bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of Water-Swelling Agents with a Function of Programmable Self-Destruction for Well Killing Operations in Oil and Gas Condensate Fields Complicated by Fractures and Abnormally Low Reservoir Pressure
Authors: Mustafin A. , Varfolomeev M. , Pavelyev R. , Bolotov A. , Minkhanov I. , Kalmeteva R. , Mirzakimov U. 
Published: November 2024

Abstract
This work presents a water swelling agent with a function of "programmable" self-destruction for reservoirs complicated by fractures, and investigates the possibility of its application for well-killing operations, where conventional agents are ineffective.
At the first stage, the synthesis of water swelling gel particles formulations was carried out, taking into account the selection of crosslinker to achieve "programmable" self-destruction of the formulation after a certain period of time. Then, works were carried out on selection of optimal formulations on the basis of study of swelling coefficient, long-term stability, self-degradation time, rheological parameters and blocking properties under different salinity conditions and compared with current commercial agent. The efficiency of the compositions was determined on the basis of wellbore modeling system under reservoir conditions to analyze the blocking efficiency of gel particles in real time.
Synthesis of samples based on different crosslinker monomers, initiators, and modifiers (79 compositions) were carried out, which allowed us to develop a composition with optimal degradation dynamics, capable of being stable for 7 days and further self-degrade at 40 °C and salinity of 3.6 g/L (grams per litter), have a low density and are compatible with reservoir fluids. All samples, have corrosion rates up to 0.1 g/m2·hour (grams per square meter hour) and characterized by a high strength factor for 7 days. After this period, the parameter decreases due to the beginning of degrade. The results of studies of rheological characteristics of blocking compositions indicate that the synthesized samples show high values of static and dynamic shear stress more than 62 Pa (pascal). According to the results of studies, on the HTHP filter press, it can be concluded that the synthesized particles are characterized by sufficiently high blocking properties, capable of creating a fracture aperture 0.2 mm (milimeters) opening and low ability to penetrate, less than 4 ml of liquid has been filtered during experiment. According to the results of filtration studies, the synthesized gel particles can effectively block the model of an idealized fracture, 0.1 mm opening up to pressures of 63 bar, making this system a great potential for well killing operations in reservoirs complicated by fractures. Based on the results of laboratory tests, the developed particles can be recommended for further pilot tests.
Generally, the developed water swelling gel particles with "programmable" degradation showed high efficiency of blocking and reduction of well development costs. This allows expanding the application to well killing operations for abnormally low formation pressure reservoirs complicated by natural fractures.
Can 3D Seismic Assist the Subsurface Characterization of Low Permeability Reservoirs? A Western Canada Case Study
Authors: Abaco Christian , Jowett David , Baniak Greg 
Published: November 2024

Abstract
The appraisal and development of low permeability reservoirs require detailed knowledge of their quality and spatial variation. Different applications of 3D seismic data are used to map reservoir's property variation for subsurface characterization, including structural analysis, reservoir property mapping and seismic lithofacies prediction.
Seismic structural attributes were used to map large structural features that compartmentalize the reservoir and to identify enhanced natural fracture zones that may locally increase reservoir's permeability (‘sweet spots’). The prestack AVO and rock physics inversions are used for geomechanical properties calculations and total porosity and mineral fraction volume estimation. The inversion results were further extended for reservoir stress computation including pore pressure and effective stress estimations calibrated to pore pressure and DFIT measurements. Reservoir lithofacies volumes were generated using machine learning workflows and direct probabilistic inversion outputs, with the results generally matching the reservoir's lithology mapped from the core data.
Moving forward, the 3D seismic data can successfully assist the subsurface characterization of low permeability reservoirs and can be used to support well placement and to optimize the drilling and completion operations.
Recent Findings from Ongoing Surveillance Using Time-Lapse Walkaway VSP Monitoring of CO2 WAG EOR Project in an Onshore Field in Abu Dhabi
Authors: Mohamed M. , Ahmed J. , Waqas M. , Alhamedi A. , Gupta S. , Dogra S. , Albulushi A. , Guerra R. , Ali S. 
Published: November 2024

Abstract
Time-lapse seismic is an essential tool for reservoir monitoring. However, using 4D seismic in carbonate reservoirs poses some difficulties because of rock's nature. Time-lapse Walkaway VSP surveys were carried out on an onshore carbonate field in Abu Dhabi, which included Baseline and three Monitor surveys. The surveys aimed to track the fluid-front due to the CO2 WAG injection, and to assess repeatability between sources and receivers and associated 4D signal. The recent Monitor-3 survey was acquired after 6.5 years of CO2 WAG injection post the Baseline survey. This paper discusses results from the Monitor-3.
The acquisition geometry was same for all the surveys, however for Monitor-3 the source offset was extended to 5km from the wellhead to refine the anisotropy model to improve the final image. The data revealed good repeatability between the Baseline and Monitor-3 surveys. A processing workflow that followed 4D standards effectively improved repeatability during the stages of processing. Final images around the reservoir showed visible seismic changes along Walkaway VSP orientation post injection during the period between Baseline and Monitor-3. The 4D signal is stronger due to the longer period of injection.
3C VSP processing was performed while taking into consideration Baseline and Monitor-3 NRMS and predictability at each processing step. The customized processing workflow that was developed for Monitor-2 was implemented in the current Monitor-3 processing. The extended Walkaway VSP line provided higher arrival angles to estimate overburden Shale Layer anisotropy parameters in the model which improved the final image. 4D signal was observed on the migrated images at the reservoir level which is consistent with the findings of the rock physics model.
Time-lapse seismic studies are common in clastic reservoirs, yet they are very limited in carbonates. It is believed that this study will add value in understanding the fluid distribution in the reservoir using seismic methods. This will provide better confidence in considering and deploying time-lapse borehole seismic surveys as an initial part of reservoir monitoring activities plan for Enhanced Oil Recovery (EOR) projects. This paper highlights the improvements and optimizations done in acquiring the Walkaway VSP data as well as data processing and interpretation. This workflow can serve as a guide for future time-lapse Walkaway VSP studies.
Breakthrough in Fractured Reservoir Modeling: p-EDFM Approach Resolves Complexities and Accelerates Hydrocarbon Recovery
Authors: Alameri Maitha , Singh M. K. , Tene M. , Rinco S. , Al Kobaisi M. S. , Rat Coriolan , Dias Daniel , Szyndel Matthew , Kada Kloucha C. , Al-Kindi Z. S. , Mustapha H. 
Published: November 2024

Abstract
Fractured reservoirs present unique challenges in the oil and gas sector due to their complex genesis and fluid flow dynamics. Modeling these reservoirs requires overcoming various hurdles, including characterizing complex fracture networks, understanding dual-porosity flow dynamics, and managing scale dependencies. This paper highlights advancements in fractured reservoir modeling technology achieved through a collaborative effort between industry and academia. Our research and technological advancements have culminated in a commercial simulation solution tailored for fractured heterogeneous reservoirs, integrating the projection-based embedded discrete fracture modeling approach (p-EDFM). This method effectively models multiphase fluid flow within highly fractured formations, sidestepping grid complexities and expediting simulation processes. In p-EDFM, fractures are distinctly represented as discrete entities, each with their unique geometry and properties, embedded within a porous matrix with its own characteristics. This breakthrough significantly enhances simulation capabilities for practical applications and establishes a solid foundation for future strides in dynamic, multilevel, multiscale simulations. The new modeling technology facilitates more accurate reservoir characterization, enabling enhanced reservoir management strategies that optimize production rates and maximize hydrocarbon recovery. Additionally, the ability to predict fluid flow dynamics within fractured reservoirs with greater precision empowers operators to mitigate operational risks and minimize environmental impacts, promoting sustainable resource extraction practices. This paper offers novel insights by presenting advanced modeling techniques specifically tailored for fractured reservoirs, including scenarios involving hydraulic fractures where applicable. The performance-to-accuracy ratio achieved by this technology supports effective uncertainty and optimization
Artificial Intelligence for Crew Operations Optimization: A Novel Approach to Passenger Transportation
Authors: Thatcher Jimmy , Amankhan Assilkhan , Eldred Morgan , Rehman Abdul , Vedat Kris 
Published: November 2024

Abstract
This paper presents a case study on enhancing offshore helicopter passenger transportation using artificial intelligence (AI), focusing on refining fleet management, route optimisation, and sustainability. The methodology integrates extensive data analysis, algorithm development, and real-world testing to optimize helicopter schedules and reduce carbon footprints. By correcting fleet status inaccuracies and incorporating variables such as shuttle flights and maintenance schedules, the study develops a dynamic scheduling algorithm that prevents time overlaps and responds efficiently to real-time operational changes. Results indicate that AI-driven solutions can significantly improve operational efficiency—optimising crew assignments and departure intervals, reducing delays by up to 15.70%, and cutting fuel consumption by 8.10%, which also decreases CO2 emissions. This novel approach not only enhances operational efficiency and environmental sustainability but also offers a scalable model for future innovations in offshore transportation logistics.
Operational Insights with Predictive Gamma Analysis to Enhance Drilling Performance
Authors: Davis J. R. , Kolesnik I. , Blackwell B. 
Published: November 2024

Abstract
The drilling process in oil and gas extraction is a multifaceted operation requiring the integration of various systems and technologies. These systems include fluid systems, mechanical rig systems, the drill string, and the bottom hole assembly (BHA), all of which play critical roles in achieving efficient and safe drilling operations. This study focuses on optimizing drilling operations by predicting gamma changes and trends based on drilling parameters measured at the surface, aiming to enhance drilling efficiency, reduce non-productive time (NPT), and minimize the frequency of unplanned trips due to measurement-while-drilling (M/LWD) tool failures.
The study underscores the importance of "knowing the rock" by reducing uncertainty associated with gamma signal lag. This approach empowers drilling teams to manage formation transitions better and improve geosteering, particularly as the industry moves towards longer laterals. In vertical applications, accurate formation transition management and avoidance of unplanned trips due to M/LWD failures are critical for optimizing performance. In lateral applications, predicting gamma trends at the bit enables more precise geosteering and allows drilling to continue despite tool failures.
This paper outlines the method used to generate usable machine learning algorithms for gamma trend prediction using only surface drilling parameters, including: data selection, data preparation, feature engineering, model selection and the challenges experienced. The data preparation process is discussed in detail, including initial data selection, smoothing, filtering, gap handling. Feature engineering involved selecting and transforming relevant drilling parameters, such as weight on bit (WOB), surface rotations per minute (SRPM), differential pressure (DIFF), rate of penetration (ROP), and surface torque (TQ). Pre-processing was conducted to identify steady and non-steady state drilling regimes, with unique models trained for each hole section.
Two types of supervised classification models, Random Forest and XGBoost, were tested for gamma change trend prediction across three different hole sections. The results demonstrated the models' ability to identify and correlate changes in gamma trends with drilling parameters commonly measured on every rig, making the model widely applicable. Discrete models were trained per field and per hole section to account for the differences in drilling techniques across hole sizes and trajectories and proved successful with a low volume of offset data. The model results showed promise for enhancing drilling efficiency and reducing NPT.
In conclusion, this study shows that machine learning techniques can effectively predict changes in gamma trends at the bit by correlating these changes with drilling parameters to optimize drilling operations.
AI-Automated Codification-QC Model for Daily Drilling Reports
Authors: Benzine Abdallah , Tamaazousti Youssef , Vadakkekalam Sudheesh , Balakrishnan Sreejith , Chraibi Imane , Ezzeddine Dhaker , Arnaout Arghad , Khambete Shreepad Purushottam , Bimastianto Paulinus , Muhammad Shahid Duivala , Mughal Idrees Muhamamd , Murad Abdulrahman Abduljalil 
Published: November 2024

In the oil and gas industry, Daily Drilling Reports (DDRs) are vital to the well life cycle. Drilling supervisors meticulously record every detail of daily operations, ensuring accurate data is readily accessible for future use. This information is crucial for optimizing operations and future decision-making. However, manually preparing and coding these reports is both time-consuming and costly, introducing the risk of errors that can compromise data quality.
Having accurate data and correct classification of drilling activities are key to making decisions that boost drilling efficiency and cut costs. The precision of DDRs affects how well drilling operations can be analyzed and optimized. But, manual codification by the rig crew often leads to errors, with mistakes in 10% to 20% of entries. These errors stem from the difficulty of categorizing over 400 unique drilling activities, leading to wrong classifications that can disrupt operations and cause expensive errors.
This is where Quality Control (QC) comes in, playing a crucial role in making sure DDRs are accurate. However, QC faces similar challenges, requiring highly experienced engineers to spot and correct errors from the initial coding. These QC engineers need a deep understanding of drilling operations and must be able to pick up on subtle errors in many categories. The complexity and high skill requirements make the QC process as lengthy and prone to errors as the initial coding, highlighting the need for more reliable and efficient methods.
Analysis of Back-Scattered Laser Signal in Oil and Gas Pipelines
Authors: Talele Harshal 
Published: November 2024

Abstract
India boasts an extensive network of over 50,000 kilometers of cross-country pipelines dedicated to transporting oil and gas. These pipelines traverse diverse terrains, rendering them vulnerable to tampering and damage from third-party activities. Despite regular line patrolling by inspectors, incidents of pipeline tapping and damage continue to occur, primarily due to limitations in existing leak detection systems. Current systems, which rely on volume balance methods, are only capable of detecting leaks after they occur. This reactive approach fails to prevent damage or intrusion, leading to significant operational downtime and substantial financial losses from halted refinery operations and disrupted retail outlets. To address these challenges, Bharat Petroleum Co. Ltd has implemented an advanced Optical Fibre-based Pipeline Intrusion Detection System (PIDS). Unlike traditional methods, PIDS offers a proactive solution by utilizing dark fibre for detecting and localizing both unintended damages and deliberate acts of tampering. The system operates on the principles of Coherent Optical Time-Domain Reflectometry (COTDR), where a laser source is injected into the fibre, and backscattered signals are analyzed to identify disturbances along the pipeline. The PIDS system monitors micro-changes in the fibre’s shape caused by various activities, which affect the backscatter signal. This data is processed through sophisticated algorithms and compared against a library of known signatures to detect different types of disturbances, such as manual excavation, agricultural activities, heavy equipment operations, and explosions. The system’s ability to distinguish between these activities is enhanced by a Quick Response Team equipped with vehicles to address potential issues promptly. The implementation of the PIDS system has demonstrated its effectiveness in improving pipeline security and integrity. By enabling early detection and timely intervention, the PIDS system not only minimizes operational downtime and financial losses but also helps mitigate environmental damage. This technology represents a significant advancement in pipeline surveillance and offers valuable insights for other oil and gas industries aiming to enhance their security measures.
End to End Digital Corrosion Management Hub
Authors: Al-Ghazali Eithar , Al-Jabri Nawras S. , Albulushi Yahya Y. 
Published: November 2024

Abstract
The primary objective of the project was to digitalize the Corrosion Management (CM) system within the Asset Performance Management (APM) platform for oil and gas facilities, both offshore and onshore. The aim was to enhance process efficiency by reducing human intervention and error, and to provide a more robust and comprehensive data management system. The scope included the implementation of a new digital CM platform that integrates all recorded CM data, enabling direct and comprehensive analysis through predefined KPIs. This system is intended to monitor and evaluate the performance of corrosion controls continuously.
The new CM solution was implemented on a digital platform that includes handheld devices programmed to interface with the existing computerized maintenance management system (SAP). Technicians use these devices to collect data directly from barcoded equipment at specific TAG locations. This method allows for immediate data entry and upload into the APM software, reducing the need for manual data recording and entry. The platform utilizes a series of check sheet templates that list necessary indicators, ensuring that all relevant data is captured accurately and consistently.
The following are the categories of KPI used to ensure CM performance for Oil and Gas Facilities: Internal Corrosion Management: these KPIs defines the internal corrosion information and ensure reporting on timely mannerExternal Corrosion: these KPIs defines the external corrosion information related to Cathodic Protection and Coating.
The digitalization of the CM system has resulted in significant cost savings, approximately 127K USD annually, by reducing time spent on-site by contractors and staff, and minimizing paperwork. The reduction in manual data entry has also decreased the likelihood of errors compared to traditional paper-based methods. Furthermore, the system has improved the tracking of pending issues and concerns arising from site findings, facilitating more timely and effective responses.
This digital CM solution is unique in its integration of handheld devices with existing maintenance systems, which has not been previously recognized in the market. It has been nationally and internationally acknowledged, receiving appreciation from several clients, and has been recommended for broader implementation. The digital solution not only provides end-to-end digitalization but also innovatively enhances the corrosion management process, making it a pioneering initiative in the field.
Rapid Forecasting of Underground Hydrogen Storage Using Fourier-Integrated Hybrid Neural Framework
Authors: Omeke James , Misra Siddharth , Retnanto Albertus 
Published: November 2024

Abstract
Deep learning can significantly accelerate the simulation of the injection, storage and production processes in an underground hydrogen storage (UHS). By understanding complex system interactions, deep learning offers accelerated simulations, enabling the creation of an fast visualization, forecasting and optimization framework. Our research presents a novel data-driven approach leveraging deep learning to mitigate the computational challenges of high-fidelity underground hydrogen-storage simulations.
In this study, an innovative Fourier-Integrated Hybrid Neural Framework (F-IHNF) is used to create deep-learning-based surrogate models for field-scale hydrogen storage simulation. This framework combines Convolutional LSTM, 3D convolutions, and Fourier Neural Operators (FNO) for precise spatio-temporal analysis, focusing on hydrogen flow dynamics influenced by production and injection cycles. The deep-learning-based accelerated simulation workflow is developed and deployed on Fenton Creek gas reservoir model, segmented into 97×18×35 grid blocks with 61,110 active cells and a grid size of 121ft×136ft×2.8ft.
The newly developed 3D Fourier-Integrated Hybrid Neural Framework (F-IHNF) achieves an impressive 98% accuracy in blind test validation for both pressure and hydrogen saturation forecasting over a period of 2 years involving 3 production-injection cycles. In the data generation phase, 76 simulations of hydrogen storage in various realizations of Fenton Creek geomodel under various engineering parameters were generated over 48 hours, with each 24-month hydrogen-storage simulation averaging 38 minutes. In comparison, the F-IHNF takes 0.5 seconds to forecast the spatiotemporal pressure and saturation evolutions over 24 months. This marks a 5000-time speedup in the forecasting. It took approximately 2 hours to train the F-IHNF model using 60 training samples and 6 validation samples. By finely tuning key hyperparameters—including convolutional LSTM hidden channels, 3D convolutional channels, and the selection of Fourier modes in x, y, and z directions—the precision of F-IHNF was impoved. The use of a weighted mean absolute error for saturation and a normal mean absolute error for pressure resulted in validation MAEs of 0.002 and 0.004, respectively.
This study integrates a Fourier-Integrated Hybrid Neural Framework with deep learning for accelerated simulation of underground hydrogen storage. This approach will support faster analysis, visualization, forecasting, monitoring, and robust optimization, potentially aiding the adoption of underground hydrogen storage in the renewable energy sector for a sustainable future.