bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatially Resolved CFD-DEM Modelling and Experimental Validation for Enhanced Sand Retention Understanding in Standalone Screen Completions
Authors: Razak R. , Alosail M. S. , Musa K. I. , Gago P. A. , Chen Z. , Tyson S. , Rahman S. S. 
Published: October 2024

Abstract
This paper aims to enhance the understanding of sand retention mechanism in oil and gas wells through a coupled CFD-DEM approach and innovative laboratory experiments. The motivation stems from the economic impact of sand production, where standalone screens serve as cost-effective alternatives to gravel-pack completions. However, existing selection criteria are limited under certain operating conditions. This study addresses these limitations by focusing on the often-overlooked role of the deposited bulk filter bed on the screen, a critical factor in sand retention.
Utilising a spatially resolved CFD-DEM model, this study simulates the sand retention process, extending beyond basic parameters like the mass of sand particles produced. It incorporates a comprehensive analysis of filter bed growth, porosity, and permeability, which significantly impact the productivity index of the fluid rather than the screen itself. The permeability analysis includes both the numerical filter bed and the screen, inherently addressing screen plugging. A novel experimental methodology benchmarks the numerically derived single-phase permeability of the filter bed using micro-CT images of experimentally derived filter beds.
The findings demonstrate strong matches between the CFD-DEM simulations and laboratory experiments, validating the numerical model and showing consistent porosity and single-phase permeability of the filter bed. These results highlight that the dominant filter medium is the filter bed rather than the screen itself. This contributes to a more nuanced understanding of sand retention systems, with implications for optimising standalone sand screen selection criteria in screen-only completions.
The novelty of this research lies in its holistic approach to sand retention systems, considering the interplay between the filter bed and standalone screens. By addressing existing criteria limitations and incorporating a detailed analysis of filter bed properties, the presented CFD-DEM model provides a foundation for improving selection criteria. This model lays the groundwork for future analyses, exploring parameters to determine optimal conditions for maximising sand retention while maintaining hydrocarbon productivity in screen-only well completions.
Comparative Analysis and Suggestions for Sucker Rod Pump Working Condition Diagnosis Using Machine Learning Techniques
Authors: Sun Y. , Wang H. , Zhang X. , Yang J. , Wang C. , Shao J. , Zhao R. , He J. , Shi J. , Zhang X. , Jiang M. , Liao C. , Zeng X. , Ma G. , Wang S. , Hu D. , Yu Y. , Li Y. 
Published: October 2024

Abstract
This study explores the application of six state-of-the-art image classification models—ResNet, MobileNet, EfficientNet, ViT, EfficientViT, and MobileViT—in diagnosing sucker rod pump faults using dynamometer cards. The models, fine-tuned from pre-trained models developed on ImageNet-1k, are evaluated on a dataset of 11,941 dynamometer cards. Our findings demonstrate a significant inverse correlation between model accuracy and the number of trainable parameters, notably with EfficientViT achieving the highest accuracy (95.73%) despite having fewer parameters. This suggests that simpler, but more efficient models can outperform more complex architectures in specific tasks like SRP diagnosis. Through Grad-CAM visualization, we analyzed model predictions to identify critical features, highlighting ResNet's superior generalization ability and robustness. This work provides insights into model selection and data preparation, aiming to enhance the reliability and efficiency of SRP working condition diagnosis models.
Intelligent Approach for Estimating CO2-Brine Relative Permeability in Carbon Capture and Storage Process Based on Deep Learning: A Case Study of Aquifer Under the South China Sea
Authors: Wang Lian , Zhang Liang , Deng Rui , Wang Hehua , Zhao Xing , Kang Bo , Xu Bing , Jiang Liping , Deng Qi 
Published: October 2024

Abstract
Geological storage of carbon dioxide (CO2) in the brine aquifer is a dual-benefit strategy for mitigating greenhouse gases and advancing carbon capture and storage (CCS). Meanwhile, the gas-water relative permeability (RP) of CO2 and brine provides fundamental insights into flow behavior in the aquifer which plays an essential role in evaluating the carbon storage capacity. Consequently, establishing an economic and useful approach for obtaining the gas-water RP curve in the CCS process is urgently needed. In this study, an intelligent yet accurate model, called DL-MICP, for estimating the gas-water RP curve in the CCS process adopting deep learning (DL) techniques and experimental mercury injection capillary pressure (MICP). Specifically, the DL methods and experimental data including the Gramian angular field (GAF), the convolutional long short-term memory (ConvLSTM), self-supervised learning framework, curves of mercury injection and withdrawal, and distribution of pore size are utilized in the DL-MICP approach for evaluating CO2-brine RP curves. To validate the superior and effective performance of the DL-MICP approach, the experimental data of aquifer samples under the South China sea is applied, in which 38 small-sample data spaces are utilized for training and 9 samples for testing. It is found that the average errors are 3.32% and 2.58% for the CO2 and brine RP curves, respectively. In addition, the superior accuracy of the DL-MICP method is demonstrated through the comparative study against classical convolutional neural network (CNN) and long short-term memory (LSTM) models. This study combined deep learning techniques and mercury injection capillary pressure to efficiently realize the rapid intelligent prediction of CO2-brine RP curves that facilitate the evaluation of the CO2 injection capacity and flow behaviors in the CCS process. To this end, this intelligent DL-MICP method achieved the goal of obtaining CO2-brine RP curves in excellent accordance with experimental data without costly and time-consuming experimental measuring.
Effect of Rough Surface and Moisture Content on the Competitive Adsorption off CO2/CH4 Mixtures in Orthoclase Nanopores with the Insight for Carbon Storage
Authors: Hu Hao , Liu Yueliang , Rui Zhenhua , Qian Cheng , Kai Du , Mao Zhiqiang , Jiang Hongzhi , Wang Hao , LI DEQIAN 
Published: October 2024

Abstract
Injecting CO2 into shale reservoirs can enhance shale gas recovery and store CO2 to help mitigate global warming. Orthoclase is a key mineral in shale reservoirs. To understand the competitive adsorption mechanism of CO2/CH4 mixtures in orthoclase nanopores under various conditions, we employed molecular simulations. Using three different orthoclase nanopore models—dry, water-containing, and rough—we studied the adsorption behavior of CO2, CH4 single component gas and their binary mixtures in orthoclase pores with the Grand Canonical Monte Carlo (GCMC) method. We comprehensively considered the effects of temperature (303.15-393.15 K), pressure (0-30 MPa), gas molar volume fraction, water content, and surface roughness on the adsorption characteristics of gas molecules in 3nm orthoclase pores. In dry orthoclase nanopores, the adsorption of CO2 decreases while CH4 adsorption increases with rising temperature and pressure, with CO2 being adsorbed more than CH4. CO2's interaction energy is primarily electrostatic, whereas CH4's is mainly van der Waals, leading to different patterns in their adsorption heat curves. The gas mole fraction has a much greater effect on CO2 in binary mixtures compared to CH4. In water-containing pores, the adsorption capacity of both gas components introduced into the pores of orthoclase is inhibited, with water having a significantly greater inhibitory effect on CO2 than on CH4. At 10 MPa, the adsorption heat of CO2 is 12.684 kcal/mol when the water content is 0, and it decreases to 3.273 kcal/mol when the water content increases to 6%, a decrease of about 74%. Surface roughening increases the retention of CO2 and CH4 in surface "depressions," significantly influencing CO2 storage and CH4 recovery. This study elucidates the mechanism of competitive gas adsorption in orthoclase-rich shale reservoirs, offering theoretical support for the advancement of CO2 storage and natural gas enhanced recovery (CSEGR) technologies.
A Comprehensive Machine Learning Algorithm Based Methodology for Intelligent Water Injection Optimization - Case Study of D Field in Asia
Authors: Li X. , Zhang J. , Liu H. , Luo X. , Du K. , Hou B. , Wang L. 
Published: October 2024

Abstract
In the oilfield development by water flooding, lower injection rate results in insufficient reservoir energy. Conversely, a higher injection rate usually results in an early breakthrough and rapid water cut development, which causes serious invalid and low-efficiency water cycling. The development of fine separated layer water injection technology which improves the water injection effect can better adapt to multi-layered reservoir and can alleviate the interlayer conflicts. Meanwhile, more accurate injection allocation is required to achieve good performance. Fine optimization of suitable water injection rate of each well and interval is critical for effective and successful water injection.
This paper presents a fast and intelligent water injection allocation method based on comprehensive machine learning algorithm. The evaluation indexes of injection performance are established by integrating dynamic production data, such as injection-production ratio, water consumption rate, water cut, injection intensity, daily oil production capacity. According to the evaluation indexes, it is necessary to determine the water injection adjustment to achieve better effects. Random forest algorithm (RF) is applied to determine the allocation type for wells and intervals, such as strengthened, controlled, and unaltered wells and intervals. Subsequently, the multi-objective simulated annealing algorithm (MOSA) is chosen to specify the water injection rate and proportion for each well and interval that needs to be adjusted. This intelligent water injection optimization methodology is formed to realize scientific and reasonable water injection allocation.
The water injection plan optimized through intelligent methodology was successfully implemented in a pilot project in the D oilfield which is a multi-layered sandstone reservoir in Asia. The selection and optimization of plan is as the goal of high oil and liquid production and low water cut. Dynamic data of the pilot, collected over eight months after applying this optimal plan, indicate that the single well oil production capacity increased from 1.42 to 1.5 tons per day, reflecting an increase of 5.6%. The water cut decreased from 96.33% to 95.93%, showing a slight decrease of 0.4 percent without any subsequent increase. The injection-production ratio improved from 1.04 to 1.05 with an increase of 0.01. The intelligent water injection optimization method has yielded favorable development effects.
The new method promotes reasonable allocation and water injection development to fine and intelligent. It also provides a methodology and reference case for reservoir and production engineers to develop other similar fields.
Optimizing Gas Condensate Subsea Tiebacks: Lessons Learned from Unload to Host Operations
Authors: Zhang J. , Shepherd A. G. 
Published: October 2024

Abstract
Gas condensate subsea tiebacks increasingly favor Unload to Host (U2H) operations over traditional Unload to Rig for enhanced execution efficiency and cost reduction. They further eliminate the need for temporary floating production units to partially unload wellbore liquids/solids and introduce first gas with flaring. While U2H brings significant benefits like HSE risk mitigation, it also poses integrity and operability challenges to new and existing facilities. This paper will present case studies and lessons learned from U2H operations conducted on multiple gas condensate subsea tiebacks. Key focus areas include understanding the impact of U2H fluid composition on LNG production, selecting subsea preservation liquids to balance hydrate and corrosion risks, and optimizing liquid handling and inventory management at host facilities. Additional considerations cover solids management during wellbore and pipeline transport, solids filtration at host facilities, and effective production chemistry management with a tailored sampling plan and temporary chemical injection package readiness. The outlined learnings emphasize the critical need for upfront planning on U2H operation, advocating an integrated multidisciplinary approach to influence new facilities designs as well as to minimize last-minute changes during execution. Overall, the aim of this work is to highlight operational excellence for flow back to host where asset integrity and production optimization can assist to unlock value if risks are well-understood and managed.
Dehydration Unit Failure in CCS Hubs – Managing Risk through Detailed Analysis
Authors: Shen R. , Henriksson J. , Stokes S. 
Published: October 2024

Abstract
Carbon Capture and Storage (CCS) hubs are considered a cost-effective means for industries to reduce carbon dioxide (CO2) emissions through access to shared infrastructure. However, impurities in the emitters’ CO2 streams alter the physical properties and behaviour of the fluid, introducing integrity risks. CO2 is highly corrosive in the presence of agitated bulk condensed water, with carbon steel pipework corrosion rates potentially ranging from 0.5 mm/year to greater than 10 mm/year. The primary control mechanism is to set a specification on impurity levels, including water. Regardless, individual emitters, albeit infrequently, will experience control failures that result in off-specification fluids entering the network. This manuscript considers a CCS hub dehydration plant failure and demonstrates how detailed simulations facilitate effective risk management.
A dehydration upset will result in wet CO2 being fed into the dry (previously on-specification) carbon steel pipeline network. Accurately simulating the migration of the wet CO2 and the propensity for free water drop-out are critical to establishing an appropriate operator response. The first challenge is to determine the solubility of water in the CO2-rich fluid. A literature review of experimental data and equation of state performance for water solubility in CO2 is presented, with commentary on impurity impact. The second challenge is to model the water dew point line, determine the quantity of free water formed as a function of pipeline operating conditions, and understand the resultant extent of water migration. This work demonstrates the use of a thermohydraulic simulator to track the concentration of water through the system over time.
Different simulation scenarios of off-specification events are assessed, and the study demonstrates that modelling the exposure to risk of free water driven corrosion in the CO2 pipeline is subject to uncertainty and highly sensitive to model setup. It is therefore important to understand the simulation models and simulators in use as different interpretations of simulation results may lead to different operator responses, making an informed modeller / engineer essential to determining the risk during off-specification events and hence advise the appropriate response. The results also show that the appropriate action depends on the circumstances, making predictive simulations critical. A design analysis, as presented here, can only investigate a limited number of scenarios, leading to the conclusion that subsequent operations would benefit from an online flow management tool (FMT) to provide situational awareness and track the water distribution through the entire network. An FMT will provide the operator with far more information than physical metering alone to inform risk-based control strategies.
Performance Analysis of Eco-Friendly Palm Oil Biodiesel as a Replacement for Diesel Oil in Drilling Mud Applications
Authors: Bataee M. , Wong S. , Khur W. S. , Bennour Z. , Mostofi M. , Hamdi Z. 
Published: October 2024

Abstract
This study investigates the potential of palm-based biodiesel mud as a feasible alternative to conventional diesel-based mud for drilling operations. The rheological properties, including mud density, viscosity, plastic viscosity (PV), apparent viscosity, gel strength, yield point (YP), and filtration properties, were measured and compared. Eight mud samples were prepared using diesel and palm-based biodiesel oils with various additives. The results indicate that palm-based biodiesel mud generally has higher densities and comparable rheological properties to diesel-based mud. While the yield points of diesel-based mud samples were slightly higher, the differences were not substantial except in specific cases. The study also found that palm-based biodiesel mud exhibited favorable filtration loss characteristics and consistent gel strength values, indicating its suitability for maintaining wellbore stability and effective cuttings transport. The minor differences in plastic viscosity and the consistent performance in mud filtration tests further support the viability of palm-based biodiesel mud. Overall, this research demonstrates that palm-based biodiesel mud can be a promising and environmentally friendly alternative to diesel-based mud in drilling operations. Future studies should focus on optimizing the mud's density and conducting field tests under various drilling conditions to validate its practical application.
Evaluation of Sand Screen Performance Through Correlation Between Air Velocity, Sand Injection Rate, Test Duration, and Screen Type with Slot Opening Size in Sand Erosion Testing (SET) Equipment
Authors: Maoinser M. A. , Zainal A. Z. , Tham K. M. , XianLung I. C. , Madon B. 
Published: October 2024

Abstract
The aim of this study is to assess the correlation between air velocity, sand injection rate, test duration, screen type, and slot opening size of the sand screen during testing in the sand erosion test (SET) equipment. Additionally, a correlation will be developed at the conclusion of this study. The Sand Erosion Test (SET) equipment is utilized to assess the effectiveness of the sand screen based on its slot opening size. Several parameters are considered, including air velocity (ranging from 10 to 200 ft/s), sand injection rate (between 1.5 and 3.0 g/min), and test duration (0 to 48 hours). Prior to testing, the slot size of the sand screen is verified to match the supplier-provided value. Post-testing, the sand screen size is re-evaluated after both the 2-hour and 48-hour intervals. Testing ceases if the slot size opening exceeds 50 μm. Each slot's opening size is measured at three points. Data is then recorded and analyzed using Design Expert 12 software. The significance of the developed model is assessed through Analysis of Variance (ANOVA), and a mathematical model is derived from this analysis. After conducting the test, it was noted that there exists a linear relationship among the factors—air velocity, sand injection rate, time, screen type, and slot opening size. The model derived from the data analyzed in this study, as produced by the Design Expert software, demonstrates significance and accuracy. Additionally, it indicates that the test duration and screen type exhibit greater significance in influencing the increase of slot opening size compared to other factors like air velocity and test duration. The correlation between these factors and the response has been developed and is presented as a novelty within this study. This study established a correlation among air velocity (A), sand injection rate (B) and test duration (C).
Pioneering Perforation, Wash and Bismuth Plug for Groundbreaking Revolution and Decommissioning and Abandonment
Authors: Talapatra Didhiti , Kuijken Chris , Carragher Paul 
Published: October 2024

Abstract
This paper presents the first Plug and Abandonment (P&A) operation in Malaysia that utilized Bismuth Alloy as an innovative abandonment material. The operator employed a novel approach by placing the Bismuth Alloy Barrier not within a milled window, but rather in the annulus against the formation through perforations that had been meticulously washed to remove the original cement.
The choice of Bismuth Alloy P&A barrier over conventional materials was motivated by the desire for a more efficient and compact solution. The Bismuth Alloy barrier, requiring only 6 feet in length, provides superior integrity compared to a standard 120-foot conventional P&A barrier. This significant reduction in length enabled the operator to create a shorter window, a strategic advantage given the milling efficiency limitations of the hydraulic workover rig used for the P&A operations. The operator further innovated by replacing the planned 20-foot section milled window with a 6.2-foot casing perforation run, followed by precise cement removal using high-efficiency washing techniques. Subsequently, three Bismuth Alloy plugs were installed across the zone, marking a paradigm shift in operational methodology.
Firstly, this paper highlights the distinctive sealing mechanism of Bismuth Alloy, demonstrating how its expansive forces during the solidification process provide enduring benefits as a P&A barrier. Secondly, the paper delves into the business case for Bismuth Alloy P&A, contrasting the conventional approach with the innovative Bismuth Alloy P&A method featuring a shorter 20-foot section milled window, and ultimately introducing the groundbreaking 6-foot Perf Wash Bismuth P&A. This evolution represents a significant leap in P&A operational efficiency, resulting in reduced rig time, lower Health, Safety, and Environment (HSE) and operational Non-Productive Time (NPT) exposure, decreased emissions, and, most importantly, enhanced barrier integrity. Thirdly, the paper details the comprehensive Perf Wash Bismuth business process. This innovative system involved the installation of three Bismuth Alloy Plugs, covering the 13-3/8" perforated casing wellbore and the 13-3/8" × 18-1/2" open hole annulus. The results illustrate a transformative approach to P&A operations, underscoring the efficacy and benefits of utilizing Bismuth Alloy in well abandonment. This pioneering operation in Malaysia sets a new standard for future P&A projects, emphasizing the importance of adopting advanced materials and techniques to achieve optimal well integrity and environmental safety.