction in interfacial energy that resulted from adsorption of the nanoparticles and salt ions at the interfacial thereby, reducing the crude oil-brine IFT. Considering the effect of salinity variation on the IFT modification of nanoparticles in different brines, the lowest IFT was observed with the 90D brine with lowest salinity and similar effect was observed with 50D and FMB within experimental error. These results generally do not show any effectiveness of SiO2 nanoparticles in IFT reduction either in the presence or absence of salt components. This means that the effective application of this nanoparticles in enhanced oil recovery process will not be majorly attributable to IFT reduction. Although previous studies [19, 20, 21] have proposed IFT reduction as one of the mechanisms by which nanoparticles enhance oil production, this result however showed that the application of this nanoparticles in the reservoir with the brine and crude oil investigated in this study may not be associated with any significant IFT reduction.
+
+
+Figure 2View largeDownload slideThe effect of SiO2 nanoparticles on crude oil-water and crude oil-brines interactions.Figure 2View largeDownload slideThe effect of SiO2 nanoparticles on crude oil-water and crude oil-brines interactions. Close modal
+
+
+Figure 3 presents the results of the third phase experiments in which the effect of enzyme on crude oil-water and crude oil-brines interactions was investigated. A significant reduction of IFT from 26.51 to 14.49 mN/m was observed with the application of enzyme in aqueous solution relative to the deionised water. This is due to the good surface activity of enzyme that made it possible to it to absorb at interfaces thereby reducing the surface energy and interfacial tension. Further reduction in IFT was observed with the application of enzyme in the brines relative to its applications in deionised water and enzyme aqueous solution. This further shows the effect of the interfacial adsorption of enzyme and salt ions at the interface thereby resulting IFT reduction. This is consistent with the previous study that shows that enzyme has good surface activity which is enhanced in saline environment [12]. Evaluating the results of the effect of enzyme on crude oil-brines interaction, it is obvious that no significant effect of salinity variation was seen. This shows that increase in ionic concentration in the high salinity brines (50D and FMB) did not translate to increase in their interfacial adsorption. The results of this test show that the effective enhanced oil recovery application of this enzyme in reservoir with the brine and oil investigated in this study may be attributed to IFT reduction.
+
+
+Figure 3View largeDownload slideThe effect of enzyme on crude oil-water and crude oil-brines interactions.Figure 3View largeDownload slideThe effect of enzyme on crude oil-water and crude oil-brines interactions. Close modal
+
+
+The results of the final phase in which the combined effect of nanoparticles and enzyme on crude oil-water and crude oil-brines interactions was investigated are presented in Figure 4. A significant reduction in crude oil-water IFT from 26.51 mN/m and 26.24 mN/m to 6.50 mN/m was observed with the combined application of nanoparticles and enzyme in aqueous solution relative to the application of deionised water and nanoparticles aqueous solution, respectively. Also, a good IFT reduction from 14 mN/m to 6.50 mN/m was archived relative to the application enzyme aqueous solution along. This shows that the combined application of nanoparticles and enzyme is more efficient than the application of the either of them alone. This suggests that the application of the combined nanoparticles and enzyme in enhanced oil recovery process in the reservoir with the fluids investigated in this study will be associated with better recovery than the application of either of them could archive alone. The brine salinity variations however do not seem to influence the performance of combined nanoparticles and enzyme IFT modification as evident by insignificant IFT reduction modification. This further shows the dominate interfacial activity of enzyme relative to salt ions interfacial modification.
+
+
+Figure 4View largeDownload slideThe effect of combined SiO2 nanoparticles-enzyme on crude oil-water and crude oil-brines interactions.Figure 4View largeDownload slideThe effect of combined SiO2 nanoparticles-enzyme on crude oil-water and crude oil-brines interactions. Close modal
+
+
+Figure 5 shows a comparison between the results of the IFT modifications of crude oil-water and crude oil-brines interactions using nanoparticles, enzyme, and combination of both. From the results, two distinct IFT regimes characterised by high and low IFT were observed. The high regime was defined by the base IFT of the crude oil-water and crude oil-brines interactions in the absence of nanoparticles and enzyme. The application of nanoparticles as an IFT modification agent did not significantly influence the IFT of crude oil-water and crude oil-brines interactions and hence, the results lay in the high regime and signifies no impact. The applications of enzyme and combine nanoparticles-enzyme however fall into the low regime characterised by low IFT due to their capacities to reduce IFT of the crude oil-water and crude oil-brines. This signifies a positive impact that can be explored for additional oil recovery.
+
+
+Figure 5View largeDownload slideComparison between the effects of SiO2 nanoparticles, enzyme and SiO2 nanoparticles-enzyme on crude oil-water and crude oil-brines interactions.Figure 5View largeDownload slideComparison between the effects of SiO2 nanoparticles, enzyme and SiO2 nanoparticles-enzyme on crude oil-water and crude oil-brines interactions. Close modal
+
+
+Conclusion
+
+
+In this study, experimental investigations of the IFT modification potential of silica (SiO2) nanoparticles, enzyme, and the combination of both were conducted under similar conditions and the results show that:
+
+
+Crude oil-water and crude oil-brines interactions were characterised by high IFT.The used silica nanoparticles as an IFT modification agent did not significantly influence crude oil-water and crude oil-brines interactions.The application of enzyme as an IFT modification agent significantly influence crude oil-water and crude oil-brines interactions by reducing their respective IFT.Combination of nanoparticles and enzyme effected the best IFT modification of crude oil-water and crude oil-brines interactions that was characterised by the lowest IFT.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgement
+
+
+The authors appreciate Biotech Processing Supply Dallas, Texas for the supply of the enzyme used in this study.
+
+
+References
+
+
+S.Liu, "Alkaline Surfactant Polymer enhanced oil recovery process," Dissertation Abstracts International, vol. 04, no. 69, 2008.Google Scholar B.Towler, H.Lehr, S.Austin, B.Bowthorpe, J.Feldman, S.Forbis, D.Germack and M.Firouzi, "Spontaneous imbibition experiments of enhanced oil recovery with surfactants and complex nano-fluids," J. Surfactants Deterg, vol. 20, pp. 367–377, 2017.Google ScholarCrossrefSearch ADS  A. M.Johannessen and K.Spildo, "Enhanced oil recovery (EOR) by combining surfactant with low salinity injection," Energy & Fuels, vol. 27, no. 10, p. 5738–5749, 2013.Google ScholarCrossrefSearch ADS  T.Udoh, L.Akanji and J.Vinogradov, "Experimental Investigation of Potential of Combined Controlled Salinity and Bio-Surfactant CSBS in Enhanced Oil Recovery EOR Processes," in Paper SPE 193388 in SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers. 10.2118/193388-MS, Lagos, Nigeria, 2018.Google Scholar T.Udoh and J.Vinogradov, "A Synergy between Controlled Salinity Brine and Biosurfactant Flooding for Improved Oil Recovery: An Experimental Investigation Based on Zeta Potential and Interfacial Tension Measurements," International Journal of Geophysics, vol. 2019, pp. 1–15, 2019.Google ScholarCrossrefSearch ADS  A.Karimi, Z.Fakhroueian, A.Bahramian, N.Pour Khiabani, J.Darabad, R.Azin and S.Arya, "Wettability alteration in carbonates using zirconium oxide nanofluids: EOR implications," Energy Fuels, vol. 26, pp. 1028–1036, 2012.Google ScholarCrossrefSearch ADS  M.Sajjad, J.Arezou and J.Soheila, "Temperature effect on performance of nanoparticle/surfactant flooding in enhanced heavy oil recovery," Petroleum Science, vol. 16, pp. 1387–1402, 2019.Google Scholar M.Rosen, "Adsorption of surface-active agents at interfaces: the electrical double layer," Surfactants Interfacial Phenomena, vol. 3, pp. 34–104, 2004.Google Scholar T.Udoh and J.Vinogradov, "Experimental Investigations of Behaviour of Biosurfactants in Brine Solutions Relevant to Hydrocarbon Reservoirs," Colloids Interfaces, vol. 3, no. 24, pp. 1–15, 2019.Google Scholar B.Engeset, "The Potential of Hydrophilic Silica Nanoparticles for EOR Purposes: A Literature Review and an Experimental Study," Department of Petroleum Engineering and Applied Geophysics, Norwegian University of Science and Technology, Trondheim, 2012.Google Scholar T.Udoh, "Improved insight on the application of nanoparticles in enhanced oil recovery process," Scientific African, vol. 13, no. 2021, p. e00873, 2021.Google Scholar N.Ogolo, O.Olafuyi and M.Onyekonwu, "Enhanced oil recovery using nanoparticles," In Proceedings of the SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Al-Khobar, Saudi Arabia, 8-11 April2012.Google Scholar B.Suleimanov, F.Ismalov and E.Veliyev, "Nanofluid for enhanced oil recovery," Journal of Petroleum Science and Engineering, vol. 78, no. 2, pp. 431–437, 2011.Google ScholarCrossrefSearch ADS  A.Roustaei and S.a. M. M.Saffarzadeh, "An evaluation of modified silica nanoparticles’ efficiency in enhancing oil recovery of light and intermediate oil reservoirs," Egyptian Journal of Petroleum, vol. 22, pp. 427–433, 2013.Google ScholarCrossrefSearch ADS  M.Mohajeri, M.Hemmati and A.SadatShekarabi, "An experimental study on using a nanosurfactant in an EOR process of heavy oil in a fractured micro- model," J. Petrol. Sci. Eng., vol. 126, p. 162–173, 2015.Google ScholarCrossrefSearch ADS  M.Zargartalebi, R.Kharrat and N.Barati, "Enhancement of surfactant flooding performance by the use of silica nanoparticles," Fuel, vol. 143, p. 21–27, 2015.Google ScholarCrossrefSearch ADS  J. D. V.Hamme, A.Singh and O. P.Ward, "Physiological aspects. Part 1 in a series of papers devoted to surfactants in microbiology and biotechnology," Biotechnology Advances, vol. 24, no. 6, p. 604–620, 2006.Google ScholarCrossrefSearch ADS PubMed T.Udoh and J.Vinogradov, "Effects of Temperature on Crude-Oil-Rock-Brine Interactions During Controlled Salinity Biosurfactant Flooding," in Paper SPE 198761 presented in SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 2019.Google Scholar L.Hendraningrat, S.Li and O.Torster, "A coreflood investigation of nanofluid enhanced oil recovery," J. Petroleum Sci. Eng., vol. 111, no. 2013, p. 128–138, 2013.Google Scholar A.Roustaei, J.Moghadasi, H.Bagherzadeh and A.Shahrabadi, "changes in interfacial tension and wettability alteration," in in: SPE International Oilfield Nanotechnology Conference and Exhibition, Noordwijk, The Netherlands, 12-14 June, 2012.Google Scholar A.Ragab and A. A.Hannora, "Comparative investigation of nano particle effects for improved oil recovery-experimental work," in in: SPE Kuwait Oil and Gas Show and Conference, Mishref, Kuwait, 2015 11-14 October, 2015.Google Scholar L.Hendraningrat and O.Torsaeter, "Unlocking the potential of metal oxides nanoparticles to enhance the oil recovery," in Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia, 25-28 March, 2014.Google Scholar 
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+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211913-MS
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+

files/2022/Experimental Study on Gas Reservoir Pore Pressure Changes During Natural Gas Recovery and CO2 Storage in Porous Medium.txt

@@ -0,0 +1,197 @@
+----- METADATA START -----
+Title: Experimental Study on Gas Reservoir Pore Pressure Changes During Natural Gas Recovery and CO2 Storage in Porous Medium
+Authors: Nuhu Mohammed, Abubakar Abbas J., Godpower Enyi C.
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/211971-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Much research has been conducted to determine the impact of gas injection settings on residual natural gas recovery and CO2 sequestration. However, little research has been conducted on how reservoir pore pressure varies during natural gas displacement by CO2 flooding. Using a core flooding experiment, this article examined the effects of gas injections on reservoir pore pressure and compression ratio. A core flooding experiment was done at 30-40 °C and 1500 psig to investigate the effect of gas injections on reservoir pore pressure and compression ratio. The CO2 injection rate and N2 booster volume were adjusted to 0.2-1.2 ml/min and 8-36 cm3, respectively. Because of the turbulence effect, high mean interstitial velocity raises the molecular kinetic energy of the gas species, which subsequently influences the molecular agitation of the gas species and so alleviates reservoir pressure and gas compression. The typical CO2 injection experiments revealed substantial compression and pore pressure rises as the injection rate increased. The trial with N2 as a booster resulted in a steady increase, which explains their low dispersion coefficient value. As a result, there is less gas mixing and compression compared with typical CO2 flooding.
+
+
+
+
+Keywords:
+compressors engines and turbines,
+reservoir geomechanics,
+gas injection method,
+reservoir characterization,
+upstream oil & gas,
+enhanced recovery,
+journal,
+compression,
+experiment,
+co 2
+
+
+Subjects: 
+Processing Systems and Design,
+Reservoir Characterization,
+Improved and Enhanced Recovery,
+Compressors, engines and turbines,
+Reservoir geomechanics,
+Gas-injection methods
+
+
+
+
+Introduction
+
+
+As time passes, reservoir pressure tends to fall; as a result, natural gas production from the reservoirs might be hampered, and the reservoirs are ignored. Depleted oil and gas fields are the name given to these reservoirs (Abba et al., 2017). Such oil and gas fields are unrestricted for a variety of reasons, the most prevalent of which being low production output. Another factor might be a large amount of water incursion (Kalra & Wu 2014). However, these depleted reservoirs are not devoid of residual HCs in situ, and there is a need for further production and recovery to meet escalating energy demand. When CH4 is displaced and CO2 is stored, this justifies the use of Enhanced Gas Recovery (EGR) procedures considerably.
+
+
+These isolated gas reservoirs' services might be utilised for anthropogenic CO2 geological storage (Abba et al., 2017). The notion of EGR by CO2 injection takes use of the availability of residual methane in the reservoir while also storing the injected CO2. Furthermore, dispersion refers to the irreversible mixing that happens during fluid displacement via a miscible process (Adepoju et al., 2013). This mixing happens when two miscible fluids collide and their molecules interact under conditions that promote thermodynamic instability (Abba et al., 2018). According to, the two mechanisms that simultaneously play roles in mixing two miscible fluids are molecular diffusion and mechanical dispersion (Perkins & Johnston, 1963). They characterised mixing in porous media as a diffusion-like process that is influenced by velocity and concentration gradients.
+
+
+Because of the significant mixing of the injected CO2 and the nascent displaced natural gas during the flooding process, EGR promotion is still in its infancy (Oldenburg and Benson, 2002; Shtepani, 2006; Turta et al., 2007; Sim et al., 2008; Al-abri et al., 2009; Sim et al., 2009; Sidiq et al., 2011; Hughes et al., 2012; Honari et al., 2013; Khan et al., 2013; Zhang et al., 2014; Honari et al., 2015; Patel et al., 2016; Honari et al., 2016). This contaminates the recoverable natural gas, diminishing its heating and selling cost, resulting in the high cost of the leavening technique to maintain its potency for use (Oldenburg and Benson, 2002; Sim et al., 2009). The EGR project has been constrained to a few experimental tests due to the unprecedented mixing with the displaced gas (Pooladi- Darvish et al., 2008), and the technology has become uneconomical. As a result, the phenomenon is not well understood (Patel et al., 2016). Finding an alternative gas with high displacement capabilities and minimal mixing possibilities might thus be a significant improvement for the oil and gas industry.
+
+
+To date, there is limited experimental evidence on how pore pressure varies during natural gas displacement and CO2 sequestration in sandstone rocks. The influence of gas injections on compression ratio and reservoir pore pressures during EGR and CO2 storage is investigated experimentally in this paper. A laboratory core flooding procedure was used to do this in Sandstone rock. The core sample's petrophysical characteristics were measured and displayed. Thus, the operation parameters for this study are at an average normal reservoir pressure of 0.1 bar/m gradients, a depth of 800-1200m, and a temperature gradient of 300-40 °C/1000m, which is well within the EGR application.
+
+
+Diffusion theory
+
+
+Gas-phase diffusion is often thought to be dominated by molecular diffusion. Eq. 1 depicts the one-dimensional Fick's second law, which represents the unequal expansion of a solute along concentration gradients over time.
+
+
+∂C∂t=Dα∂2C∂x2(1)
+
+
+Where C denotes the gas concentration (mol/m3), t represents the duration (s), Da denotes the binary molecular diffusion coefficient of air (m2/s) and denotes the distance along the flow axis (m). Whenever the centre collision happens inside a molecule-molecule connection without striking with the container's wall, this is referred to as molecular diffusion. More complex gas-phase diffusion techniques, like viscous, Knudsen, and non-equimolar diffusion, can happen in some instances (Scanlon et al., 2000). The first two processes are assumed to occur as a result of pore walls and the resultant molecule-wall collisions (Cunningham and Williams, 1980). The other requires the presence of both system walls and a multistep gas; such conditions are most commonly seen in porous media and result in a divergence from Fick's rule (Sleep, 1998). Higher gas pressures, notably those around organic liquid sources, diverge from Fick's rule, thus according Baehr and Bruell (1990). Diffusion is a solute-dependent component of dispersion due to the connections between average kinetic energy, velocity, and molecular mass (Molly & Mark, 2006). Meanwhile, as demonstrated in Eq. 2, the average kinetic energy of all gases at constant temperature is identical.
+
+
+Ek=32kT=32mv2rms(2)
+
+
+where k denotes Boltzmann's constant (J/K), T denotes temperature (K), m denotes solute mass (kg), and vrms is the root-mean-square velocity of the gas particles (m/s). As a result, under thermal equilibrium and equivalent kinetic energy, lower molecular weight gases are thought to have higher average velocities than higher molecular weight gases (Molly & Mark, 2006). Because of the higher velocity, the diffusion coefficients become more prominent, contributing to overall dispersion domination. Diffusion processes, such as aggregates or fine-textured lenses, control transport in low-permeability zones.
+
+
+Compression ratio
+
+
+The term compression ratio can apply to a single compression cycle as well as a multilevel reduction stage. When used to a single device or set of compression, it is characterized as the phase or unit compression ratio; when related to a multiphase compressor, it is described as the total compression ratio. The compression ratio of most gas pipeline compressors is low. A sole compression cycle in a reciprocating engine and a single entity in a centrifugal compressor can meet low pressure ratios. While pressure ratio is an essential indicator for reciprocating compressors, the pressure ratio that a certain centrifugal compressor can generate is primarily controlled by gas composition and temperature. For natural gas, a single centrifugal stage may provide a pressure ratio of 1.4 (with a specific gravity of 0.58–0.70). The compression ratio (CR) is defined as the ratio of actual discharge pressure to absolute suction pressure. Eq. 3 shows how this is expressed numerically.
+
+
+CR=(P2P1)γ(3)
+
+
+However, the ratio of P2P1 represent the pressure ratio (PR). Therefore, Eq. 3 can be re-written as:
+
+
+CR=(PR)γ(4)
+
+
+Where γ is the specific heat ratio for the working gas, which is about 1.4 for N2 or air and 1.28 for CO2.
+
+
+Methodology and Materials
+
+
+The specific dimensions and petrophysical parameters of the 1 by 3 Bandera grey sandstone core sample are displayed in Table 1. This specimen was obtained from the Kocurek factory in the United States of America. Furthermore, high purity CH4 and industrial-grade CO2 with a minimum purity of 99.9 per cent were employed during the investigation.
+
+
+Table 1Bandera grey core plug measurements and rock properties characteristics Core sample
+            . Length (mm)
+            . Diameter (mm)
+            . Porosity (%)
+            . Permeability (md)
+            . Pore volume (cm3)
+            . Bandera gray 76.02 25.31 19.68 32 7.53 Core sample
+            . Length (mm)
+            . Diameter (mm)
+            . Porosity (%)
+            . Permeability (md)
+            . Pore volume (cm3)
+            . Bandera gray 76.02 25.31 19.68 32 7.53 View Large
+
+
+To explore the effect of gas injection interfacial velocity on gas reservoir pore pressure, a core flooding testing was conducted at 30-40 °C and 1500 psig. The core plugs were preheated overnight at 100-110 °C to avoid impurities interference. The dried sample was then wrapped in cling film and foil sheets before being heat shrunk. This is essential for reducing viscous fingering and gas infiltration into the ring-shaped core holder through the sleeve. It was then clamped from both ends and stapled into the core holder. To prevent cracking the core sleeve, hydraulic oil was pumped into the ring-shaped core holder to provide the necessary overburden pressure, which was kept 500 psig well above pore pressures. The heat jacket was then put on top of the core holder, and the temperature rise was recorded. The backpressure was then triggered, and the measuring pump was utilised to gently feed CH4 from the accumulator into the core sample. Following that, the line was totally saturated with methane. The CO2 injection rate and N2 booster volume were adjusted to 0.2-1.2 ml/min and 8-36 cm3, respectively, and a GC printout of the final product concentration was taken after 5 minutes. Finally, the experiment was called off (i.e., CH4 < 1-2 percent) and the line was de-pressurized.
+
+
+Figure 1View largeDownload slideSchematics of experimentational set-up for gas alternating gas injectionFigure 1View largeDownload slideSchematics of experimentational set-up for gas alternating gas injection Close modal
+
+
+Reseults and Discussion
+
+
+CO2 compression is a critical step in the development of carbon capture and storage (CCS) technology. A full CCS system necessitates safe, dependable, and cost-effective CO2 conveyance choices from the capture rig to a permanent storage location. CO2 compression varies from N2 compression due to its large molecular mass and highly compressible processes. During the compression process, the CO2 volume is greatly reduced, resulting in a huge impeller diameter. In general, CO2 compression is highly costly due to the high-pressure ratio (100:1) caused by the presence of water vapour forming during compression. In contrast, N2 may be recovered almost entirely from ambient air through an air separation unit. It has a lower compression ratio than CO2, hence less of it was required to generate high pressure in the CH4 reservoir during displacement. Fig. 2 depicts a plot of the compression ratio vs reservoir pressure. Because the maximum reservoir pressure would not surpass the given value during the CH4 displacement, a maximum pressure of 2000 psig was evaluated. Because neither of the experimental runs surpassed the 2000 psig reservoir pressure during the core flooding experiment, this number was determined to be correct. Overall, the CO2 compression ratio deviates parallelly as reservoir pressure increases, with CO2 witnessing around 33 climbs (from 13 to 47) within the pressure range (400-2000 psig) studied. This appears to be massive when compared to N2 with 22, showing a 50% increase. A comparable study was performed utilising core flooding logging data at 1500 psig, 40 °C, 0.4 ml/min injection rate, and various N2 cushion gas quantities. The results showed that when the cushion gas volume increased from 8 to 36 cm3, the head load decreased. This was visible in the decreased percentage heat load compared to pure CO2 injection, with the greatest result observed at 24 cm3 equivalent to a 25% power loss due to heat reduction. When the pressure ratio (PR) was plotted against the experimental time for traditional N2 and CO2 injection, a similar pattern was found in Fig. 3. A decline in PR was detected prior to both injections following methane saturation, which might be attributed to changes in thermophysical characteristics of the displacing fluids (N2 and CO2) and the displaced gas (CH4). Because of the behaviour of CO2 at a supercritical state, this reduction was more pronounced in CO2 than in N2. As a result, the PR of CO2 was discovered to be 4% higher than that of N2 at the end of the displacement experiment.
+
+
+Figure 2View largeDownload slideThe plot of compression ratio (CR) against reservoir pressureFigure 2View largeDownload slideThe plot of compression ratio (CR) against reservoir pressure Close modal
+
+
+Figure 3View largeDownload slide: The plot of pressure ratio (PR) against experimental time at 1500 psig, 40 °C, 0.4 ml/min injection rate, and 8-36 cm3 cushion volumesFigure 3View largeDownload slide: The plot of pressure ratio (PR) against experimental time at 1500 psig, 40 °C, 0.4 ml/min injection rate, and 8-36 cm3 cushion volumes Close modal
+
+
+Because of the responsiveness of their transport qualities to changing from ambient standard settings to EGR conditions, these fluids demonstrate good behaviour. As a result, a simulation of their respective characteristics under higher operational circumstances was evaluated to examine the behavoir of their density and pore pressure during the natural gas displacement process. These characteristics of gases varied significantly, with CO2 transport properties being far higher and more intense than N2 and CH4. This is most noticeable at pressures ranging from 650 to 1500 psig, as seen in Fig. 4. This validates the experimental circumstances chosen, as reported by Abba et al., 2018.
+
+
+Figure 4View largeDownload slideDensity and pore pressure flow behavoir for CO2, CH4, and N2 gasFigure 4View largeDownload slideDensity and pore pressure flow behavoir for CO2, CH4, and N2 gas Close modal
+
+
+Conclusion
+
+
+The effects of gas injections on compression ratio and reservoir pore pressures during EGR and CO2 storage were investigated using empirical modelling and displacement experiments. During the displacement process, N2 has a smaller compression ratio and pore pressure rise than CO2 gas, according to the data. The typical CO2 injection tests revealed a significant increase in pore pressure as the injection rate increased. In general, increasing reservoir pore pressure leads to increasing mean interstitial velocity, and the same patterns were found at the CO2 breakthrough point. The experiment with N2 as a booster, on the other hand, recorded a continuous and constant pore pressure rise, which was responsible for their low dispersion coefficient value. As a result, less gas mixing occurs during the natural gas recovery process than with conventional CO2 flooding.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+Acknowledgment
+
+
+The authors would like to thank the Petroleum Technology Development Fund, PTDF Abuja Nigeria, for their financial assistance and studentship.
+
+
+Appendix
+
+
+Table A1N2 experimental and numerical data Time
+            . Pure N2
+            . PR
+            . CR
+            . log PR
+            . log PR/R
+            . 0.15 1509 102.68 27.34 2.011 1.437 5.82 1493 101.59 27.13 2.007 1.433 11.32 1497 101.86 27.18 2.008 1.434 16.82 1503 102.27 27.26 2.010 1.436 22.32 1508 102.61 27.33 2.011 1.437 27.82 1514 103.02 27.40 2.013 1.438 33.32 1519 103.36 27.47 2.014 1.439 38.82 1525 103.77 27.55 2.016 1.440 44.32 1531 104.18 27.62 2.018 1.441 49.82 1537 104.59 27.70 2.019 1.442 55.32 1542 104.93 27.76 2.021 1.443 60.82 1548 105.33 27.84 2.023 1.445 66.32 1554 105.74 27.92 2.024 1.446 71.82 1560 106.15 28.00 2.026 1.447 77.32 1565 106.49 28.06 2.027 1.448 82.82 1570 106.83 28.12 2.029 1.449 88.32 1576 107.24 28.20 2.030 1.450 93.65 1581 107.58 28.26 2.032 1.451 99.32 1586 107.92 28.33 2.033 1.452 104.65 1592 108.33 28.40 2.035 1.453 110.48 1595 108.53 28.44 2.036 1.454 115.82 1601 108.94 28.52 2.037 1.455 121.32 1605 109.21 28.57 2.038 1.456 126.98 1608 109.42 28.61 2.039 1.456 132.48 1612 109.69 28.66 2.040 1.457 137.98 1614 109.83 28.68 2.041 1.458 143.82 1616 109.96 28.71 2.041 1.458 149.48 1617 110.03 28.72 2.042 1.458 154.82 1617 110.03 28.72 2.042 1.458 160.32 1618 110.1 28.74 2.042 1.458 165.82 1617 110.03 28.72 2.042 1.458 171.32 1617 110.03 28.72 2.042 1.458 176.82 1617 110.03 28.72 2.042 1.458 182.66 1616 109.96 28.71 2.041 1.458 188.15 1616 109.96 28.71 2.041 1.458 193.65 1615 109.89 28.70 2.041 1.458 199.32 1614 109.83 28.68 2.041 1.458 205.15 1613 109.76 28.67 2.040 1.457 210.65 1612 109.69 28.66 2.040 1.457 216.15 1611 109.62 28.65 2.040 1.457 221.48 1610 109.55 28.63 2.040 1.457 226.98 1610 109.55 28.63 2.040 1.457 232.82 1608 109.42 28.61 2.039 1.456 238.65 1608 109.42 28.61 2.039 1.456 244.15 1607 109.35 28.60 2.039 1.456 Time
+            . Pure N2
+            . PR
+            . CR
+            . log PR
+            . log PR/R
+            . 0.15 1509 102.68 27.34 2.011 1.437 5.82 1493 101.59 27.13 2.007 1.433 11.32 1497 101.86 27.18 2.008 1.434 16.82 1503 102.27 27.26 2.010 1.436 22.32 1508 102.61 27.33 2.011 1.437 27.82 1514 103.02 27.40 2.013 1.438 33.32 1519 103.36 27.47 2.014 1.439 38.82 1525 103.77 27.55 2.016 1.440 44.32 1531 104.18 27.62 2.018 1.441 49.82 1537 104.59 27.70 2.019 1.442 55.32 1542 104.93 27.76 2.021 1.443 60.82 1548 105.33 27.84 2.023 1.445 66.32 1554 105.74 27.92 2.024 1.446 71.82 1560 106.15 28.00 2.026 1.447 77.32 1565 106.49 28.06 2.027 1.448 82.82 1570 106.83 28.12 2.029 1.449 88.32 1576 107.24 28.20 2.030 1.450 93.65 1581 107.58 28.26 2.032 1.451 99.32 1586 107.92 28.33 2.033 1.452 104.65 1592 108.33 28.40 2.035 1.453 110.48 1595 108.53 28.44 2.036 1.454 115.82 1601 108.94 28.52 2.037 1.455 121.32 1605 109.21 28.57 2.038 1.456 126.98 1608 109.42 28.61 2.039 1.456 132.48 1612 109.69 28.66 2.040 1.457 137.98 1614 109.83 28.68 2.041 1.458 143.82 1616 109.96 28.71 2.041 1.458 149.48 1617 110.03 28.72 2.042 1.458 154.82 1617 110.03 28.72 2.042 1.458 160.32 1618 110.1 28.74 2.042 1.458 165.82 1617 110.03 28.72 2.042 1.458 171.32 1617 110.03 28.72 2.042 1.458 176.82 1617 110.03 28.72 2.042 1.458 182.66 1616 109.96 28.71 2.041 1.458 188.15 1616 109.96 28.71 2.041 1.458 193.65 1615 109.89 28.70 2.041 1.458 199.32 1614 109.83 28.68 2.041 1.458 205.15 1613 109.76 28.67 2.040 1.457 210.65 1612 109.69 28.66 2.040 1.457 216.15 1611 109.62 28.65 2.040 1.457 221.48 1610 109.55 28.63 2.040 1.457 226.98 1610 109.55 28.63 2.040 1.457 232.82 1608 109.42 28.61 2.039 1.456 238.65 1608 109.42 28.61 2.039 1.456 244.15 1607 109.35 28.60 2.039 1.456 View Large
+
+
+Table A2CO2 experimental and numerical data Time
+            . Pure CO2
+            . PR
+            . CR
+            . log PR
+            . log PR/R
+            . 0.15 1506 102.48 37.22 2.011 1.571 5.15 1467 99.823 36.47 1.999 1.562 11.32 1482 100.84 36.76 2.004 1.565 16.82 1494 101.66 36.99 2.007 1.568 22.32 1502 102.2 37.14 2.009 1.570 27.82 1511 102.82 37.32 2.012 1.572 33.32 1522 103.57 37.53 2.015 1.574 39.48 1535 104.45 37.78 2.019 1.577 44.98 1548 105.33 38.03 2.023 1.580 50.65 1562 106.29 38.30 2.026 1.583 56.32 1576 107.24 38.57 2.030 1.586 61.82 1588 108.06 38.80 2.034 1.589 67.32 1599 108.81 39.01 2.037 1.591 72.65 1606 109.28 39.14 2.039 1.593 78.15 1614 109.83 39.29 2.041 1.594 83.65 1625 110.57 39.50 2.044 1.597 89.15 1634 111.19 39.67 2.046 1.598 94.65 1639 111.53 39.77 2.047 1.600 100.15 1645 111.94 39.88 2.049 1.601 105.65 1650 112.28 39.97 2.050 1.602 111.15 1656 112.68 40.09 2.052 1.603 116.48 1661 113.02 40.18 2.053 1.604 Time
+            . Pure CO2
+            . PR
+            . CR
+            . log PR
+            . log PR/R
+            . 0.15 1506 102.48 37.22 2.011 1.571 5.15 1467 99.823 36.47 1.999 1.562 11.32 1482 100.84 36.76 2.004 1.565 16.82 1494 101.66 36.99 2.007 1.568 22.32 1502 102.2 37.14 2.009 1.570 27.82 1511 102.82 37.32 2.012 1.572 33.32 1522 103.57 37.53 2.015 1.574 39.48 1535 104.45 37.78 2.019 1.577 44.98 1548 105.33 38.03 2.023 1.580 50.65 1562 106.29 38.30 2.026 1.583 56.32 1576 107.24 38.57 2.030 1.586 61.82 1588 108.06 38.80 2.034 1.589 67.32 1599 108.81 39.01 2.037 1.591 72.65 1606 109.28 39.14 2.039 1.593 78.15 1614 109.83 39.29 2.041 1.594 83.65 1625 110.57 39.50 2.044 1.597 89.15 1634 111.19 39.67 2.046 1.598 94.65 1639 111.53 39.77 2.047 1.600 100.15 1645 111.94 39.88 2.049 1.601 105.65 1650 112.28 39.97 2.050 1.602 111.15 1656 112.68 40.09 2.052 1.603 116.48 1661 113.02 40.18 2.053 1.604 View Large
+
+
+References
+
+
+Abba, M.K., Abbas, A.J., Athari, A., Mukhtar, A., & Nasr, G.G. (2018). Experimental Investigation on the Impact of Connate Water Salinity on Dispersion Coefficient in Consolidated Rocks Cores during EGR by CO2 Injection. Journal of Natural Gas Science and Engineering, 60, 190–201. http://doi.org/10.1016/j.jngse.2018.10.007.Google ScholarCrossrefSearch ADS  Abba, M.K., Abbas, A.J., & Nasr, G.G. (2017). Enhanced Gas Recovery by CO2 Injection and Sequestration: Effect of Connate Water Salinity on Displacement Efficiency. SPE Abu Dhabi International Petroleum Exhibition & Conference. http://www.onepetro.org/doi/10.2118/188930-MS.Google Scholar Adepoju, O. O., Lake, L. W., & Johns, R. T. (2013). Investigation of Anisotropic Mixing in Miscible Displacements. SPE Reservoir Evaluation & Engineering, 16(1), 85–96.Google ScholarCrossrefSearch ADS  Al-abri, A., SidiqH., & AminR. (2009). 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(2014). CO2 injection for Enhanced Gas Recovery. SPE Western North American and Rocky Mountain, 16–18. https://www.onepetro.org/conferencepaper/SPE-169578–MS.Google Scholar Khan, C., AminR., & MaddenG. (2013). Carbon dioxide injection for enhanced gas recovery and storage (reservoir simulation). Egyptian Journal of Petroleum, 22(2), 225–240. http://www.sciencedirect.com/science/article/pii/S1110062113000500.Google ScholarCrossrefSearch ADS  MollyS. Costanza-Robinson and MarkL. Brussseau (2006). Gas Transport in Porous Media, Springer. Printed in the Netherlands, 121–132.Google Scholar Oldenburg, C.M., & BensonS.M., 2002. CO2 Injection for Enhanced Gas Production and Carbon Sequestration. SPE International Petroleum Conference and Exhibition in Mexico. http://www.onepetro.org/doi/10.2118/74367-MS.Google Scholar Honari, A., Bijeljic, B., Johns, M. L., & May, E. F. (2015). Enhanced gas recovery with {CO2} sequestration: The effect of medium heterogeneity on the dispersion of supercritical CO2–CH4. International Journal of Greenhouse Gas Control, 39(0), 39–50. http://doi.org/http://dx.doi.org/10.1016/j.ijggc.2015.04.014Google Scholar Honari, A., Hughes, T. J., Fridjonsson, E. O., Johns, M. L., & May, E. F. (2013). Dispersion of supercritical CO2 and CH4 in consolidated porous media for enhanced gas recovery simulations. International Journal of Greenhouse Gas Control, 19, 234–242. http://doi.org/10.1016/j.ijggc.2013.08.016Google ScholarCrossrefSearch ADS  Honari, A., Zecca, M., Vogt, S. J., Iglauer, S., Bijeljic, B., Johns, M. L., & May, E. F. (2016). The impact of residual water on CH4-CO2 dispersion in consolidated rock cores. International Journal of Greenhouse Gas Control, 50, 100–111. http://doi.org/10.1016/j.ijggc.2016.04.004Google ScholarCrossrefSearch ADS  Perkins, T.., & Johnston, O. (1963). A Review of Diffusion and Dispersion in Porous Media. Society of Petroleum Engineers Journal, 3(01), 70–84. 10.2118/480-PAGoogle ScholarCrossrefSearch ADS  Patel, M. J., May, E. F., & Johns, M. L. (2016). High-fidelity reservoir simulations of enhanced gas recovery with supercritical CO2. Energy, IN PRESS, 548–559. http://doi.org/10.1016/j.energy.2016.04.120Google ScholarCrossrefSearch ADS  Pooladi-Darvish, M., Hong, H., Theys, S., Stocker, R., Bachu, S., & Dashtgard, S. (2008). CO2 injection for enhanced gas recovery and geological storage of CO2 in the Long Coulee Glauconite F Pool, Alberta. Proceedings - SPE Annual Technical Conference and Exhibition, 4, 2271–2281. http://doi.org/10.2118/115789-MSGoogle Scholar Sim, S. S. K., Brunelle, P., Canada, Q., Systems, F., Turta, A. T., & Singhal, A. K. (2008). SPE 113468 Enhanced Gas Recovery and CO2 Sequestration by Injection of Exhaust Gases from Combustion of Bitumen. Journal of Changes, (1), 1–10.Google Scholar Sim, S. S. K., Turta, A. T., Singhal, A. K., & Hawkins, B. F. (2009). Enhanced gas recovery: Factors affecting gas-gas displacement efficiency. Journal of Canadian Petroleum Technology, 48(8), 49–55. http://doi.org/10.2118/09-08-49Google ScholarCrossrefSearch ADS  Sidiq, H., Amin, R., der Steen, E. Van, & Kennaird, T. (2011). Super critical CO2-methane relative permeability investigation. Journal of Petroleum Science and Engineering, 78(3–4), 654–663. http://doi.org/10.1016/j.petrol.2011.08.018Google Scholar Shtepani, E. (2006). CO2 sequestration in depleted gas/condensate reservoirs. Proceedings - SPE Annual Technical Conference and Exhibition.Google Scholar Sleep, B.E (1998). Modeling Transient Organic Vapor Transport in Porous Media with the Dusty Gas Model. Advances in Water Resources, 22(3):247–256.Google ScholarCrossrefSearch ADS  Scanlon, B.R., Nicot, J.P., Massmann, J.M (2000). Soil Gas Movement in Unsaturated Systems. In Handbook of Soil Science, Sumner, M.E., ed. Boca Raton: CRC Press LLC.Google ScholarCrossrefSearch ADS  Zhang, Y., Liu, S., Song, Y., Zhao, J., Tang, L., Xing, W., Jian, W., Liu, Z., Zhan, Y. (2014). Experimental investigation of CO2-CH4 displacement and dispersion in sand pack for enhanced gas recovery. Energy Procedia, 61, 393–397. http://dx.doi.org/10.1016/j.egypro.2014.11.1133.Google ScholarCrossrefSearch ADS  
+
+
+
+
+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/211971-MS
+
+
+

files/2022/Fingerprint Analysis of Light Crude Oils from Niger Delta.txt

@@ -0,0 +1,245 @@
+----- METADATA START -----
+Title: Fingerprint Analysis of Light Crude Oils from Niger Delta
+Authors: Ishioma Oshilike, Bella Mmata, Paschal Ugwu, Martins Otokpa, Chidinma Ibekwe, Okeke Hilary, Mike Onyekonwu
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212002-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Crude oil fingerprinting is a term applied to techniques that utilize geochemical analysis of hydrocarbon fluids composition to provide valuable information for well, reservoir and spill management. Analysis of crude oil fingerprints reveals a typical oil profile. Such a profile can provide information on formation history, type of carbon number preference during formation and route of migration. This study was undertaken using whole oil fingerprint and biomarkers of oils from twenty well strings from an onshore field in the Niger Delta Region. The aim was to evaluate light crude oils and determine thermal maturity, source rock quality, depositional environment and condensate correlation. The crude oil samples were analyzed using two major analytical techniques namely Gas Chromatography-Flame Ionization Detector (GC-FID) and Gas Chromatography-Mass Spectrometry (GC-MS). Evaluation of light hydrocarbon components was done using Mango parameters K1, K2, P2, P3 and N2 and the results revealed terrigenous organic matter input. Biomarker composition and pristane/phytane ratios in the range of 3.51 to 6.83 derived from GC results show that the source rock of the oils is made up of majorly terrestrial (type III) organic matter, deposited in a deltaic setting with prevailing oxic conditions. Maturity parameters calculated from Carbon Preference Indices between the range of 0.87 and 1.44 indicate the source is matured. The study provides key information on source characteristics that are applied to describe the type of petroleum prospects of a region. This study also provides information on condensate correlation, which has production implications such as application to production allocation.
+
+
+
+
+Keywords:
+geochemical characterization,
+correlation,
+table 4,
+niger delta,
+information,
+biomarker,
+fingerprint,
+compound,
+upstream oil & gas,
+hydrocarbon
+
+
+Subjects: 
+Fluid Characterization,
+Geochemical characterization
+
+
+
+
+Introduction
+
+
+Crude oil is created through the heating and compression of remains of organic materials that is, plants and animals over a long period of time. The original chemistry of the organic matter, the environment of deposition, the time and heat imposed on the organic matter dictate the type of crude oil formed (Fingas, 2011). As a result, every crude oil exhibits a unique chemical fingerprint due to the variety of geological conditions and ages under which it was formed. The concept of crude oil fingerprinting lies on the premise that every crude oil has a unique signature referred to as "fingerprint". Crude oils can be characterized compositionally by a number of established methods. Gas Chromatographic (GC) techniques are the most widely used. In gas chromatography, high-resolution capillary gas chromatography columns are used to generate fingerprints of petroleum consisting of hundreds of peaks in the range of C1-C40 (Douglas, 2007). Condensates are light crude oil with an API gravity that is typically between 50 degrees and 120 degrees (Schlumberger, Oilfield glossary, 2022). Light Hydrocarbons (e.g., methane, ethane and propane) are the main petroleum fractions in condensates. They are by-products, formed by thermal processes in the breakdown of kerogen. Due to their advanced maturity, condensates often contain no biomarkers (Halpern, 1995). To carry out fingerprint analysis on light crude oils such as condensates, there is need to evaluate the light hydrocarbons as the sparsity of biomarkers in these oils provides little information on characterizing these oils. Several authors have adopted evaluation of light hydrocarbon parameters to characterizing crude oils (Atwah, 2019, Halpern, 1994, Zhang et al, 2004)
+
+
+In this study, analysis was carried out on crude oil samples using gas chromatography techniques for their whole oil fingerprints and biomarkers. The study evaluated twenty stock tank oil samples obtained from twenty well strings in a Niger Delta field. The crude oil samples from these well strings were analyzed using Gas Chromatography-Flame Ion Detector (GC-FID) and Gas Chromatography-Mass Spectrometry (GC-MS) analytical methods. Mango’s light hydrocarbon parameters and Halpern’s Light-Hydrocarbon-Based Star Diagrams were applied. Results were interpreted to provide information relating to the organic source materials, environmental conditions during deposition, thermal maturity experienced by oil, condensate correlation and the degree of biodegradation
+
+
+Methodology
+
+
+Light hydrocarbon was used in crude oil fingerprinting to reveal information about organic source materials, environmental conditions during deposition, thermal maturity and the degree of transformation experienced by the oil. Twenty condensate samples from twenty different well strings were correlated based on the aforementioned information. Gas chromatographic analysis was performed using GC-FID and GC-MS. The heights of individual hydrocarbon parameters were selected and the results from the GC were then quality checked. Data preparation into suitable format for p:IGI software was done on excel. Figure 1 shows the workflow followed for the evaluation. Each step is discussed further.
+
+
+Figure 1View largeDownload slideWorkflow used for geochemical crude oil fingerprintingFigure 1View largeDownload slideWorkflow used for geochemical crude oil fingerprinting Close modal
+
+
+GC-FID and GC-MS Analyses
+
+
+GC-FID was used to analyze the whole oils while saturates fractions were analyzed using GC-MS. The GC-FID analysis was done on 7890A GC system equipped with capillary column 50m long with internal diameter of 0.2mm. Helium gas at flow rate of 3 ml/minute was used as the carrier. The GC oven was initially held at 35°C for 4 minutes and then ramped at 4°C/minute to 325°C. The GC-MS analysis on the other hand was done on a 7890A GC system coupled to Agilent 5975C mass selective detector (70ev). The saturate fraction was injected unto the column (HP-5MS, 30m × 250 µm × 0.25um) via a splitless injection mode and the oven temperature program optimized thus; 150°C hold for 10mins, 3°C/min to 270°C and hold for 35 mins. The GC was linked to the MS via the auxiliary transfer line maintained at 275°C. Target ions were acquired via SIM mode with the source and quad temperatures maintained at 230°C and 150°C respectively. Tri, tetracyclic terpanes and hopanes were identified from m/z 191 and m/z 177 while m/z 217 and 218 were used to identify steranes. Data generated from the analysis were used for further evaluation and interpretation.
+
+
+Data QA/QC and Preparation
+
+
+Heights of hydrocarbon compounds were checked for outliers to make sure all parameters needed for data interpretation were present. Since each crude oil sample was analyzed using three runs for both GC-MS and GC-FID, the relative standard deviation was calculated. Peak heights of compounds having relative standard deviation above ten were removed as outliers. The outputted results from GC were re-organized on excel into a format more suitable for p:IGI software.
+
+
+Selection and Generation of Ratios and Parameters
+
+
+Heptane, C7 hydrocarbons were used for light hydrocarbon evaluation using Mango parameters. Ratios were constructed using most transformation resistant compound as the denominator with susceptible compounds as numerators.C7 hydrocarbons were used for correlation because compared to lighter hydrocarbons, their relatively high boiling points makes them somewhat resistant to evaporation that may have resulted in the course of storage and sample preparation as well as alteration by sampling procedures. Mango parameters K1, K2, P2, P3 and N2 were employed to infer source of organic matter input. Parameters making K1, K2, P2, P3 and N2 are given in Table 1.
+
+
+Table 1Mango Parameters Parameter name
+            . Ratio
+            . K1 (2-MH + 2, 3-DMP)/(3-MH + 2,4-DMP) K2 P1/(P2 + N2) P2 2-MH + 3-MH P3 3-EP + 2, 3-DMP + 2, 3-DMP + 2, 2-DMP + 3, 3-DMP + 2, 3, 3-TMB N2 1, 1-DMCP + c-1, 3-DMCP + t-1, 3-DMCP Parameter name
+            . Ratio
+            . K1 (2-MH + 2, 3-DMP)/(3-MH + 2,4-DMP) K2 P1/(P2 + N2) P2 2-MH + 3-MH P3 3-EP + 2, 3-DMP + 2, 3-DMP + 2, 2-DMP + 3, 3-DMP + 2, 3, 3-TMB N2 1, 1-DMCP + c-1, 3-DMCP + t-1, 3-DMCP P1 = n-C7, MH = Methylhexane, DMP = Dimethylpentane, EP = Ethylpentane, TMB = Trimethylbutane, DMCP = DimethylcyclopentaneView Large
+
+
+Biodegradation and alteration were inferred from Halpern’s transformation parameters (eight C7 process transformation ratios). The ratios, designated Tr1 through Tr8 were plotted on the C7 oil transformation star diagrams (C7OTSD) and are ranked according to the magnitude of their change (decrease in value) in biodegradation. Halpern’s five C7 gas chromatographic correlation ratios were used for condensate correlation. The ratios C1 through C5 are plotted on C7 oil correlation star diagram (C7OCSD) using p:IGI software. The hydrocarbon parameters making up Tr1 to Tr8 and C1 to C5 are given in Tables 2 and 3 respectively.
+
+
+Table 2Halpern’s Transformation Parameters Position on star diagram
+            . Parameter name
+            . Ratio
+            . 1 Tr1 Toluene/1,1-dimethylcyclopentane 2 Tr2 n-C7/1,1-dimethylcyclopentane 3 Tr3 3-methylhexane/1,1-dimethylcyclopentane 4 Tr4 2-methylhexane/1,1-dimethylcyclopentane 5 Tr5 P2/1,1-dimethylcyclopentane 6 Tr6 1-cis-2-dimethylcyclopentane/1,1-dimethylcyclopentane 7 Tr7 1-trans-3-dimethylcyclopentane/1,1-dimethylcyclopentane 8 Tr8 P2/P3 Position on star diagram
+            . Parameter name
+            . Ratio
+            . 1 Tr1 Toluene/1,1-dimethylcyclopentane 2 Tr2 n-C7/1,1-dimethylcyclopentane 3 Tr3 3-methylhexane/1,1-dimethylcyclopentane 4 Tr4 2-methylhexane/1,1-dimethylcyclopentane 5 Tr5 P2/1,1-dimethylcyclopentane 6 Tr6 1-cis-2-dimethylcyclopentane/1,1-dimethylcyclopentane 7 Tr7 1-trans-3-dimethylcyclopentane/1,1-dimethylcyclopentane 8 Tr8 P2/P3 P2 = 2-methylhexane + 3-methylhexaneP3 = 2,2-dimethylpentane + 2,3-dimethylpentane + 2,4-dimethylpentane + 3,3-dimethylpentane + 3-ethylpentaneView Large
+
+
+Table 3Halpern’s Correlation Parameters Position on star diagram
+            . Parameter name
+            . Ratio
+            . 1 C1 2,2-dimethylpentane/P3 2 C2 2,3-dimethylpentane/P3 3 C3 2,4-dimethylpentane/P3 4 C4 3,3-dimethylpentane/P3 5 C5 3-ethylpentane/P3 Position on star diagram
+            . Parameter name
+            . Ratio
+            . 1 C1 2,2-dimethylpentane/P3 2 C2 2,3-dimethylpentane/P3 3 C3 2,4-dimethylpentane/P3 4 C4 3,3-dimethylpentane/P3 5 C5 3-ethylpentane/P3 P3 = 2,2-dimethylpentane + 2,3-dimethylpentane + 2,4-dimethylpentane + 3,3-dimethylpentane + 3-ethylpentane.View Large
+
+
+From the analysis, it can be deduced that the crude oil samples were majorly condensates (light crude), and their biomarker imprints were limited, or even absent in some oil samples (Table 4). Diagnostic biomarker ratios and parameters such as hopane/sterane, C29/C30hop and oleanane index where present were calculated and aided in data interpretation. The ratios were calculated using extracted peak heights.
+
+
+Table 4Organic Matter Input and Depositional Condition Parameter Wells
+            . Pr/Ph
+            . K1
+            . K2
+            . CPI
+            . C29/C30hop
+            . Oleanane index
+            . %C29
+            . αααR C29/C27
+            . Hopane/Sterane
+            . 1 5.00 1.07 0.23 0.87 0.66 0.23 49.04 2.62 3.37 2 5.27 1.07 0.22 1.29 0.73 0.30 39.44 2.51 2.03 3 4.65 1.08 0.23 1.07 0.57 0.33 40.52 4.25 3.57 4 4.58 1.06 0.24 1.05 0.65 0.46 44.99 2.10 2.21 5 4.31 1.08 0.23 1.05 0.55 0.35 32.98 3.73 3.05 6 5.78 1.09 0.23 1.44 0.47 0.28 - 0.56 - 7 6.83 1.11 0.23 1.25 0.52 0.35 40.51 3.48 1.99 8 3.52 1.10 0.22 1.07 0.68 0.32 47.15 2.45 4.17 9 4.62 1.09 0.23 1.11 0.69 0.30 32.02 2.54 5.02 10 5.27 1.09 0.23 1.03 0.69 0.25 41.12 2.47 4.36 11 3.51 1.08 0.22 1.11 0.69 0.34 44.92 3.44 3.46 12 3.95 1.09 0.22 1.08 0.52 0.35 41.76 3.46 5.04 13 4.72 1.09 0.24 1.06 0.69 0.29 43.64 2.45 1.72 14 4.96 1.08 0.24 1.04 0.69 0.27 41.36 2.89 1.84 15 5.40 1.10 0.23 1.05 0.61 0.24 46.42 2.91 3.43 16 5.25 1.10 0.23 1.04 0.69 0.29 47.15 2.20 3.46 17 5.46 1.10 0.23 1.07 0.80 0.29 38.80 2.00 3.35 18 5.12 1.10 0.23 1.08 0.81 0.36 49.58 - 4.89 19 4.86 1.08 0.24 0.87 0.53 0.34 41.37 2.25 1.84 20 4.41 1.10 0.23 1.29 0.98 - 47.75 - 3.07 Wells
+            . Pr/Ph
+            . K1
+            . K2
+            . CPI
+            . C29/C30hop
+            . Oleanane index
+            . %C29
+            . αααR C29/C27
+            . Hopane/Sterane
+            . 1 5.00 1.07 0.23 0.87 0.66 0.23 49.04 2.62 3.37 2 5.27 1.07 0.22 1.29 0.73 0.30 39.44 2.51 2.03 3 4.65 1.08 0.23 1.07 0.57 0.33 40.52 4.25 3.57 4 4.58 1.06 0.24 1.05 0.65 0.46 44.99 2.10 2.21 5 4.31 1.08 0.23 1.05 0.55 0.35 32.98 3.73 3.05 6 5.78 1.09 0.23 1.44 0.47 0.28 - 0.56 - 7 6.83 1.11 0.23 1.25 0.52 0.35 40.51 3.48 1.99 8 3.52 1.10 0.22 1.07 0.68 0.32 47.15 2.45 4.17 9 4.62 1.09 0.23 1.11 0.69 0.30 32.02 2.54 5.02 10 5.27 1.09 0.23 1.03 0.69 0.25 41.12 2.47 4.36 11 3.51 1.08 0.22 1.11 0.69 0.34 44.92 3.44 3.46 12 3.95 1.09 0.22 1.08 0.52 0.35 41.76 3.46 5.04 13 4.72 1.09 0.24 1.06 0.69 0.29 43.64 2.45 1.72 14 4.96 1.08 0.24 1.04 0.69 0.27 41.36 2.89 1.84 15 5.40 1.10 0.23 1.05 0.61 0.24 46.42 2.91 3.43 16 5.25 1.10 0.23 1.04 0.69 0.29 47.15 2.20 3.46 17 5.46 1.10 0.23 1.07 0.80 0.29 38.80 2.00 3.35 18 5.12 1.10 0.23 1.08 0.81 0.36 49.58 - 4.89 19 4.86 1.08 0.24 0.87 0.53 0.34 41.37 2.25 1.84 20 4.41 1.10 0.23 1.29 0.98 - 47.75 - 3.07 View Large
+
+
+Results and Discussion
+
+
+Crude oil fingerprints and biomarkers have been widely used by geochemists to determine organic input and depositional environment, assess thermal maturity, evaluate reservoir oil biodegradation and alteration. We have applied this to our study with emphasis on light hydrocarbons and the results are discussed.
+
+
+Light Hydrocarbons and Source Parameters
+
+
+The K1 Mango parameter of the oils from the different well strings were relatively low ranging from 1.06-1.11 as shown in Table 4. There is a general invariance in the values of K1 across the different oils. This indicates that the oils may have similar sources as homologous oils (from a common source) share a similar K1, and different K1 values may indicate different oil groups (Mango, 1994; Haven, 1996). In Figure 1a, a cross plot of P3 vs. P2+N2 shows the oil samples from the different well strings fall within one major group. The relatively low values of K2 between 0.22-0.24 as shown in Table 4 are suggestive of terrigenous organic matter input (Mango, 1994).
+
+
+Figure 1aView largeDownload slideCross plot of P3 vs. P2+N2Figure 1aView largeDownload slideCross plot of P3 vs. P2+N2 Close modal
+
+
+Organic Matter Input and Depositional Condition
+
+
+The GC fingerprints of the oils from the different well strings show a distinct unimodal pattern as represented in Figure 2, with the dominance of C3 – C30 maximizing between C4 – C7. This is typical of light oils with terrigenous organic matter input (Peters et al, 1993, Fadokun, 2014).
+
+
+Figure 2View largeDownload slideRepresentative GC chromatogram of the different oilsFigure 2View largeDownload slideRepresentative GC chromatogram of the different oils Close modal
+
+
+The pristane/phytane (Pr/Ph) ratio was used as it is one of the most commonly used geochemical parameters and has been applied as an indicator of depositional environment. High Pr/Ph (>3.0) indicates terrigenous input under oxic conditions, low (Pr/Ph<2) indicate aquatic depositional environment including marine, fresh and brackish water (reducing conditions), lower Pr/Ph (<0.8) indicates anoxic/hypersaline or carbonate environments, whereas higher values (up to 10) are related to peat swamp depositional environment (Haven 1996, Roushdy et al. 2010). Table 4 shows the different oils are characterized by pristane/phytane ratios in the range of 3.51 to 6.83 thus confirming that these oils originated from terrigenous organic matter deposited under an oxic paleo environment.
+
+
+Oleanane identified from m/z 191 (Figure 3) in Niger Delta oils is linked to higher land plants (angiosperm) contribution to organic matter in source rock (Ekweozor et al, 1979). Oleanane index (Oleanane/C30-hopane) was applied to assess the amount of higher land plant contribution to the source rock. The values for the ratio are in the range of 0.23-0.46 as seen in Table 4. These values indicate significant contribution from higher land (terrestrial) plants.
+
+
+Figure 3View largeDownload slideRepresentative m/z 191 Hopanes ChromatogramFigure 3View largeDownload slideRepresentative m/z 191 Hopanes Chromatogram Close modal
+
+
+Thermal Maturity and Alteration
+
+
+CPI which gives the ratio of odd carbon number to even carbon number n-alkanes in extractable organics was applied to assess thermal maturity of the oils. CPI was used as an indicator of maturation of crude oil. Immature rocks often have high CPI values. The CPI values ranges between 0.87 and 1.44 for all the different oils (Table 4), with most wells having CPI values above 1 signifying a dominance of odd carbon. The observed CPI values >1 in most of the oil samples is believed to be influenced by the thermal maturity, as all samples possess some level of maturity.
+
+
+The Halpern oil transformation plot (Figure 4) was used to distinguish variations in oils caused by transformation. The ratios were plotted in order of decreasing sensitivity to biodegradation through Tr1 to Tr8, with Tr1 being the most sensitive to biodegradation (as it measures the depletion in toluene which is by far the most soluble C7 compound i.e., Tr1 = tol/11DMCyC5). The values of Tr1 to Tr8 are given in Table 5. The relatively lower Tr1 values for wells 1, 5, 9, 10, 11, 12, 18 and 20 suggests that these oil samples have started undergoing some form of alteration.
+
+
+Figure 4View largeDownload slideHalpern Plot of oil transformation for the different oilsFigure 4View largeDownload slideHalpern Plot of oil transformation for the different oils Close modal
+
+
+Table 5Halpern’s Correlation and Transformation Parameters for The Oils from the Different Wells Wells
+            . Tr1
+            . Tr2
+            . Tr3
+            . Tr4
+            . Tr5
+            . Tr6
+            . Tr7
+            . Tr8
+            . C1
+            . C2
+            . C3
+            . C4
+            . C5
+            . 1 7.71 12.13 3.98 3.94 7.92 0.02 1.95 2.79 0.19 0.41 0.28 0.12 0.00 2 10.72 12.78 4.19 4.02 8.21 0.03 2.06 3.00 0.17 0.45 0.26 0.12 0.00 3 10.59 12.16 4.09 4.04 8.14 0.02 2.04 2.84 0.18 0.43 0.27 0.12 0.00 4 19.46 12.29 3.95 3.92 7.87 0.04 1.81 2.79 0.18 0.40 0.28 0.14 0.00 5 9.82 11.86 4.05 4.00 8.04 0.02 2.03 2.82 0.18 0.43 0.27 0.12 0.00 6 11.06 11.74 3.99 3.98 7.97 0.03 1.97 2.86 0.19 0.43 0.26 0.12 0.00 7 10.70 11.68 3.99 4.01 8.00 0.03 1.92 2.93 0.19 0.43 0.26 0.12 0.00 8 8.49 11.42 3.92 3.83 7.76 0.02 2.06 2.88 0.18 0.45 0.25 0.12 0.00 9 6.73 11.57 3.83 3.81 7.64 0.02 1.89 2.80 0.20 0.42 0.26 0.12 0.00 10 6.49 11.56 3.86 3.82 7.66 0.03 1.90 2.77 0.20 0.42 0.26 0.12 0.00 11 9.87 11.89 3.98 3.83 7.82 0.04 2.11 2.87 0.17 0.45 0.25 0.12 0.00 12 8.99 11.65 3.99 3.88 7.87 0.03 2.09 2.84 0.18 0.45 0.26 0.12 0.00 13 18.61 11.75 3.80 3.80 7.59 0.07 1.76 2.82 0.18 0.41 0.26 0.14 0.00 14 18.45 11.70 3.81 3.81 7.62 0.07 1.76 2.80 0.19 0.40 0.27 0.15 0.00 15 10.57 11.69 3.99 3.97 7.95 0.05 1.97 2.86 0.19 0.43 0.26 0.12 0.00 16 10.70 11.69 3.99 3.98 7.96 0.06 1.97 2.88 0.19 0.43 0.26 0.12 0.00 17 10.22 11.69 4.00 3.98 7.97 0.07 1.98 2.86 0.19 0.43 0.26 0.12 0.00 18 3.32 12.03 4.14 4.06 8.21 0.03 2.19 2.78 0.19 0.45 0.26 0.11 0.00 19 18.17 11.56 3.79 3.81 7.60 0.06 1.76 2.80 0.19 0.40 0.27 0.15 0.00 20 5.83 11.17 4.13 4.03 8.16 0.03 2.34 2.76 0.17 0.47 0.26 0.11 0.00 Wells
+            . Tr1
+            . Tr2
+            . Tr3
+            . Tr4
+            . Tr5
+            . Tr6
+            . Tr7
+            . Tr8
+            . C1
+            . C2
+            . C3
+            . C4
+            . C5
+            . 1 7.71 12.13 3.98 3.94 7.92 0.02 1.95 2.79 0.19 0.41 0.28 0.12 0.00 2 10.72 12.78 4.19 4.02 8.21 0.03 2.06 3.00 0.17 0.45 0.26 0.12 0.00 3 10.59 12.16 4.09 4.04 8.14 0.02 2.04 2.84 0.18 0.43 0.27 0.12 0.00 4 19.46 12.29 3.95 3.92 7.87 0.04 1.81 2.79 0.18 0.40 0.28 0.14 0.00 5 9.82 11.86 4.05 4.00 8.04 0.02 2.03 2.82 0.18 0.43 0.27 0.12 0.00 6 11.06 11.74 3.99 3.98 7.97 0.03 1.97 2.86 0.19 0.43 0.26 0.12 0.00 7 10.70 11.68 3.99 4.01 8.00 0.03 1.92 2.93 0.19 0.43 0.26 0.12 0.00 8 8.49 11.42 3.92 3.83 7.76 0.02 2.06 2.88 0.18 0.45 0.25 0.12 0.00 9 6.73 11.57 3.83 3.81 7.64 0.02 1.89 2.80 0.20 0.42 0.26 0.12 0.00 10 6.49 11.56 3.86 3.82 7.66 0.03 1.90 2.77 0.20 0.42 0.26 0.12 0.00 11 9.87 11.89 3.98 3.83 7.82 0.04 2.11 2.87 0.17 0.45 0.25 0.12 0.00 12 8.99 11.65 3.99 3.88 7.87 0.03 2.09 2.84 0.18 0.45 0.26 0.12 0.00 13 18.61 11.75 3.80 3.80 7.59 0.07 1.76 2.82 0.18 0.41 0.26 0.14 0.00 14 18.45 11.70 3.81 3.81 7.62 0.07 1.76 2.80 0.19 0.40 0.27 0.15 0.00 15 10.57 11.69 3.99 3.97 7.95 0.05 1.97 2.86 0.19 0.43 0.26 0.12 0.00 16 10.70 11.69 3.99 3.98 7.96 0.06 1.97 2.88 0.19 0.43 0.26 0.12 0.00 17 10.22 11.69 4.00 3.98 7.97 0.07 1.98 2.86 0.19 0.43 0.26 0.12 0.00 18 3.32 12.03 4.14 4.06 8.21 0.03 2.19 2.78 0.19 0.45 0.26 0.11 0.00 19 18.17 11.56 3.79 3.81 7.60 0.06 1.76 2.80 0.19 0.40 0.27 0.15 0.00 20 5.83 11.17 4.13 4.03 8.16 0.03 2.34 2.76 0.17 0.47 0.26 0.11 0.00 View Large
+
+
+Condensate Correlation
+
+
+Halpern’s five C7 gas chromatographic correlation ratios were used to determine the mixing of various oils in the reservoir and alteration processes among the oil samples from the different wells (Table 5). These correlations are plotted on oil correlation star diagrams (Figure 5) to capture the variation of very small ratios. The large boiling point difference in the compounds comprising C5 make it useful as a light-end loss (evaporation) parameter; it increases with light-end loss. C5 for all oil samples was zero because of the absence of the compound 3-ethyl pentane in the crude oil samples. The oils from the different wells show correlation among themselves both from the correlation star plot (Figure 5) and numerical values (Table 5). These results indicate the oils from the different wells are similar. This similarity can be attributed to similar charge and/or possible communication across the reservoirs.
+
+
+Figure 5View largeDownload slideHalpern Correlation Plot of the different oil samplesFigure 5View largeDownload slideHalpern Correlation Plot of the different oil samples Close modal
+
+
+Source Rock Quality
+
+
+All the oils show the dominance of C30hopane over C29hopane. The C29/C30hopane ratio <1, as seen in Table 4, suggest the oils were derived from clay-rich shale source rock. The oils are characterized by the presence of gammacerane, which suggests prevailing stratified water column conditions of source rock organic matter accumulation (Bjoroy et al., 1991, Wilhelms et al., 1994). m/z 217 chromatogram (Figure 6) shows distribution of steranes in the oils. A ternary plot of ααα (217) steranes in the oils (Figure 7) show that the source rock containing the organic matter are of a deltaic setting. Higher % C29 sterane and high ααα20R C29/C27 ratio for the oil samples (Table 4), indicate the dominance of terrestrial organic matter in the source rock.
+
+
+Figure 6View largeDownload slideRepresentative m/z 217 Steranes ChromatogramFigure 6View largeDownload slideRepresentative m/z 217 Steranes Chromatogram Close modal
+
+
+Figure 7View largeDownload slideααα (217) Sterane Distribution of the different oilsFigure 7View largeDownload slideααα (217) Sterane Distribution of the different oils Close modal
+
+
+Conclusion
+
+
+The light oil samples have been analyzed for their fingerprints. Most of the interpretations made were done using the light hydrocarbons present in these samples. The assessment of compositional variation using Halpern’s correlation parameters and star plots indicate one major pattern, suggesting similar charge and/or possible communication across the reservoirs. The results from biodegradation and alteration show that the oils are mostly unaltered. Biomarker composition and ratios derived from GC results which was limited was used to infer source rock quality. It revealed that the source rock of the oils from the field is made up of majorly terrestrial (type III) organic matter, deposited in a deltaic setting with prevailing oxic conditions. Maturity parameters calculated from Carbon Preference Indices indicate the source is matured. This study shows that evaluation of light hydrocarbon components (C7 and its isomers) proves to be a reasonable tool for fingerprint analysis of light crude oil samples where biomarkers might be scarce or even absent.
+
+
+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
+
+
+References
+
+
+AtwahIbrahim, StephenSweet, JohnPantano, AnthonyKnap, "Light Hydrocarbon Geochemistry: Insight into Mississippian Crude Oil Sources from the Anadarko Basin, Oklahoma, USA", Geofluids, vol. 2019, Article ID 2795017, 15 pages, 2019. https://doi.org/10.1155/2019/2795017Google Scholar Bjoroy, M., Hall, P.B., Gillyon, P., Jumeau, J., 1991. Carbon isotope variations in n-alkanes and isoprenoids of whole oil. Chemical Geology93, 13–20.Google ScholarCrossrefSearch ADS  Douglas S.Gregory, Stephen D.Emsbo-MattinglyScott A.Stout, Allen D.Uhler, Kevin J.McCarthy., 2007. Chemical Fingerprinting methods. Introduction to Environmental Forensics, 2nd Edition, 311–454Google Scholar Fadokun, D., SeleghaA., 2014. Characterizing oils using fingerprints and biomarkers: a case study on oils from two Niger Delta fields. NAPE Bulletin, Vol. 26 No 2 / 11.Google Scholar Fingas Merv2011. Oil Spill Science and Technology. Gulf Professional Publishing.Ekweozor, C.M., Okogun, J.I., Ekong, D.E.U., Maxwell, J.R., 1979a. Preliminary organic geochemical studies of samples from the Niger Delta (Nigeria): I. Analysis of crude oils for triterpanes. Chemical Geology27, 11–28Google ScholarCrossrefSearch ADS  RoushdyMI, El NadyMM, MostafaYM, El GendyNS, AliHR (2010) Biomarkers Characteristics of Crude Oils from some Oilfields in the Gulf of Suez. Egypt. Journal of American Science6(11):911–925Google Scholar Schlumberger, Oilfield glossary, 2022Ten Haven, H.L., 1996. Applications and limitations of Mangos light hydrocarbon parameters in petroleum correlation studies. Org. Geochem. 24, 957–976.Google ScholarCrossrefSearch ADS  Thompson, K.F.M., 1983. Classification and thermal history of petroleum based on light hydrocarbons. Geochim. Cosmochim. Acta47, 303–3Google ScholarCrossrefSearch ADS  Wilhelms, A., Larter, S.R., Hall, K., 1994. A comparative study of the stable carbon isotopic composition of crude oil alkanes and associated crude oil asphaltene pyrolysate alkanes. Organic Geochemistry21, 751–759.Google ScholarCrossrefSearch ADS  HalpernHenry, 1995. Development and Applications of Light Hydrocarbon-Based Star Diagrams. 10.1306/8D2B1BB0-171E-11D7-8645000102C1865DJO - AAPG BulletinGoogle Scholar Mango, F.D., 1994. The origin of light hydrocarbons in petroleum: ring preference in the closure of carbocyclic rings. Geochim. Cosmochim. Acta58, 895–901.Google ScholarCrossrefSearch ADS  Peters, K.E., Moldowan, J.M., 1993. The Biomarker Guide 2: Biomarkers and Isotopes in Petroleum Exploration and Earth History. Prentice-Hall, Englewood Cliffs, New JerseyGoogle Scholar ZhangShuichang,, Huang, Haiping, 2004. Geochemistry of Paleozoic marine petroleum from the Tarim Basin, NW China, Part 1. Oil family classification. 10.1016/j.orggeochem.2005.01.013JO - Organic GeochemistryGoogle Scholar 
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212002-MS
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files/2022/Flare Gas to Energy Using Hydrogen Fuel Cell Solid Oxide Fuel Cells The Nigerian Perspective.txt

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+----- METADATA START -----
+Title: Flare Gas to Energy Using Hydrogen Fuel Cell Solid Oxide Fuel Cells: The Nigerian Perspective
+Authors: Chinenye Ezechi, Chukwuemeka Ndulue
+Publication Date: August 2022
+Reference Link: https://doi.org/10.2118/212036-MS
+----- METADATA END -----
+
+
+
+Abstract
+
+
+Energy must be available and affordable to attain energy security as it is fundamental to human and economic development which drives virtually every aspect of the world economy. Globally, the energy demand is increasing; however, the increase is more significant for the African continent due to increased population, industrialization, and economic development. As a proactive measure, technology to meet the demand is crucial to find, develop, process and produce this energy.One of the challenges of the African continent is gas flaring due to gas management solutions and cost-related issues. Based on the African Energy Portal, (Africa Energy Portal, 2020) Nigeria loses about $2.5 billion yearly from 178 flare sites (predominantly onshore sites) nationwide, ranking Nigeria the 7th country on the list of the most flared gas countries in the world. While this is a massive loss in revenue for the major stakeholders, it is also detrimental to public health.While there are other complementary technologies available to help utilize more flared gas, the hydrogen fuel cell has proven to be more instrumental in the quest for acleaner and sustainable energy. With Europe as a frontline adopter, the sustainable energy benefits of a hydrogen fuel cell can be seen in its continuous development, deployment, and utilization in most of its countries. Africa, especially Nigeria has the potential to reduce gas flaring by 70%, (Africa Energy Portal, 2020) via the deployment and usage of fuel cells technologies in the conversion and utilization of flared gas to clean hydrogen gas, which can serve as alternative energy used in a wide range of applications across multiple sectors.This paper focuses on the impact of gas flaring, and the application of evolving hydrogen fuel cell technology as a means of flare gas reduction and gas recovery through steam reforming of methane. It also discusses the constraints of implementing hydrogen fuel cells in Africa, using Nigeria's Oil and Gas Sector as a case study.
+
+
+
+
+Keywords:
+upstream oil & gas,
+electricity,
+united states government,
+hydrogen,
+international energy agency,
+air emission,
+north america government,
+solid oxide fuel cell,
+reaction,
+sofc
+
+
+Subjects: 
+Environment,
+Air emissions
+
+
+
+
+Introduction
+
+
+Globally, supplying hydrogen to industrial users has become a substantial business. Hydrogen demand, which has increased by more than threefold since 1975, continues to rise. The annual demand for hydrogen in its pure form is estimated to be around 70 million tonnes (MtH2/yr). This hydrogen is virtually completely derived from fossil fuels, with hydrogen production accounting for 6% of global natural gas and 2% of global coal. As a result, the production of hydrogen results in CO2 emissions of around 830 million tonnes per year (MtCO2/yr), which is roughly equal to the CO2 emissions of Indonesia and the United Kingdom combined. In terms of energy, annual global hydrogen consumption is roughly 330 million tonnes of oil equivalent (Mtoe), which is higher than the primary energy supply. (International Energy Agency, 2019)
+
+
+Figure 1View largeDownload slideGlobal annual demand for hydrogen since 1975Figure 1View largeDownload slideGlobal annual demand for hydrogen since 1975 Close modal
+
+
+The use of hydrogen fuel cell technology to generate electricity by processing flared gases in a Steam-Methane Reformer (SMR) has primarily been used in the United States, Europe, and Asia; however, we have yet to fully exploit this potential in Africa. (International Energy Agency, 2019).
+
+
+One of the most widely used methods for producing hydrogen is steam methane reforming. Because of the high purity value, cost-effectiveness, and traditional technology required to produce hydrogen, the discussion on its effective use in a fuel cell has become possible. SMR is the reaction of hydrocarbons (natural gas) with steam to produce hydrogen for use as a fuel.
+
+
+Fuel cell research has progressed, with a focus on increased working efficiency, resulting in a variety of fuel cells of various sizes, types, and specifications. The choice of electrolytes, which determines the fuel cell components, remains a major deciding factor. The type of impurity tolerance in the fuel supplied and the temperature at which they operate distinguish these fuel cells. This in-depth examination of the use of flared gas in a fuel cell will aid industry decision-makers and policymakers in determining appropriate countermeasures to reduce gas flaring and increase power generation.
+
+
+Gas Flaring
+
+
+The extraction of crude oil is a precise and potentially dangerous operation. It involves precise technology combined with experience and expertise developed over decades. Part of the current safety regime in the industry involves the burning of the associated gas from crude oil production and this has been practicedfor over 160 years.
+
+
+Flare gas refers to the gases disposed of and flared into the atmosphere, the gas is predominantly hydrocarbon gases that could contain Sulphide and Carbon dioxides as contaminants, hence the gas is treated before being releasedinto the atmosphere.
+
+
+In Nigeria, a country with significant gas flaring and limited energy access, oil producers flare between 7 and 8 billion cubic meters of gas per year.(The World Bank, 2020).
+
+
+Figure 2View largeDownload slideGlobal Gas Flaring and Oil ProductionFigure 2View largeDownload slideGlobal Gas Flaring and Oil Production Close modal
+
+
+Figure 3View largeDownload slideFlare gas by top 10 countries in the worldFigure 3View largeDownload slideFlare gas by top 10 countries in the world Close modal
+
+
+The flaring of the gases is done in controlled combustion via a flare stack that is elevated above the ground and situated at a safe distance from surface facilities and personnel. An alternative to flaring would be venting which is just the release of the hydrocarbon into the atmosphere, however, the flaring of gas effectively destroys the methane and volatile organic carbons (VOCs) in the gas stream (Anon., n.d.)
+
+
+The flaring of these gases is done in line with certain reasons that have supported the continued flaring of gases:
+
+
+–Safety: to prevent blowout and pressure-related risk from oil production, gas flaring allows the pressure conditions to be controlled during production.–Environmental Health: the contaminant gases in Natural gas like sulphides are not environmentally friendly gases and one of the ways to properly dispose of the gases would be burning them.–Economic: the cost of getting the associated gas captured and delivered to market via gas utilization processes could be significant, especially in remote oilfields where the volume and consistency of the gas produced are not significant to ensure economic viability–Regulations: in countries where there is a penalty for the amount of gas flared, some operators find it more economically viable to flare the gas than the cost of capturing and selling the gas
+
+
+With the need to reduce the pollution due to gas flaring several steps have been taken by regulators and governments worldwide to reduce the amount of gas being flared including and not limited to – gas capturing, reducing routine flaring, the incentive to employ gas utilization technology, gas flaring fines, gas flare metering.
+
+
+Being a global practice, the gas flaring activities in Nigeria and Africa is in line with the industry, however, the efforts to reduce the amount of gas flared is not on par, as step such as gas flare metering is not enacted on all oil production sites as at 2021. However, there are regulations in place to ensure flare gas metering.
+
+
+In 2018, the flare gas regulation was approved with objectives to reduce the environmental and social impact of gas flaring, reduce waste etc. In 2016, the Nigerian Gas Flare Commercialization program was launched to help eliminate gas flaring through technically and commercially sustainable gas utilization projects
+
+
+Nigerian National Petroleum Corporation (NNPC) –announced a three-point strategy to control gas flaring by 2020. The plan involves: (Anon., n.d.)
+
+
+–Making new oil and gas field development plans ineligible if they do not have a viable plan to utilize gas–Steady reduction of existing flares through a combination of targeted policy interventions–Re-invigoration of the flare penalty and new legislation which places a ban on gas flaring via Flare Gas (Prevention of Waste and Pollution) Regulations
+
+
+Figure 4View largeDownload slideNigeria Flare volumes versus flare intensity from 2012 to 2021.Figure 4View largeDownload slideNigeria Flare volumes versus flare intensity from 2012 to 2021. Close modal
+
+
+Methodology
+
+
+With the massive gas flaring that has been discussed, steam methane reformation can be used to extract the hydrogen content of the flared gas, and electricity can be generated using a hydrogen fuel cell. This process will be extensively discussed in this chapter to demonstrate the availability of technologies to reduce gas flaring and improve electricity supply.
+
+
+Steam-Methane Reformer (SMR) Technology
+
+
+Hydrogen can be gotten from a variety of sources, biomass, water, and hydrocarbons (fossil fuels). However, the challenge remains that it is closely bonded to other elements. Steam methane reforming like electrolyzing is one of the most extensively used methods to produce hydrogen, it's the process of producing hydrogen for fuel by the reaction of hydrocarbons (natural gas) with steam. Steam methane reforming is performed in a conventional reformer where it is made to go through a reaction with steam in the presence of intense heat, this is followed by two-staged water gas shift (WGS) reactors (high and low temperature), and finally through a hydrogen purification compartment.
+
+
+The typical process flow of a steam methane reforming process is shown below.
+
+
+Figure 5View largeDownload slideTypical Steam Reformation Flow ChartFigure 5View largeDownload slideTypical Steam Reformation Flow Chart Close modal
+
+
+In the steam methane reforming chamber, methane is made to react with high-temperature steam (700°C – 1000°C) under a pressure between 3 - 25 bar in the presence of a Ni-based catalyst to produce hydrogen and carbon monoxide. Basile et al. (2015). This reaction is largely endothermic, that is, heat must be applied to the process for the reaction to be complete.
+
+
+The below equations depict the chemical reactions that take place in the SMR chamber, and the amount of energy needed in the reaction.
+
+
+CH4+H2O+(Heat)⇌CO+3H2 ΔHSR=206  kJ/molCH4+2H2O⇌CO2+4H2 ΔHDSR=165 kJ/mol
+
+
+As seen in the above reaction, the product gives carbon monoxide which is relatively present in high quantity. Therefore, to eliminate this, a shift conversion chamber is introduced.
+
+
+CO+H2O⇌CO2+H2+(Heat) ΔHWGSR=−41  kJ/mol
+
+
+This helps to further break down the carbon monoxide and limits the number of toxins that are exposed to the environment and further generates more hydrogen for economic use.
+
+
+Pressure swing adsorption is a commonly used technique that helps generates high hydrogen purity. Pressure swing adsorption uses the principle of selective adsorption of impurities from the gas stream, this produces 70 – 85% of hydrogen from the stream with 99.99% purity. Basile et al. (2015).
+
+
+Fuel Cell Technology
+
+
+Just like any electrochemical cell, a fuel cell consists of an electrolytic membrane, anode, and cathode. During the operation of a typical fuel cell, hydrogen gas is fed from the reformer passing through the fuel cell's anode while oxygen goes in through the cathode. The catalyst utilized in the cell splits the hydrogen molecules at the anode into electrons and protons, the protons pass through the porous electrolyte membrane, while the electrons are forced through an electric circuit which leads to electric current generation and release of heat. After generating electric currents, the electrons meet the protons and oxygen at the cathode to generate water molecules as a by-product, the cycles go on till the fuel source is isolated.
+
+
+Figure 6View largeDownload slideTypical Reformer and Hydrogen Fuel Cell set-upFigure 6View largeDownload slideTypical Reformer and Hydrogen Fuel Cell set-up Close modal
+
+
+The basic chemical reaction is shown below.
+
+
+2H2+O2→2H2O+2e−
+
+
+Considering the operating and chemical philosophy, fuel cells are considered clean and carbon-free as their only byproducts are electricity, heat, and water. Fuel cells are expandable, they can be combined in stacks to form a larger system. There are currently different types of fuel cells varying in different sizes, types, and specifications. Presently, the key types of fuel cells are alkali, molten carbonate, phosphoric acid, proton exchange membrane, and solid oxide fuel cells.
+
+
+Each type of fuel cell has advantages and drawbacks compared to the others as shown below.
+
+
+Table 1Comparison Table for Fuel Cell Technologies Fuel Cell Type
+            . Operating Temperature
+            . Power
+            . Efficiency
+            . Applications
+            . Advantages
+            . Challenges
+            . Solid Oxide Fuel Cell (SOFC) 500 - 1000°C 1 kW - 2 MW 60% Auxiliary powerElectric utilityDistributed generation Hybrid/gas turbine cycleHigh efficiencyFuel flexibilitySolid electrolyteSuitable for CHP Corrosion and failure of cell components at high temperaturesLong start-up timeLimited number of shutdowns Alkaline Fuel Cell (AFC) <100°C 1 - 100 kW 60% MilitarySpaceBackup powerTransportation Wider range of stable materials allows lower-cost componentsLow temperatureQuick start-up Electrolyte management (aqueous)Electrolyte conductivityCO2 sensitivity in fuel and air (polymer) Proton Exchange Membrane Fuel Cell (PEM) <120°C <1 kW - 100 kW 60% direct H2; 40% reformed fuel Power source (backup)Power source (portable)Generation that is dispersedModes of transportationVehicles with specialized capabilities Solid electrolyte helps with corrosion and electrolyte control. Quick start-up and load followingLow temperature Sensitive to fuel impuritiesExpensive catalysts Phosphoric Acid Fuel Cell (PAFC) 150 - 200°C 5 - 400 kW, 100 kW module (liquid PAFC); <10 kW (polymer membrane) 40% Distributed generation Suitable for CHPIncreased tolerance to fuel impurities Expensive catalystsLong start-up timeSulfur sensitivity Molten Carbonate Fuel Cell (MCFC) 600 - 700°C 300 kW - 3 MW, 300 kW module 50% Electric utilityDistributed generation High efficiencyFuel flexibilitySuitable for CHPHybrid/gas turbine cycle High-temperature corrosion and breakdown of cell componentsLong start-up timeLow power density Fuel Cell Type
+            . Operating Temperature
+            . Power
+            . Efficiency
+            . Applications
+            . Advantages
+            . Challenges
+            . Solid Oxide Fuel Cell (SOFC) 500 - 1000°C 1 kW - 2 MW 60% Auxiliary powerElectric utilityDistributed generation Hybrid/gas turbine cycleHigh efficiencyFuel flexibilitySolid electrolyteSuitable for CHP Corrosion and failure of cell components at high temperaturesLong start-up timeLimited number of shutdowns Alkaline Fuel Cell (AFC) <100°C 1 - 100 kW 60% MilitarySpaceBackup powerTransportation Wider range of stable materials allows lower-cost componentsLow temperatureQuick start-up Electrolyte management (aqueous)Electrolyte conductivityCO2 sensitivity in fuel and air (polymer) Proton Exchange Membrane Fuel Cell (PEM) <120°C <1 kW - 100 kW 60% direct H2; 40% reformed fuel Power source (backup)Power source (portable)Generation that is dispersedModes of transportationVehicles with specialized capabilities Solid electrolyte helps with corrosion and electrolyte control. Quick start-up and load followingLow temperature Sensitive to fuel impuritiesExpensive catalysts Phosphoric Acid Fuel Cell (PAFC) 150 - 200°C 5 - 400 kW, 100 kW module (liquid PAFC); <10 kW (polymer membrane) 40% Distributed generation Suitable for CHPIncreased tolerance to fuel impurities Expensive catalystsLong start-up timeSulfur sensitivity Molten Carbonate Fuel Cell (MCFC) 600 - 700°C 300 kW - 3 MW, 300 kW module 50% Electric utilityDistributed generation High efficiencyFuel flexibilitySuitable for CHPHybrid/gas turbine cycle High-temperature corrosion and breakdown of cell componentsLong start-up timeLow power density Source: Felseghi, R.A. et al. (2019) View Large
+
+
+Solid Oxide Fuel Cell (SOFC)
+
+
+Solid oxide fuel cells amongst others are becoming a more appealing potential option as they promise to help people avoid pollution while also providing a clean and efficient power supply directly from flared gas. SOFC converts chemical energy to electrical energy directly from various gaseous fuels – hydrogen and hydrocarbons - via numerous electrochemical reactions with very low environmental emissions. This hydrogen gas can be produced from natural gas by internal or external steam reforming.
+
+
+SOFCs are composed of four layers, namely, anode, electrolyte, cathode, andinterconnect. Three of the four segments (anode, electrolyte, and cathode) are made of ceramics or solid materials, while the fourth (interconnect) is made of metal.
+
+
+The cathode is filled with air, and the cathode is filled with fuel. At the cathode, oxygen in the air is reduced by absorbing electrons and forming Oxygen gas. These oxygen ions diffuse to the anode via the electrolyte. As a result, when the fuels enter the anode, they absorb oxygen while releasing heat, water, and two electrons.These two electrons will travel to an external circuit to generate power before returning to the cathode. To generate electricity, these cycles will be repeated indefinitely, completing the SOFC circuit. (Afroze et al., 2020). Solid oxide fuel cells (SOFC) operate at a very high temperature, around 1000°C when compared to other fuel cells. This fuel cell has a 60 per cent efficiency, which is significantly higher than the current combustion engines used to generate electricity. With a power output of up to 2MW, it's an excellent choice for a modular power solution.The factors that distinguish this fuel cell from others are shown in the comparative table above.
+
+
+The schematics and working principles of a simple SOFC are depicted in the diagram below.
+
+
+Figure 7View largeDownload slideSchematic diagram of solid oxide fuel cellSource: Yadav and Singh, (2015) Figure 7View largeDownload slideSchematic diagram of solid oxide fuel cellSource: Yadav and Singh, (2015)  Close modal
+
+
+Case Study on Solid Oxide Fuel Cell (SOFC)
+
+
+Themodeling of integration of SOFCs into a flare system at a 2 billion standard cubic feet (bscf)natural gas processing plantfor both off-shore andon-shore applications by the Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar shows a huge potential in solving the menace of flared gases.(Khalid A, et al 2021). The economic assessment, amount of power generated,the safety of the gas plant during operation and estimation of carbon dioxide equivalents achieved were also studied in the modeling.
+
+
+The study successfully modeled the integration of the flare system and the SOFC system to generate electricity using waste inert gas as a fuel in the SOFC system using on-shore and off-shore inert gas. Their goal was to recover as much on-shore flare gas as possible while also identifying bottlenecks in the system and re-modelling the ultimate flare gas recovery calculation model to address those bottlenecks. As a result, the design of the SOFC system was based on the flow of flare gas during normal plant operation.
+
+
+Figure 8View largeDownload slideSchematic diagram of solid oxide fuel cell with a flare systemFigure 8View largeDownload slideSchematic diagram of solid oxide fuel cell with a flare system Close modal
+
+
+From the model, all of the parameters of SOFCs were calculated and identified utilizing the mathematical setup that had been constructed (Steward et al., 2013). The engineering equation solver software was used to confirm the results. All the equations (Chemical equations, power equations, and energy balance calculations) used were from the fuel cell handbook, in addition to numerous sources (Colpan et al., 2007; Duan et al., 2011; Kabza, 2015; Pianko-oprych and Hosseini, 2017; Semelsberger and Borup, 2004). (Edition and Virginia, 2004).
+
+
+They recovered flare gas from all flare headers after the flare knock-out drums and before flare stacks on day-to-day plant on-shore and off-shore operations. The SOFC system generates electricity, and the exhaust gas from the SOFC is returned to the flare stack. In the event of an emergency, any inconveniences that result in a higher flare gas flow will skip the SOFC system, and flare gas will be sent to the flare stack according to the original design while meeting all safety requirements.After the modeling, the result shows that 20 megawatts of electricity were generated for the onshore plant after feeding the SOFC with 70% of flared gas and fueling with 3.5 million standard cubic feet(MMSCF) of inert gas, thus causing a significant reduction of CO2 equivalent per day from 263 tons to 101 tons. On the other hand, the offshore plant generates 600 kilowatts of electricity with the same amount of flare gas fueled with 105 thousand standard cubic feet (MSCF)of inert gas causing a reduction of CO2 equivalent per day from 9 tons to 3 tons.(Khalid A, et al 2021).
+
+
+The economic analysis of SOFC integration proves that USD 145 million investment will provide a positive net present value of USD 30 million and an internal rate of return of around 6% over 25 yearsindicating that system improvement is conceivable.Also, the financial outcomes with the flare system, as it is well known that the fuel is provided free of charge in this scenario. All economic criteria, such as (Net Present Value) NPV, (Internal rate of return) IRR, and (Return of investment) ROI, reveal that both locations are profitable. The Levelized cost of electricity(LCOE) is an important factor. For onshore operations, the LCOE is 0.03 USD/kWh, which is 50% less than the current pricing in Qatar. Similarly, the LCOE for off-shore operations is 0.20 USD/kWh, which is 50% less than the cost of power generated by off-shore diesel generators, which is around 0.41 USD/kWh. This costcovers the cost of diesel, as well as the cost of shipment and maintenance of the diesel generator. (Khalid A, et al 2021)
+
+
+Figure 9View largeDownload slideSchematic diagram of Onshore and offshore flaringFigure 9View largeDownload slideSchematic diagram of Onshore and offshore flaring Close modal
+
+
+Apart from the numerous fuel cell types and applications mentioned, the aforementioned case study on SOFC emphasizes the importance of the discussed solution in eliminating waste gas flaring and converting flared gas to electricity.
+
+
+Global Data Discussion
+
+
+Insights from the Global Use of the Fuel cell
+
+
+Figure 10View largeDownload slideShowing total Shipments by fuel cell type Source: Felseghi, R.A. et al. (2019) Figure 10View largeDownload slideShowing total Shipments by fuel cell type Source: Felseghi, R.A. et al. (2019)  Close modal
+
+
+According to figure 7, the number of units shipped for use in SOFC technology around the world increased significantly in 2016. Around 2700 units were reported in 2014; by 2018, there had been a significant increase, with 27,800 SOFC units shipped for use. In addition, there has been a significant increase in the supply of various types of fuel cells around the world, indicating the potential for the development and expansion of clean energy through hydrogen.
+
+
+Figure 11View largeDownload slideShowing total fuel cell installed capacity Source: Felseghi, R.A. et al. (2019) Figure 11View largeDownload slideShowing total fuel cell installed capacity Source: Felseghi, R.A. et al. (2019)  Close modal
+
+
+The graph depicts the global distribution of total megawatts generated by hydrogen fuel cells from 2014 to 2018, as reported. The trend shows that hydrogen fuel cells are gaining a lot of traction around the world. These units are designed to be able to replace the grid in areas where access to the grid and grid infrastructure is limited.
+
+
+Figure 12View largeDownload slideShowing a forecasted fuel cell growthFigure 12View largeDownload slideShowing a forecasted fuel cell growth Close modal
+
+
+The above graph was modelled using the growth rate of the last three years, based on data from the graph depicting fuel cell installed capacity. The forecasted growth of fuel cells in those years is expected to be progressive, assuming continued green energy activities. Even though Africa, which is classified as "Rest," is experiencing slow growth, the region's growth rate has a good chance of speeding up if the constraints identified in this paper are eventually explored and prioritized.
+
+
+Benefits of an Hydrogen Economy
+
+
+The hydrogen economy for some countries looks innumerable, while for some it continues to maintain and strengthen its mandate for energy security and active contribution to the world's fight against global warming. The benefits of the hydrogen economy can be broadly classified into three pillars, namely: environmental sustainability, energy security, and economic stimulation.
+
+
+Environmental Sustainability
+
+
+It is estimated that over the years, the planet's average surface temperature has increased by about 2 degrees Fahrenheit, a change which is largely due to increased carbon dioxide emissions in the atmosphere and other human activities on the planet. Emissions of greenhouse gases, such as carbon dioxide and methane, are considered a major global concern. Hydrogen fuel cells produce no harmful emissions, eliminating the costs associated with handling and storing hazardous materials such as battery acids or diesel fuel. When fueled with pure hydrogen, the only byproducts are heat and water, making our products a sustainable, zero-emission energy source. Efficient hydrogen production technologies and carbon capture and sequestration would make hydrogen from natural gas viable feedstock options, even in low-carbon environments.
+
+
+Energy Security
+
+
+For energy to be considered safe, it must be readily available and usable. Hydrogen is the most abundant element on earth, making it readily available for use through various extraction methods. For Africa, this would be a means of energy diversification as it has the largest non-energy population. This can help consolidate low-income economies into the world's high-income economies. Increasing energy independence will benefit many countries that are currently dependent on fossil fuel supplies, and it will solve the problem of increasing fossil fuel prices as supplies dry up. The production, storage, and use of hydrogen will be critical in the continued development of renewables, balancing their intermittent supply mode with stringent end-user demands and avoiding the need for significant network infrastructure modernization.
+
+
+Economic Stimulation
+
+
+Hydrogen presents a one-of-a-kind opportunity to dramatically improve the efficiency with which we produce and consume energy. We can also reduce the impact of external factors on energy prices because it can be produced from a variety of domestically available resources. New businesses will be sparked by the economic and technical success of hydrogen-based distributed energy systems. The development of hydrogen fuel cells will open new economic opportunities for the integrated production of energy services like electricity, transportation fuel, and cooking. When compared to batteries, hydrogen as a fuel has the advantage of a longer run time and faster recharging. Hydrogen has a lot of potential for spurring new economic growth in rural and urban areas that are currently too far away to attract investmentand have access to the national grid.
+
+
+Constraints to the Implementation of Hydrogen Fuel Cells in Africa
+
+
+As seen in this paper, there are a lot of advantages of switching to hydrogen for electrification and transportation. However, there poses a lot of challenges for this transition to be effectively achieved. The constraints can be grouped into the following.
+
+
+Technology and Infrastructure
+
+
+Infrastructure to extract, refine, transport, distribute, and store hydrogen is needed for hydrogen to be used with fuel cells, but this is lacking because the primary focus is on fossil fuels. Countries in Africa with abundant natural gas, such as Nigeria, have recently invested in natural gas networks; however, for hydrogen to be used, envisioned technology for the seamless introduction of hydrogen into existing transmission and distribution natural gas networks must be in place. Unfortunately, this is not the case.
+
+
+Policies and Government Support
+
+
+Green technologies would thrive largely as a result of government support and policies enacted to encourage their development. However, due to the slow growth and adoption of hydrogen technology in Africa, as well as the need for African countries to accelerate their economic growth, the use of flared gas has received less attention. Policies and initiatives to encourage hydrogen development and fuel cell adoption should be implemented, as this will help to attract investment and grow the economy indirectly.
+
+
+Initial High Cost
+
+
+As it is with new development in technologies, fuel cell technology adoption in Africa would be faced with high production costs of hydrogen, high production costs of the fuel cell, high initial investment installation costs, high production costs of systems based on hydrogen fuel cell technologies, the high price of energy generated by hydrogen-based energy systems, high costs for hydrogen storage and, high costs for adaptation of the hydrogen economy.
+
+
+Conclusion and Recommendation
+
+
+This paper has gone into detail about the issues of natural gas flaring in Africa. It has also been demonstrated that, despite Africa's abundant natural resources, we still suffer from under-electrification and low economic growth. As a result, this paper has dissected the possibilities of steam reforming to produce hydrogen and its use in a fuel cell to generate electricity. The benefits and drawbacks of this technology have also been investigated, revealing that much work remains to be done to bring Africa up to speed with other nations in the pursuit of a hydrogen economy.
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+Fuel cell technology is in an advanced stage of development, as shown, and has already been developed around the world. Significant total energy bill reductions can be gained if SOFCs and flares are integrated into all gas plants in Nigeria while maintaining safety measures. It also reduces electricity imports from the national grid, minimizing grid dependency and emissions from these facilities.Finally, widespread adoption of SOFC technology in a variety of oil/gas industry applications is expected to improve system efficiency and result in significant emissions reductions.
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+This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
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+Copyright 2022, Society of Petroleum Engineers DOI 10.2118/212036-MS
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