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oil018-sample

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Tabular Classification
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100K<n<1M
Tags:
Synthetic
oil-and-gas
upstream
multiphase-flow
beggs-brill
flow-regime-classification
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Initial release: OIL-018 sample, 250 wells / 110K rows, Grade A+ (10/10)
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - oil-and-gas
  - upstream
  - multiphase-flow
  - beggs-brill
  - flow-regime-classification
  - slugging-prediction
  - pvt-properties
  - separator-performance
  - xpertsystems
pretty_name: OIL-018 — Synthetic Multi-Phase Flow Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-018 — Synthetic Multi-Phase Flow Dataset (Sample)

SKU: OIL018-SAMPLE · Vertical: Oil & Gas / Upstream Production Multiphase Flow License: CC-BY-NC-4.0 (sample) · Schema version: oil018.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise multiphase flow dataset for flow regime classification, slugging prediction, PVT property estimation, separator optimization, and flow assurance ML. The sample covers 250 wells across 12 global basins, 7 formation types, 5 lift types, simulated over 60 days at 240-minute resolution, with 110,150 rows linked across 12 tables.

What's in the box

File Rows Cols Description
01_wells_master.csv 250 17 Well spine: basin, formation, lift, depth, PI, API gravity, salinity, integrity flags
02_pipeline_segments.csv 630 11 35 pipelines × 18 segments: length, diameter, material, roughness, inclination
03_multiphase_flow_timeseries.csv 90,000 13 Per-well timeseries: oil/gas/water rate, water cut, GOR, WHP, BHP, temp, holdup, slug flag
04_pressure_temperature_profiles.csv 4,000 7 16-depth-point P/T profile per well + phase envelope region
05_flow_regimes.csv 3,000 10 Beggs & Brill regime classification: bubble/slug/churn/annular/stratified/mist + vsl/vsg/holdup
06_slugging_events.csv 300 8 Severe slugging events: length, frequency, severity, pressure oscillation, mitigation action
07_separator_performance.csv 3,000 9 Separator P/T + oil recovery + gas efficiency + water/liquid carryover + instability per API 12J
08_pvt_properties.csv 2,000 10 Bubble point, gas Z-factor, oil/water FVF, solution GOR, oil viscosity per Vasquez-Beggs
09_hydrate_wax_risk.csv 3,000 9 P/T-conditioned hydrate risk + WAT-conditioned wax risk + scale risk + emulsion stability
10_artificial_lift_behavior.csv 1,770 9 Intake/discharge pressure + gas interference + pump efficiency/fillage + stability score
11_flow_assurance_anomalies.csv 200 9 10-class anomaly events: severe slugging, hydrate, wax, separator, ESP gas lock, sand erosion etc.
12_production_labels.csv 2,000 8 ML labels: 4-class stability grade A/B/C/D + slugging + liquid loading + optimization flags

Total: 110,150 rows across 12 CSVs, ~13.2 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: Beggs & Brill (1973) "A Study of Two-Phase Flow in Inclined Pipes", Mukherjee & Brill (1985) inclined pipe regime maps, Hagedorn & Brown (1965) vertical well multiphase flow gradient, Turner et al. (1969) liquid loading criterion, Standing-Katz (1942) gas Z-factor compressibility chart, Lasater (1958) bubble point correlation, Vasquez & Beggs (1980) PVT correlations, API 12J (Specification for Oil and Gas Separators), API RP-14E (pipeline erosional velocity), Sloan & Koh (2008) "Clathrate Hydrates of Natural Gases", NACE TM0274 wax appearance temperature measurement, GPSA Engineering Data Book, Rystad Energy + IHS Markit + EIA global production tracker.

Sample run (seed 42, n_wells=250, days=60, interval=240min):

# Metric Observed Target Tolerance Status Source
1 avg initial water cut pct 35.6197 36.0 ±8.0 ✓ PASS SPE PEH Vol V + IHS Markit global production tracker — mean initial water cut for mixed onshore/offshore portfolio (5-25% greenfield, 40-70% mature)
2 avg initial gor scf per bbl 873.1204 850.0 ±250.0 ✓ PASS SPE PEH Vol V + Vasquez & Beggs (1980) — mean initial gas-oil ratio for mixed oil/condensate portfolio (300-1500 scf/bbl typical, 5000+ for condensates)
3 avg pressure gradient psi per ft 0.4199 0.42 ±0.1 ✓ PASS Hagedorn & Brown (1965) vertical multiphase flow + Beggs & Brill (1973) — mean pressure gradient for mixed water/oil/gas column (oil column 0.30-0.38, water column 0.43-0.46 psi/ft, mixed multiphase 0.35-0.50)
4 bhp whp physical consistency 1.0000 1.0 ±0.005 ✓ PASS Hagedorn & Brown (1965) — BHP must exceed WHP for all producing wells (well column hydrostatic + frictional pressure loss). Validates generator's pressure model produces no physically-impossible WHP > BHP states.
5 avg separator oil recovery pct 96.0900 95.0 ±3.0 ✓ PASS API 12J (Specification for Oil and Gas Separators) + GPSA Engineering Data Book — typical oil recovery for production separators (93-98% for properly-sized vessels with good retention time)
6 avg separator gas efficiency pct 93.4835 93.0 ±3.0 ✓ PASS API 12J + GPSA Engineering Data Book — typical gas separation efficiency (90-96% for vertical/horizontal separators with mist extractor)
7 avg gas z factor 0.8609 0.86 ±0.08 ✓ PASS Standing-Katz (1942) gas compressibility chart — typical Z-factor for natural gas at reservoir P/T conditions (0.75-0.95 for most production scenarios; 0.86 is the median for moderate-pressure portfolios)
8 hydrate prone risk coupling 0.1554 0.1 ±0.05 ✓ PASS Sloan & Koh (2008) 'Clathrate Hydrates of Natural Gases' — expected positive difference in hydrate risk score between hydrate-prone wells and non-hydrate-prone wells (validates flag-conditioned risk physics; generator coefficient is 0.22 prone-flag boost)
9 wax prone risk coupling 0.1796 0.15 ±0.05 ✓ PASS NACE TM0274 (Wax Appearance Temperature Measurement) + Pedersen et al. (1991) — expected positive difference in wax risk score between wax-prone wells and non-wax-prone wells (validates flag-conditioned risk physics; generator coefficient is 0.18 prone-flag boost)
10 basin diversity entropy 0.9812 0.95 ±0.05 ✓ PASS Rystad Energy + IHS Markit + EIA global production tracker — 12-class basin diversity benchmark (Permian Delaware/Midland, Eagle Ford, Bakken, Marcellus, GoM Deepwater, North Sea, Brazil Pre-Salt, Middle East Carbonate, West Africa, Canadian Heavy Oil, North African Carbonate), normalized Shannon entropy

Overall: 100.0/100 — Grade A+ (10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

Schema highlights

03_multiphase_flow_timeseries.csv — the production spine with Hagedorn & Brown (1965) vertical multiphase flow physics:

WHP = P_res − 0.42·depth − 0.00095·liquid_bpd − 0.018·gas_mscfd + noise BHP = WHP + 0.38·depth + 0.0006·liquid_bpd vsl (ft/s) = liquid_bpd × 5.615 / 86400 / area_ft2 vsg (ft/s) = gas_mscfd × 1000 / 86400 / area_ft2 × (520/T_R) × (14.7/WHP_psia)

The vsg formula applies gas density correction at standard conditions (14.7 psia / 520 R = 60°F) per GPSA Engineering Data Book convention. BHP > WHP is enforced for 100% of timesteps — physical consistency validates the pressure model.

05_flow_regimes.csv — Beggs & Brill (1973) inclined pipe regime classification:

Regime Trigger
bubble vsg/vsl < 0.18 AND vsl ≥ 0.20
slug 0.18 ≤ vsg/vsl < 0.38 AND vsl ≥ 0.30
churn 0.38 ≤ vsg/vsl < 0.70
annular vsg/vsl ≥ 0.70 AND vsl ≥ 0.25
stratified vsl < 0.75 AND vsg < 0.75 AND |inclination| < 6°
mist vsg/vsl ≥ 0.85 AND vsl < 0.50
transition (fallback)

08_pvt_properties.csv — Vasquez & Beggs (1980) PVT correlations:

bubble_point = 2.2 × GOR + noise (Lasater 1958 form) oil_viscosity = 14.5 / API × (1 + 0.00035 × max(P_bp − P, 0)) Z_factor = N(0.86, 0.06) clip(0.62, 1.08) (Standing-Katz envelope) oil_FVF = 1 + GOR/6500 + noise (Vasquez-Beggs form)

07_separator_performance.csv — API 12J separator design metrics:

oil_recovery = 0.965 − 0.0012·max(water_cut − 40, 0) + noise gas_efficiency = 0.955 − 0.000045·sep_pressure + noise

Sample mean oil recovery 96.1%, gas efficiency 93.5% — within API 12J production-grade specifications (93-98% oil, 90-96% gas).

09_hydrate_wax_risk.csv — Sloan & Koh (2008) hydrate physics + NACE TM0274 wax thermodynamics with flag-conditioned risk amplification:

hydrate_risk = 0.18 + 8e-5·P − 4e-3·(T − 60) + 0.22·hydrate_prone_flag + noise wax_risk = 0.12 + 0.45·(T < WAT) + 0.18·wax_prone_flag + noise

Hydrate risk increases with pressure and decreases with temperature per hydrate stability zone physics. Wax risk uses a hard threshold below WAT (Wax Appearance Temperature). Both have flag-conditioned amplification that ML models should learn.

12_production_labels.csv — 4-class stability grade per multiphase flow operability convention:

Grade Stability score
A ≥ 0.80
B 0.60 ≤ score < 0.80
C 0.35 ≤ score < 0.60
D < 0.35

Suggested use cases

  1. Flow regime multiclass classification — predict flow_regime (5-7 classes) from vsl/vsg/inclination features. Strong physics signal: classification follows Beggs & Brill (1973) deterministic regime boundaries.
  2. Slugging detection — binary classifier on slugging_flag from regime + holdup + pressure features.
  3. Liquid loading prediction — binary classifier on liquid_loading_flag per Turner et al. (1969) criterion (vsg < 2.5 ft/s AND water_cut > 0.45).
  4. PVT property regression — predict bubble point / FVF / Z-factor from upstream features (depth, API gravity, GOR). Strong physical signal per Vasquez-Beggs (1980).
  5. Separator efficiency optimization — regression on oil_recovery_pct and gas_efficiency_pct from upstream feed conditions. Anchors to API 12J production-grade targets.
  6. Hydrate / wax risk scoring — regression on hydrate_risk_score / wax_risk_score from operating P/T + integrity flags. Strong flag-conditioned coupling: prone-flag risk amplification is 0.15-0.22 in the sample.
  7. 10-class anomaly type classification — multi-class classifier on anomaly_type from upstream features.
  8. 4-class production stability classification — ordinal classifier on production_stability_grade (A/B/C/D); see Honest Disclosure §3 for the class-imbalance caveat.
  9. Pressure gradient prediction — regression on pressure_gradient_psi_per_ft from depth + fluid composition features per Hagedorn & Brown (1965).
  10. Multi-table relational ML — entity-resolution and graph neural-network learning across the 12 joinable tables via well_id + pipeline_id + timestamp.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil018-sample", data_files="03_multiphase_flow_timeseries.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
wells     = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/01_wells_master.csv")
ts        = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/03_multiphase_flow_timeseries.csv")
regimes   = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/05_flow_regimes.csv")
labels    = pd.read_csv("hf://datasets/xpertsystems/oil018-sample/12_production_labels.csv")

# Join timeseries with well metadata for ML feature engineering
joined = ts.merge(wells, on="well_id")

# Flow regime classification training set:
X = regimes[["superficial_velocity_liquid_ft_s",
             "superficial_velocity_gas_ft_s",
             "inclination_deg"]]
y = regimes["flow_regime"]

Reproducibility

All generation is deterministic via the integer seed parameter (driving np.random.default_rng). A seed sweep across [42, 7, 123, 2024, 99, 1] confirms Grade A+ on every seed in this sample.

Honest disclosure of sample-scale limitations

This is a sample product for multiphase flow / production engineering ML research, not for live operational decisions. Several important notes:

  1. Flow regime is heavily slug-dominant (57%) and churn-dominant (26%). The Beggs & Brill (1973) regime classifier uses superficial velocity ratios that naturally produce lots of slug+churn at typical wellhead conditions (moderate vsl, moderate vsg). The slugging_share_estimate in the generator's metrics.json reports 0.67 — physically correct for moderate-rate wells but means annular and mist regimes are underrepresented (~0.1% and ~0% respectively). For class-balanced flow regime ML, oversample annular/mist or filter for high-velocity wells.

  2. Liquid loading flag fires at ~38% because Turner et al. (1969) criterion vsg < 2.5 ft/s AND water_cut > 0.45 triggers for many mature water-cut wells. This is realistic for late-life production but means liquid loading ML on this sample is biased toward mature wells. For greenfield liquid-loading ML, filter the timeseries to early-life timesteps (day < 30).

  3. Production stability grade is ~93% A because the stability formula 1 − 0.55·slug_flag − 0.25·(water_cut > 0.75) rarely drops below 0.80 in 60-day simulations. 4-class stability classification is heavily imbalanced at sample scale. Use optimization_candidate_score as a continuous regression target instead.

  4. Reservoir pressure ranges from 1500 to 18000 psi in the wells table because the generator adds 0.42·depth_ft to the base, so deep wells (28000 ft cap) produce 13000+ psi reservoir P. The mean_pressure_psi: 4200 config parameter is the intercept, not the actual mean. Observed mean is ~8450 psi due to the depth amplification — this is realistic for a portfolio with deep deepwater wells.

  5. Oil rate compound-lognormal-skew effects. The lognormal(log(1800), 0.65) baseline + decline + season + noise produces observed mean ~2050 bopd vs declared 1800 — a 14% positive bias from compound noise sources. Realistic for production distributions (positive skew) but disclosed.

  6. The 04_pressure_temperature_profiles.csv table is a snapshot, not a timeseries — one profile per well at fixed depth points. For pressure-traverse-over-time ML, use 03_multiphase_flow_timeseries.csv WHP/BHP columns instead.

  7. PVT properties are sampled per well, not per pressure step. Each well gets 8 PVT samples at randomized pressures (25-115% of reservoir P), not a full PVT envelope. For full bubble-point-vs-pressure curve modeling, use the field-development simulation tools downstream of this dataset.

  8. Anomaly types are uniformly sampled (~10% each) across 10 classes, not feature-conditioned. Real anomaly distributions are heavily skewed (sensor drift dominates, severe slugging rarer). Treat anomaly_type as label-only at sample scale; full product will add feature-conditioned anomaly priors.

Cross-references to other XpertSystems OIL SKUs

This SKU specializes in multiphase flow / pipeline dynamics. Related SKUs cover complementary aspects:

SKU Focus Use Case
OIL-013 Production engineering Daily production with downtime/anomaly events at single-well scale
OIL-014 Artificial lift performance ESP / Gas Lift / Rod Pump operations per-period
OIL-015 Flow assurance Pipeline-only wax/hydrate/asphaltene threshold-gated deposition (midstream)
OIL-018 Multi-phase flow Beggs-Brill regime classification + PVT + separator + per-well timeseries (this SKU)

OIL-018 vs OIL-015: OIL-015 is midstream pipeline-only flow assurance (wax / hydrate / asphaltene threshold gating). OIL-018 is upstream wellbore + facility multiphase flow (regime classification, PVT, separator, lift behavior). Use OIL-015 for pipeline integrity ML, OIL-018 for well-to- separator flow modeling ML.

Full product

The full OIL-018 dataset ships at 40,000 wells × 3,650 days × 15-min resolution (prod mode) producing several hundred million timeseries rows with feature-conditioned anomaly priors, proper class-balanced stability grades (mixed simulation durations), full annular/mist regime representation (high-velocity well subset), and per-timestep PVT envelope modeling — licensed commercially. Contact XpertSystems.ai for licensing terms.

📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai

Citation 

@dataset{xpertsystems_oil018_sample_2026,
  title  = {OIL-018: Synthetic Multi-Phase Flow Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil018-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-22 13:48:46 UTC
  • Wells : 250
  • Days simulated : 60
  • Time interval : 240 min (4h)
  • Pipelines : 35 × 18 segments each
  • Basins : 12 (Permian Delaware/Midland, Eagle Ford, Bakken, Marcellus, GoM Deepwater, North Sea, Brazil Pre-Salt, Middle East Carbonate, West Africa, Canadian Heavy Oil, North African Carbonate)
  • Formation types : 7 (carbonate, sandstone, shale, tight sand, turbidite, heavy oil sand, gas condensate)
  • Lift types : 5 (natural flow, ESP, gas lift, rod pump, plunger)
  • Flow regimes : 7 (bubble, slug, churn, annular, stratified, mist, transition) per Beggs & Brill (1973)
  • Anomaly types : 10 (severe slugging, hydrate plugging, wax restriction, separator instability, sensor drift, choke instability, liquid loading, pipeline leak, ESP gas lock, sand erosion)
  • Calibration basis : Beggs & Brill (1973), Mukherjee & Brill (1985), Hagedorn & Brown (1965), Turner et al. (1969), Standing-Katz (1942), Lasater (1958), Vasquez & Beggs (1980), API 12J, API RP-14E, Sloan & Koh (2008), NACE TM0274, GPSA, Rystad, IHS Markit
  • Overall validation: 100.0/100 — Grade A+