Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
dense
Generated from Trainer
dataset_size:44838
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Sampath1987/EnergyEmbed-nv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Sampath1987/EnergyEmbed-nv1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sampath1987/EnergyEmbed-nv1", trust_remote_code=True) sentences = [ "How does the volume and flow rate of cement affect the cementing process in oil and gas wells?", "Overview of International Offshore Decommissioning Regulations: Volume 1 – Facilities \nThe Petroleum Code does not make any specific requirements in relation to whether\noffshore facilities need to be removed following cessation of production. However, as a\nsignatory to UNCLOS III/IMO and the Abidjan Convention, the Republic of Guinea is bound\nby these international and regional agreements. \nThe Environment Code is enforced by the Ministry of Natural Resources, Energy and\nEnvironment. Its key aims are to protect the environment while promoting the use of\nnatural resources. Title 2/Chapter III of the Environment Code deals with maritime waters\nand their resources and Title 5 deals with EIA requirements for major projects.", "Well Cementing design is a critical component of Well engineering, as efficient cement design ensures the protection of the casing assemblies from fluid corrosion, and ensures the mechanical support of the well. It also ensures that hydraulic communication between different zones is prevented.\nWell abandonment is also critical as the design of the slurry required needs to be designed to efficiently keep hydrocarbons in the wellbore and prevent any immediate, short term or long term migration of hydrocarbons to surface.\nThere are numerous studies and publications discussing the causes of gas migration after primary cement jobs and well abandonment, some of the causes of gas migration have been linked to poor fluid loss control, poor drilling fluid displacement (reduces seal efficiency at the interfaces), and long cement setting times which allows time for gas to percolate through the partially set cement slurry.\nThis paper highlights the engineering methods, and how they can be used to properly evaluate the cement slurry design to ensure that gas flow through the cement lattice is completely prevented. It assumes that all other issues which involving poor execution (mud displacement, poor slurry mixing, use of low quality materials and chemicals, human errors), are annulled.\nThe correlations/equations discussed and used for the evaluation of the abandoned case study well (Well XRT) are the Gas Flow Potential, Slurry Performance Number, Hydrostatic Number and Pressure Decay Limit Parameter. Results from critical evaluation with these equations confirmed that the Well XRT was efficiently abandoned.\nThe paper further recommends that these equations should be used by Well Engineers be used to evaluate slurry designs for casing cementing and abandonment operations, as they will help ensure that the mechanical and hydraulic isolation is efficiently designed for and achieved.", "This article discusses the big volume top job of oil and gas wells, specifically wells A and B which were drilled in Kuwait. The process involves pumping a larger volume of mixture of cement, water, and other additives into the annulus to seal the wellbore, prevent fluid migration and provide structural support.\nThe article highlights the need for precision and control to ensure proper placement. The conventional methods like two stage method and lightweight systems used for the wells A and B were not sufficient to get the good zonal isolation throughout the well bore due to the lower fracture gradient observed in this well. The successful zonal isolation was achieved due to pumping large volumes from the annulus.\nThe wells were under losses before and during the primary cementing process, which was difficult to achieve the desired top of cement (up to surface). To overcome these challenges, the well was cemented in unique unconventional method which is pumping the bigger volumes from the annulus to cover up to loss zone and eliminate any other fluid column in between. Cement Bond Log (CBL) and Variable Density Log (VDL) were taken after a 24 Hrs wait on cement and the results were good, indicating that the wellbore is properly sealed, and the well is structurally stable.\nPumping large volumes of cement through the annulus can be challenging, as it requires a high level of precision and control to ensure that the cement is properly placed. This process is different to that of conventional top jobs carried out by installing cement baskets. The intention of conventional top job methods is to just seal the annulus at surface without paying any attention to mud caps left in the open hole. This has resulted in remedial jobs which has increased the cost or reduced the life span of wells.\nOne of the key considerations when pumping cement through the annulus is the volumes considered and thickening time. The rate of flow must be carefully controlled to ensure that the cement is properly mixed along with the additives and that it does not become too thick or too thin. In addition, the rate of flow must be adjusted to account for the variations in pressure and temperature that occur as the cement moves through the well.\nCementing also plays an important role in preventing fluid migration. If the well is not properly sealed, there might be inter communication of the fluids which affects the life of the well. The extremely lower frac gradient wells undergo losses Inspite of using the conventional methods (light weight systems and two stage method) and is the reason to follow the unconventional method of cementing from the annulus so that entire well bore from shoe to the surface is properly sealed with cement. This will result in reducing the unnecessary remedial jobs during the life of the well." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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