bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing ESP Power Consumption: A Case Study for Cost Reduction and Extended Runlife in Egypt
Authors: Salah Ahmed , Fayez Wael , Sabaa Ahmed , Mokhtar Mohamed , ElMenshawy A. D. , Diaa Ahmed 
Published: October 2024

Abstract
During times of volatile oil prices, optimization of production costs per barrel is a critical component for international upstream companies to ensure competitiveness and profitability. Therefore, the issue of maintaining oil production energy efficiency becomes more important nowadays. While Electrical Submersible Pumps (ESPs) are a reliable solution for lifting moderate to high fluid volumes from wells, a crucial challenge lies in maximizing system performance and minimizing daily energy consumption for each unit.
Agiba Petroleum Company relies on Electric Submersible Pumps (ESPs) for artificial lift in its onshore field (SWM-4) in Western Desert of Egypt. However, ESPs were operated by standalone generators and suffered from high diesel consumption. Additionally, several electrical failures encountered due to unstable input voltage and high motor temperature for low-rate wells. Those failures impacted both ESP run life and overall oil production efficiency. To address these limitations, this paper presents a case study exploring how replacing conventional ESPs with permanent magnet motors (PMMs) coupled with innovative power-saving pumps can significantly reduce power consumption and eliminate electrical failures.
A novel ESP system utilizing a Power Save high-efficiency ESP driven by a permanent magnet motor, has demonstrably improved run life in the SWM-4 fields. This configuration boasts a substantial increase in meantime between failures compared to traditional ESP systems. Additionally, a study revealed a substantial 30% reduction in total diesel consumption when compared to diesel consumption before the implementation. Additional benefits of PMMs include higher power factor, increased efficiency, synchronous operation, and higher power density. These advantages make it easier to address challenges like low productivity wells, unstable inflow, viscous fluids, and solids content. This case study outlines the artificial lift system selection process and the implementation of new PMM technology in ESP systems, covering conceptual aspects (design, specifications, and manufacturing), installation, and operational considerations (reliability and optimization).
In response to growing concerns about climate change, National Oil Companies (NOCs) are actively seeking ways to minimize greenhouse gas emissions from their operations. New advancements in ESP technology offer a promising solution: Power Save ESP systems driven by PMM. These systems not only improve profitability by lowering operating costs and reducing electricity requirements, but more importantly, they contribute significantly to environmental sustainability by decreasing overall power consumption throughout the ESP's lifespan.
Calculating Indirect Emission for Business Support Activities by Using Emission Factor Improvement with Carbon Calculator Application
Authors: Ligafinza A. , Putra N. , Nurjanah S. , Kadarsih W. S. , _ Machfud , Natalie R. E. 
Published: October 2024

Abstract
As a company with a vision to become a global and sustainable energy company, we need to disclose several environmental aspects, one of which is emissions. Scope 1 & 2 emissions can easily be disclosed by the company as the data is within the company. However, for scope 3 emissions, particularly categories 6 & 7, which include business travel and employee commuting, calculation data resides with the employees. Therefore, a tool is needed to standardize the calculation methods for scope 3 categories 6 & 7 and also to accelerate data collection process. Hence, we have decided to develop an application called "Carbon Calculator."
The basic design of this application refers to the IPCC Guidelines for National Greenhouse Gas Inventories 2006. In general, greenhouse gas emissions (GHG) are calculated by multiplying activity data with emission factors. Activity data refers to energy consumption data according to the type used, while emission factors are coefficients indicating the amount of emissions per activity. In the technical implementation of emission calculations in the Carbon Calculator application, calculations will be adjusted based on data availability and emission factors. To simplify emission calculations from vehicles, the company processes advanced data on Net Calorific Value (NCV), emission factors, and fuel economy to obtain emission load calculation results from the use of private vehicles. Carbon calculator is a web-based application for GHG inventory that will be used by all workers.
PHE Carbon Calculator enhances emissions transparency for indirect emission (Scope 3 Category 6 & 7) and empowers PHE to proactively manage and reduce its environmental footprint and further indicates its commitment to global sustainability leadership. Currently, PHE is focused on familiarizing the use of the carbon calculator and conducting a campaign to disclose scope 3 emissions. At present, nearly all regions have participated in filling out the carbon calculator.
IPCC has provided the methodology for calculating GHG, however in this study we improve the emission factor specification to have more specific result in employee commuting and business travel disclosure. Future development of PHE Carbon Calculator will focus on scalability and integration of advanced analytics to further optimize sustainability initiatives.
Leveraging Weibull Analysis and Python Automation for Enhanced Failure Prediction of Electrical Submersible Pumps
Authors: Radwan O. , Salem Adel. M. , Ghareeb M. , Ibrahim F. 
Published: October 2024

Abstract
The primary objective of this study is to predict the failure of electrical submersible pumps (ESPs) by utilizing Weibull distribution analysis and Python coding, thereby enhancing the reliability and operational efficiency of ESPs in the petroleum industry. Moreover, it assists in intervention planning and rig count optimization.
This study employs a systematic approach involving the collection of ESP failure data, which is then analyzed using Weibull distribution to determine the shape and scale parameters. Python scripts are utilized to automate data processing, fitting the Weibull model, and conducting goodness-of-fit tests including the Anderson-Darling and Kolmogorov-Smirnov tests. This automation facilitates efficient data handling and analysis, ensuring accurate and reproducible results.
The analysis reveals that the Weibull distribution provides a good fit for the ESP failure data, as indicated by the p-values from the goodness-of-fit tests. The shape and scale parameters derived from the Weibull model enable the estimation of Mean Time to Failure (MTTF) and the probability of failure over specified time intervals. These insights are critical for maintenance planning and reliability improvement of ESPs. Furthermore, the automation process significantly reduces the time and effort required for data analysis, making it a practical tool for ongoing reliability assessments.
This paper introduces a novel integration of Weibull analysis with Python automation for ESP failure prediction, offering a significant improvement over traditional manual method. The approach enhances the reliability analysis process, providing valuable insights that contribute to the optimization of ESP maintenance strategies in the petroleum industry.
Water Injection Optimization for Natural Fractured Carbonate Reservoir: Descriptive Model Configuration Achieving Optimized Field Management: Khepri-D Field Case
Authors: Abdel Rahman M. Fathy , Elshaafie A. , Abdel-Fattah S. , Abdelazim A. , Fagelnour M. S. 
Published: October 2024

Abstract
Development of naturally fractured reservoirs, and water flooding pattern design for such reservoir is critical due to complicated nature of this type. The objective here, was how to maximize oil production and reserves through minimizing early breakthrough risk at the producers and define the optimal locations of new producers and injectors. In moderate development stages, the degree of confidence increased in static model including fracture network, permeability. facies and dynamic data (ex. production data) is relatively high.
A reservoir simulation study of the depletion drive oil mechanism in (Khepri-D field), was done to evaluate its original oil in place (OOIP), recovery factor (RF) and hence the optimum Field Development Plan (FDP) and injection pattern. Integration between reservoir, geological and petrophysical teams was organized to achieve this target. Reservoir study passed through different phases of history matching, along the reservoir lifetime. First history matching phase, was after one year of discovery and the second phase was after three years. In primary phase, the main concern was the pressure matching, which should be consistent with rate transient analysis and material balance rather than the water breakthrough. Consequently, building scenarios to describe static model, mainly includes representation of fracture network and geological structure of high productivity wells. Appropriate changes in structure model have been done to eliminate chance of early breakthrough. In the second phase, the main objective of the study was evaluation of proposed development injection and production wells. The preferred locations of wells would achieve maximum impact on the economics without losing reserves. The model uncertainties have been reduced by conducting several scenarios, especially on the structure model.
Finally, there were two scopes of results for the two phases of study. The first Phase, the study proved that current structure will not be representative and need to be reconfigured for achieving accurate history matching, in order to be consistent with reservoir management work, especially OOIP, that was estimated from reservoir analytical method. Drilling new wells was mandatory to confirm and capture additional reserves. as per the second phase of the study, dynamic modeling resulted in drilling a new well with initial oil production rate around 5000 STB/D. The proposal of new drilling opportunity, based on the structure configuration, facies, SCAL analysis, and fracture network achieving high ultimate recovery of about 40%.
This paper is a good example of how to develop depleted carbonate reservoirs achieving reasonable ultimate recovery through cooperative work between all disciplines and gathering data from different source to build integrated representative model.
Enhancing Rate of Penetration Predictions in the Surat Basin Using Supervised Machine Learning: A Comparative Study of ANN and XGBoost Models
Authors: Chang Mu , Zhong Ruizhi , Johnson Ray , Chen Zhongwei 
Published: October 2024

Abstract
The Surat Basin (the Basin) in Australia is rich in coal seam gas resources, characterized by up to 2500 meters of relatively continuous sediment deposition. However, the Basin presents significant geological challenges, including extended drilling cycles and rising costs, necessitating precise rate-of-penetration (ROP) predictions. Current models often rely on engineers' experience and exhibit considerable errors (typically between 25%-50%) and unstable performance. This highlights the need for refined approaches to improve ROP prediction accuracy. This study employs supervised machine learning methods, specifically Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost), to predict ROP. Data were collected and preprocessed from five wells in the Surat Basin, divided into three training/validation wells and two testing wells. The data underwent detailed cleaning and processing, including selecting long drilling periods and eliminating low rotations per minute (RPM) points during frequent drilling pipe extensions. For model training, hyperparameter tuning and cross-validation were employed to optimize the models. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²).
Both XGBoost and ANN models delivered good results, with XGBoost generally outperforming ANN, particularly after data filtering. The two testing wells (i.e., W2 and W6) showed enhanced performance through data filtering, which removed noise and irrelevant data points. For well W2, the results were R² of 0.59, MAE of 10.2 m/h, RMSE of 13.5 m/h, and MAPE of 14.3%. For well W6, the results were R² of 0.55, MAE of 11.7 m/h, RMSE of 15.5 m/h, and MAPE of 15.8%. The predicted ROP curves closely matched the actual drilling ROP curves. The application of ANN and XGBoost for ROP prediction demonstrates significant improvements in accuracy and predictability, with XGBoost showing superior performance. This study provides a robust and reliable tool for optimizing drilling operations in complex geological settings, contributing to more efficient coal seam gas extraction.
Comprehensive Integrated Study on a Novel Completion Technology of Hybrid ESP-Gas Lift System for a Gas Lift Field in the GOS
Authors: Magdy K. M. , Moussa A. , Helmy A. G. , Abd Elrahman M. , Aly Seida A. , Khattab A. , Sobhy M. , Ibrahim M. , Hammam M. , Elbanna B. , Korish M. , El-Sheikh M. I. 
Published: October 2024

Abstract
GS327 field is a small field located in the central of GOS with a relative heavier oil than the other neighboring fields which affects the sweeping efficiency. Started production in 1986 with a peak production in 2007. The field is being operated under gas lift artificial method, producing mainly from two reservoirs with high petro-physical parameters and very good sand quality in addition to be supported by a strong aquifer. One of our main responsibilities as petroleum engineers is to select the optimum artificial lift technology to maximize the field production. In an especially limited offshore field, not all methods are applicable due to the platform spaces, operating costs, and system reliability.
Concern for alternative lifting methods has arisen since 2012, due to the limitation of gas lift injection capacity that is no longer adequate for the current and future development wells at that time. Moreover, the network system is suffering from bottleneck causing back pressure and losing more oil, this is mainly due to the gas effect in the production lines. After due consideration, the Electrical Submersible Pump (ESP) was finally decided as the key driver to reinforce well production performance. Moreover, the gas lift has been brought in as a backup in case of pump failure, which is not only to prolong well life, save workover expenditure but also boost production if operating in hybrid mode.
A comprehensive integrated study was carried on to evaluate the switching from Gas Lift operated field to the novel hybrid ESP Gas Lift system taking the advantage of the ESP wide range of outflow performance to increase the oil production and hence adding more reserve. Initializing the study first by using the reservoir analytical approaches to assess the added value of the ESP; WOR Exercise, analog performance, asset integrated modeling by GAP MBAL PROSPER was created which revealed we can add around 1.5 MMSTBO as an incremental reserve. Realizing the construction, layout and electricity power sources were critical points that took much effort to assess. In addition the risk of sand production due to the higher drawdown from the ESP was studied as well from the geomechanics team to mitigate it.Finally a complete reservoir simulation model was created to integrate all the inputs, assumptions, risks and uncertainties which revealed we have a very good room to increase the sweeping efficiency, drawing out more oil and increase the reserve.
In this paper, we present sequential events from the conceptual study to the designed pilot test. It is a very good example of managing uncertainty by integrating different data to convert it into reality that can be translated ultimately into oil production and revenues
Innovative Machine Learning Approach to Locate the Remaining Gas
Authors: Mohamed Islam A. , Hosny Ahmed , Amin Sameh 
Published: October 2024

Abstract
Locating remaining gas in a producing gas field poses several challenges, primarily due to complexities in reservoir dynamics, data availability, and technological limitations. Gas reservoirs often exhibit complex geological formations and heterogeneities. Variations in porosity, permeability, and rock types can create irregular gas distribution, making it difficult to accurately predict where remaining gas pockets might be located. Moreover, sparse well coverage, incomplete data sets, or inaccuracies in historical data can hinder accurate reservoir modeling and forecasting. Addressing these challenges requires a multidisciplinary approach combining geological understanding, advanced reservoir engineering, and innovative technologies. We designed a new approach that predicts the reservoir properties via Machine Learning algorithm, Neural Network Inversion, in order to locate the remaining reserves. In this workflow, we predict 3D volumes of porosity and water saturation using deep learning neural network algorithm. Then, we resampled these volumes into reservoir 3D structural grid. Then, we defined the net and calculated the gas in-place for different reservoir segments. And finally compare these calculations with the dynamic production data. The proposed workflow was calibrated through multiple blind well tests and the predictions showed high accuracy. The results of the project were astonishing. 500+ billion cubic feet of gas were added to the portfolio in the form of infill and near-field exploration opportunities. Consequently, a next phase of development was proposed based on the findings. The proposed approach demonstrates that the data integration and advanced predictive modeling are key strategies to optimize gas recovery and extend the productive life of gas fields.
Innovative Approach of Generative AI for Automating Technical Bid Evaluations in Oil Companies
Authors: Abdelaziem O. E. , Khafagy A. Nasser , Yehia T. A. 
Published: October 2024

Summary
The process of outlining a scope of work and evaluating technical bids in the oil and gas industry is commonly burdensome, labor-intensive, and susceptible to human bias. This paper introduces an AI-assistant chatbot based on the power of open-source large language models (LLMs), natural language processing (NLP), and data analytics, to aid in automating the entire workflow of technical tendering processes, facilitating an improved decision support system (DSS), and mitigating potential subjectivity.
The workflow starts with loading documents in the format of scanned PDF files. Firstly, data was extracted using optical character recognition (OCR), and text mining techniques. Secondly, Langchain was implemented to optimally split the document into chunks with adequate overlapping. Thirdly, embeddings were created using sentence transformers, and a vector store was constructed. After that, LlaMa model, was optimized and employed to query the vector store efficiently. Then, retrieval augmented generative (RAG) query engines were used to retrieve the matching documents and generate the final answer. Finally, a chat memory buffer was incorporated to maintain context and initialize a chatbot.
Implementing LLM in the tendering process has proven promising for reviewing and ranking the technical data of the submitted bids based on pre-defined criteria. However, it was shown that extracting tabulated data effectively from scanned documents is challenging. Consequently, it was essential to pre-process documents and extract tables into structured databases before querying them. In addition, model augmentation was employed to avoid hallucination and enhance the model reasoning and capability of identifying pass and fail criteria, nonetheless, human feedback is required to assess satisfying performance. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) was found valuable to expedite the comparison between the submitted data in technical tables and the detailed data sheets. Furthermore, LLM was found effective in accelerating the process of market survey comparison, by retrieving and comparing prices and conditions of different documents. Moreover, supervised fine-tuning (SFT) was implemented to improve the model's capacity to compare clauses of the scope of work along with terms and conditions across different contracts, in order to discern major inherent differences and reduce potential bias in the future. This enhancement was configured through quantized low-rank adaptation (QLoRA) and parameter-efficient fine-tuning (PEFT).
This paper presented a novel approach, which integrated LLMs into procurement information development systems, automating multiple tasks in the tendering process in the oil and gas upstream industry. This is the first time that such an application has been applied to the oil industry, which shall provide a foundation for future research.
Novel Dual Porosity Simulation Workflow and Solutions for the 3D Fracture Modeling
Authors: Hammam M. , Soliman K. 
Published: October 2024

Abstract
Naturally fractured reservoirs represent more than 50% of reservoirs and contribute in a large extent to the worldwide production of oil and gas (more than 60% of the world's proven oil reserves and 40% of the world's gas reserves). These reservoirs exhibit significant heterogeneity, featuring intricate networks of multiple fracture families with varying spatial distribution and conductivity. Conducting and performing reservoir studies on these naturally fractured systems are challenging tasks because they present an extreme property contrast between the two domains it comprises: rock matrix and fractures.
The process of 3D fracture modeling involves multiple disciplines and steps within reservoir characterization and simulation. Its core objective is to build on geological concepts and collect data, including interpreting beds, faults, and fractures from image log data, using field outcrop studies as conceptual analogs, and utilizing seismic attributes as fracture drivers. Given the complexity of these heterogeneous systems and the involvement of various disciplines, workflow and solutions reference for the fracture modeling can uniquely guide teams, enhance knowledge, and improve work efficiency. Hence, the value for this paper comes in the absence of handed reach educational reference.
In this work, an integrated sequential workflow for 3D fracture modeling is presented. This workflow identifies the necessary data and its analysis that needed in building the model. It begins with creating the fracture intensity to construct a Discrete Fracture Network (DFN) and then generates upscale fracture properties based on the DFN model to develop the dual porosity simulation model.
This paper provides a detailed practical workflow reference for the fracture modeling for every researcher. In addition, the study was aiming to enhance knowledge and work efficiency to increase the integration and dependencies awareness between different disciplines.