bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Gas While Drilling in Fluid Detection and Perforation Strategy in Jurassic Reservoirs
Authors: Nguyen Kim Long , Shehabeldin Mahmoud , Al-Sabea Salem Hamad , Moustafa Ahmed , Ousdidene Karim , Sharma Sachin , El-Masry Mohab 
Published: February 2024

Abstract
Nowadays, the acquisition of Gas While Drilling (GWD) is applied widely in the Oil and Gas industry. The analysis of gas data plays a remarkable role in evaluating the hydrocarbon potential of the formations, especially in high-pressure, tight, and fractured carbonate reservoirs. In addition, the GWD finds its application in geo-steering to drive high-angle or horizontal wells to achieve maximized reservoir contact (MRC). This paper presents a procedure of interpreting gas data while drilling and its application in formation evaluation, determination of productive intervals, fluid characterization, and identification of fluid contacts. The study discusses the Upper and Middle Jurassic reservoirs in New Fields, West Kuwait. The excellent result of the GWD analysis efficiently identifies the hydrocarbon potentials and fluid contacts in New Fields. The well test results of recently drilled wells absolutely prove the successful deployment of the GWD method in West Kuwait.
Innovative Emissions Monitoring and Management Methods to Reduce Environmental Impact During Well Testing Operations
Authors: Franklin G. C. , Finley D. , Fink K. , Kool H. , McGouldrick M. 
Published: February 2024

Abstract
Well testing is crucial within the oil and gas industry. When production facilities are unavailable, hydrocarbon can flare during the gaseous or liquid phase. When this occurs, smoke and fallout from the burner are the most visible byproduct; during which, hazardous CO, CO2, and SO2 can be emitted. The choices available to the operator to manage produced hydrocarbon in an environmentally acceptable manner can be limited and driven by project economics.
Carbon production accounting is rapidly growing as a decision-making process for operators. Within just the last few years, operators have rapidly increased their commitment to reduced carbon emissions. However, proper quantification and monitoring of the environmental impact, particularly during well testing activities, still relies on dated gross calculations performed even before the operation. Innovative technologies and software are discussed to help control and evaluate the impact of emissions in real-time.
Current options available to operators are discussed with respect to management of well testing emissions and the trade-offs that must be considered during selection of the optimum well test design. Various case examples are highlighted, showing how technologically advanced solutions (i.e., hardware and software tools for the continuous acquisition, evaluation, monitoring, control, and reporting of emissions data) can provide critical information that enables environmentally acceptable decision-making in real time to support emissions abatement. Furthermore, the operator should examine all options available to design a well testing program with minimum impact to the environment. These include, but are not limited to, emissions management techniques at the surface and downhole as well as innovative operational efficiencies.
Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators
Authors: Andrew EbunOluwa , Xu Chicheng , Odi Uchenna , Sheludko Stanislav , Silver Andrew , Zayer Yaser 
Published: February 2024

Abstract
The main objective of this study is to derive petrophysical characteristics and predict petrophysical rock types (PRT) from a well log from the Volve Field Dataset, using unsupervised and supervised machine learning algorithms. We utilized available core data of the reservoir with an unsupervised K-means algorithm and supervised random forest classifier to predict rock types and permeability of the well. This paper proposes a methodology that takes advantage of calculated flow zone indicators (FZI) and hydraulic units for enhanced characterization.
The core data set containing 557 core plug samples, which consists of porosity, permeability, grain density, and water saturation measurements, was classified into PRTs based on core FZI and hydraulic units (HU). These calculated FZI and HU values of the core data acted as the training data set to estimate HUs for the well log using random forest classification. Permeability was then estimated from a linear regression associated with each HU, based on the calibration to calculated core FZI values.
This paper documents the ability to conduct petrophysical characterization with minimal initial core data. By applying the K-means algorithm to intuitively identify sedimentary and diagenetic characteristics of the reservoir, and by applying the random forest algorithm on a conventional parameter such as FZI, one can sufficiently estimate petrophysical properties and classify PRTs from well logs in a time-saving manner.
Artificial-Intelligence-Based Drilling Risk Prediction - Prevention is Better than Cure
Authors: Hussain Syed Aaquib , Hasija Ketan , Guillot Valerian , Sesboue Pierre , el Ganaini Ahmed , Hughes Todd 
Published: February 2024

Abstract
Efficient drilling operations necessitate accurate risk assessment to prevent wellbore failure and consequent nonproductive time (NPT). In this work, the authors present a comprehensive strategy to enhance subsurface well planning decisions using artificial intelligence (AI) and machine learning techniques, optimizing the estimation of integrated drilling contracts value, specifically Authorization for Expenditure (AFE) models.
In this study, authors address the challenge of quantitative risk assessment during drilling operations, aiming to predict minor impacts that can lead to catastrophic wellbore failure. The primary objective is to develop an AI-driven interactive dashboard that assimilates historical drilling experience to provide superior decision-making for well planning.
The approach involves analyzing historical offset well drilling events to identify critical risks leading to NPT. By leveraging the Gower similarity (GS) algorithm, a similarity matrix is established between observations from offset wells and a new drilling contract. Thirteen key features, including section depths, expected rate of penetration (ROP), mud characteristics, mud density, and dog leg severity (DLS), influence the clustering process. The resulting similarity matrix informs an unsupervised hierarchical clustering algorithm, optimized through Silhouette analysis. Subsequently, Monte-Carlo simulation is executed on derived risk categories to yield more precise AFE estimates.
This novel approach is validated using a diverse offset well database from 20 countries, with a specific application in a Middle Eastern country. Analyzing data from more than 252 historically drilled wells across five fields, the study predicts drilling risk categories and associated NPT for a targeted well. Visualizations, including interactive charts and maps, illustrate the distribution of risk categories among offset wells. Post-outlier removal, risk categories from 139 offset wells are selected for Monte-Carlo simulations. Predictions are presented in terms of occurrence probabilities and total NPT, promoting a more informed AFE. Collaboration with drilling domain experts and blind tests further corroborate the approaches effectiveness.
In this study, the authors pioneer a real-time monitoring methodology for drilling events and risks, harnessing evolving machine learning and AI advancements. By leveraging historical data and expert insights, it successfully improves the cost-effectiveness and safety of drilling practices. The approach stands as a testament to the power of AI in revolutionizing drilling operations.
A Quantitative Approach to Characterizing Natural Fractures in Tight Gas Sands, Using Data from Mud Loss
Authors: Tan X. , Li H. , Wen L. , Liu H. , Liu T. , Jia C. , Wang S. , Zheng R. , Liu J. 
Published: February 2024

Abstract
Natural fractures have a significant impact on the development of tight sandstone gas reservoirs. However, over the years, limited types of data have been used in characterizing fractures, leading to generally unsatisfactory prediction accuracy. This paper presents a quantitative prediction approach using data from mud loss during drilling, taking the J Gas Field in the Ordos Basin, China, as a case study. The target zone is the H1 member of the Upper Paleozoic, considered a set of superimposed braided-river sand bodies. In this geological setting, horizontal wells have been the preferred choice for exploiting Zone H1. The approach comprises the following steps: First, fracture development characteristics were obtained by studying core samples, image logs, lithology, bed thickness, and seismic attributes. Second, the importance of data from mud loss on fracture prediction was discussed, and the relationship between fractures and faults was analyzed. Third, log responses after core calibration were collected at mud loss points, and potential fractures were identified through well logging. Finally, a 3D model of fracture intensity was created to provide a quantitative characterization of fracture distribution, and the prediction accuracy was verified. The key findings are as follows: (1) Mud loss data can help in locating subsurface clusters of fractures, as mud loss is inherently linked to fracture dimensions. Therefore, mud loss can serve as a valuable index for describing fractures. (2) Major faults play a crucial role in controlling the distribution of fractures, with the orientation of faults also influencing this distribution. For instance, East-West faults have a more pronounced impact. (3) The occurrence of mud loss has a dual nature. On one hand, moderate mud loss can be an indicator of promising gas production. On the other hand, uncontrolled mud loss can result in severe formation damage and unexpected drilling termination. (4) The constructed fracture intensity model offers a quantitative prediction of fracture distribution, and the result demonstrates a satisfactory prediction accuracy. What sets this paper apart is the introduction of a quantitative methodology for predicting natural fractures, making full use of often neglected mud loss data.
Effective Options Utilized to Reduce or Eliminate Non-Productive Time in Case of Severe and Total Losses While Drilling Unconventional Horizontal Wells
Authors: Almasmoom Salahaldeen S. , Mugdad Ahmad I. , Yin Guoqiang , Qahtani Faris A. 
Published: February 2024

Abstract
The horizontal wells in an unconventional area of interest are designed with a combined 8-½-in vertical/curve/lateral sections. The section is typically drilled with oil-based mud (OBM). Severe to total losses might occur while drilling through (or bellow) potential loss zone (depleted zone) in the vertical section. When losses happen, drilling further to the well total depth (TD) stops. Conventional ways to cure the losses are time-consuming. By applying the presented practices in this paper, the losses are successfully cured in most cases to allow drilling ahead to well TD. The results are less overall risk, less non-productive time (NPT), and more cost savings when compared to the traditional methods.
Typical applied traditional methods to cure the severe/total losses are tripping out of the wellbore to change the bottom hole assembly to allow pumping higher loss-circulation materials (LCM) pills, setting a cement retainer inside the previous 9-⅝-in casing, and squeezing cement below the retainer to the loss zone. When the cement retainer is run, an additional cleanout BHA is needed to drill out the cement retainer and cement below it to continue to drill the remaining section. The worst-case scenario when the losses are not cured is running and cementing a 7-in liner to cover the loss zone. This option is very expensive and time-consuming.
The team applied new strategies to reduce the NPT required to cure the losses, and to attempt to eliminate the losses from even happening while drilling through the potential loss zone. The NPT reduced dramatically when compared with wells drilled using the conventional methods, and it is eliminated in most of the newly drilled wells. The drilling fluid system (also called mud system) is modified to reduce the associated cost of the fluid lost to the loss zone. Furthermore, circulating sub is used in the bottomhole assembly (BHA) to allow pumping high concentration LCM pills without the need to trip out to change the BHA configuration. The developed pump-and-pull (P&P) cementing technique is used instead of the cement retainer when pumping cement is required. However, the most important practice is strengthening the potential loss zone while drilling through and below it to eliminate the NPT completely. A note to mention is that the loss zone in some areas in the unconventional area of interest is highly fractured to the point where even the developed methods are not successfull. Nevertheless, the success rate of these newly applied operational practices is high across the majority of the area of interest.
Towards Net Zero - Better Decision Making for Enterprise Level Green House Gas (GHG) Emission Management and Accounting
Authors: Ratasha Muhammad Aqmal Bin , Ismail M. Syahezat Bin , Aziz Ahmad Sulaiman Abd , Williams Gareth , Rees Emily 
Published: February 2024

As a globally recognized energy company, PETRONAS is deeply committed to achieving a balanced approach to energy development and transition, which encompasses energy security, affordability, and sustainability. We remain steadfast in our commitment to optimizing our asset portfolio to lower carbon emission intensity. Our primary focus is on the development of oil and gas resources that incorporate low emissions or emissions abatement technologies, while concurrently pursuing the phased withdrawal of high carbon intensity assets.
Our current emphasis is on reducing Scope 1 emissions, which are emissions directly associated with our operations, as well as Scope 2 emissions, which encompass the energy consumed in our operations. We have revised our carbon emissions accounting method to adhere to international frameworks and sector specific guidance to give us a robust basis for Scope 1 and Scope 2, and a better understanding of Scope 3 greenhouse gas emissions.
Starting in 2023, PETRONAS aims to fully adhere to the Greenhouse Gas Protocol Corporate Accounting and Reporting Standard (2004), Ipieca Climate Change Reporting Framework (2011) and ISO 14064-1: 2018. To provide a total view of emissions across the Group, we will report emissions according to the operational control approach and equity share approach, and include Scope 1, Scope 2 and Scope 3 (prioritizing Category 11 ‘use of sold products’) greenhouse gas emissions. Our strengthened reporting will improve external benchmarking and provide a better correlation between financial and greenhouse gas emissions accounting.
Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies
Authors: Janjua Aneeq Nasir , Abdulraheem Abdulazeez , Tariq Zeeshan 
Published: February 2024

AbstractLithology identification holds a pivotal role in the characterization of subsurface formations. In recent years, the advent of big data and the need for more precise lithology identification have spurred the growing adoption of machine learning algorithms. This paper's primary objective is to leverage unsupervised machine learning techniques for the identification and subsequent removal of anomalies inherent in complex datasets. The Isolation Forest model is the cornerstone of our approach for anomaly detection and elimination.To initiate this process, we employed the K-means algorithm to create clusters, followed by a evaluation using silhouette coefficients. Subsequently, we selected input data for each cluster and conducted exploratory data analysis both prior to and after the removal of outliers. The histograms portraying average anomaly scores for each cluster were thoughtfully presented. For real-time anomaly detection, we harnessed the power of the Isolation Forest model, which facilitated the creation of an Isolation Forest anomalies map by plotting neutron-porosity against bulk density.Our rigorous data analysis employed various statistical techniques, including data statistics, histograms, and cross plots between neutron-porosity and bulk density. This multifaceted approach successfully eradicated anomalies from the dataset, a fact vividly illustrated by the histograms, where anomalies were discernible through their negative scores. The Isolation Forest map conclusively demonstrated the effective removal of outliers from the dataset, underscoring the model's proficiency in identifying and mitigating these anomalies based on their negative scores.The Isolation Forest model has thus exhibited remarkable efficacy in the identification and elimination of data anomalies. Its versatility makes it a valuable asset for the detection and removal of outliers, deviations, or noise from datasets, rendering it particularly well-suited for anomaly detection and outlier mitigation in various analytical scenarios. Notably, the combination of the K-means and Isolation Forest algorithms emerges as a potent and advantageous approach, especially when dealing with extensive datasets and conducting comprehensive analyses.
Advanced Machine Learning Framework for Enhanced Lithology Classification and Identification
Authors: Zhang P. , Gao T. , Li R. , Fu J. 
Published: February 2024

Abstract
In this comprehensive study, machine learning (ML) techniques are employed to revolutionize lithology classification within the geosciences, emphasizing the transformative impact of ML on traditional practices. The research encapsulates ML's integration into well-log data analysis, enhancing prediction accuracy and efficiency in lithology identification—a crucial aspect of subsurface exploration.
The methodology adopted includes systematic data preprocessing, feature extraction, and the deployment of advanced ML algorithms such as Support Vector Machines and Random Forest for lithology classification. Models are trained and validated against well-log data from the Teapot Dome Reservoir and the Force 2020 Dataset, with the latter representing a collaborative and competitive environment aimed at advancing ML applications in geoscience.
Results reveal a marked increase in predictive accuracy when incorporating a wider array of logs, as evidenced by Models A1 and A2 for the Teapot Dome Reservoir, and Models B1 and B2 for the Force 2020 Dataset. The research highlights the critical role of ML in achieving high accuracies in lithology prediction, with improved generalization capabilities across different geological settings.
The workflow emphasizes the potential of ML algorithms to enhance well-log interpretation, streamline geological analyses, and reduce the time required for data processing. The study suggests future work focusing on expanding lithology types, normalizing log data, and broadening geographical coverage to further refine ML models for lithology classification. This effort underscores the convergence of ML with geoscience, promising a future where digital technologies create a more interconnected system for subsurface exploration.
Understanding of Geochemical Reactions in Hydrogen-Injected Wells: Cement Integrity for Safe Underground Hydrogen Storage
Authors: Al Dandan Esam , Hossain Md Mofazzal 
Published: February 2024

Abstract
As the world transitions to clean energy sources, Underground Hydrogen Storage (UHS) has emerged as a leading solution for large-scale hydrogen storage. While the depleted oil or gas reservoirs are ideal for UHS, the effect of geochemical reactions among injected hydrogen, wellbore, and cement is not documented. This study aims to assess cement and well integrity by examining the geochemical interaction between API cement and hydrogen near the wellbore under varying temperature and pressure conditions. The numerical simulation was carried out to study the geochemical reaction between hydrogen and API class G/H cement minerals using the PHREEQC version 3 simulator. The dissolution reactions of hydrogen with the initial cement components, namely calcium tetra calcium alumino-ferrite (C4AF), tricalcium aluminate (C3A), tricalcium silicate (C3S), and dicalcium silicate (C2S) were modelled at various pressure and temperature conditions. The simulation assumed continuous cement hydration over an infinite time to assess the long-term effects of hydrogen-cement interactions and its impact on cement integrity near the wellbore. Based on this numerical simulation, we found that at 56.2oC, the formation of calcium silicate hydrate(CSH), portlandite, C3AH6, Mackinawite, magnetite, and hydrotalcite. At 95°C, similar minerals were formed with slightly higher amounts of CSH and slightly less portlandite, while others did not exhibit a noticeable difference. At 119°C, it was observed that a noticeable increase in CSH and a noticeable reduction in portlandite amount. Additionally, the formation of ettringite was observed at elevated temperatures. These findings highlight the temperature- dependent changes in mineral composition near the wellbore, which may have implications for the long-term integrity of the cement matrix in hydrogen-affected environments. Based on comprehensive numerical simulation studies, this paper highlights critical insights for a better understanding of hydrogen-cement interactions in the context of underground hydrogen storage, and its impact on the long-term-integrity of wellbores in hydrogen storage application, essential for enhancing the knowledge base for safe and effective implementation of underground hydrogen storage technologies.