bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Approach for Hydraulic Fractures Modelling Using Discrete Fracture Network (DFN)
Authors: MousaviMirkalaei S. M. , Gautam S. , Ghadami N. , Edmondson M. D. 
Published: February 2025

Abstract
Hydraulic fracturing, as a technology that can effectively enhance the permeability of unconventional oil and gas reservoirs, is widely used in the development of unconventional oil and gas resources. In many unconventional fractured reservoirs, dual porosity with logarithmic space local grid refinement is used to model hydraulic fracture. However, this method is numerically expensive, and it is not practical with numerous wells and several fracture stages. A new approach of using Discrete Fracture Networks (DFN) modeling technology is proposed in this paper which can effectively model flow in hydraulic fracture and enhance the runtime compared to the previous method. A reference solution with small fracture aperture (0.001 ft) and logarithmic grid was built to get the true numerical solution and then a few template models with 2 ft fracture width were created and matched to the reference solution. Finally, the DFN model was built and results were matched to true solution and then sensitivity analysis is done for different host block and DFN aperture.
A decent match was achieved between the conventional approach of hydraulic fracture modeling and using DFN. The results show that refinement makes a small impact on the initial match and the amount of refinement required as a function of the matrix permeability. For lower reservoir &lt; 100 nD (0.0001 mD), it is required 7×7×1 or 9×9×1 refinement based on accuracy and for higher perm &gt; 0.1 mD, there is no need for refinement. There is no difference between Aperture of 0.1 ft and 0.001 ft however once aperture increases to 1 ft, it is required to increase the host block size, and this reduces the quality of match. For low porosity of DFN greatly increase run-time so if volume needs to be preserved, and the best idea is to keep DFN Aperture small. The new approach has been successfully used for a template model and the runtime enhanced by 10 times faster compared to reference solution which has a big effect on the modeling of unconventional reservoir in the future.
Complex Risk Assessment of Offset Wells Using Cognitive Analysis
Authors: Parmar H. S. , Kiran BP S. , Singh M. , Gurusamy S. K. 
Published: February 2025

Abstract
Offset Wells Analysis (OWA) involves a detailed study of drilling and completions issues that occurred in historical data. Risk assessment comprises a study of all such issues including their causes and resolutions with the aim of well design that mitigates such non-productive time (NPT) with the final aim of well delivery on time and budget. Such issues include stuck pipe, wellbore instability, lost circulation, fishing and other incidents – some of which cause complete-or partial delays of field operations.
Details of issues are described in daily drilling reports (DDRs) including time stamped activity descriptions. Human experts currently study a series of activity descriptions detailing which type of NPT occurred at particular depths and what were the precursor contributors, if any, for these incidents. The subsequent activity descriptions also detail incident resolution. Human study of this NPT is time consuming, laborious and of sub-optimal scale.
Significant time savings and scope expansion is possible if a machine could intelligently understand and identify the activities described in DDRs, including the sequences of non-productive time. Cognitive knowledge-based computing can successfully deliver this outcome. Such a cognitive system identifies about 150 drilling and completions activities and events including 14 NPT event types. It also studies drilling activity sequences and delineates phases such as drilling, casing, cementing, fishing and others. It has successfully delivered more than 50% time and cost savings by automatically classifying activities and identifying Phases and Sections for many hundreds of wells at more than 90% accuracy.
DDR information from the Volve Field (24 wells) in the North Sea was processed by the cognitive system and individual NPT incidents were identified automatically. The system studied the sequences of activities across full-well depths and delineated drilling Phases and well sections. It applied logic and reasoning to the activity sequences before and after the occurrence of NPT and correctly understood and identified the start and end of contributing sequences to NPT. For example, sequences of lost circulation and well bore instability were identified before the occurrence of stuck pipe. Also, the start and end of several fishing operations was accurately identified via cognitive analysis.
This system heralds the ability of machines to study complex industrial activity such as drilling and completions described in natural language and to apply reasoning and logic to automatically, and accurately provide quantitative analytics for faster well design as well as an expanded study of risk in historical wells, leading to lower field downtime in future.
Optimization of Completions Using Inner String Metal Expandable Packers: A Case in Production and Injection Wells
Authors: Cayao J. J. R. , O’Reilly T. , Ismail A. , Kolakalapudi M. K. , Tea K. , Adeyeba A. 
Published: February 2025

Abstract
Several challenges are involved in the design and construction of wells, especially in the initial phase of field development. Oftentimes, wells penetrate a number of geologic targets that must be effectively isolated. In order to maximize the recovery in any hydrocarbon field development, well completions must be designed effectively and should last for a number of years while adapting to ever-changing conditions (Bellarby, 2009).
Uncertainties concerning early water breakthrough, gas cusping, and late-life souring have been identified as just a few of the main challenges that must be addressed by the multi-zone completion design for the field offshore Brunei, operated by Brunei Shell Petroleum (BSP). Furthermore, an effective zonal isolation (ZI) is necessary to avoid any potential crossflow between zones over the life of the well for the said multi-zone completions.
Considering the challenges identified, an Inner String Packer (ISP) must be incorporated in the completion design. ISP must be set inside cemented and perforated liners to complement controlled downhole production and ensure effective reservoir management.
A Metal Expandable Packer (MEP) capable of passing through perforation burrs without the need for a dedicated wellbore clean-out or deburr run has been designed, tested, and qualified as an ISP to optimize the completion design and operations.
Driven by the increasing associated costs in drilling and completing a well, the industry has always been on the lookout for an enabling technology. This paper focuses on the design considerations, planning and execution of a recently completed drilling campaign offshore Brunei. An innovative MEP technology utilized in a single-string, multi-zone completion is presented, and how the above-mentioned novel MEP design optimized the completion design while reducing associated rig time and cost over the whole drilling and completion campaign.
The improved functionality of the MEP in multi-zone systems presented in this paper highlights not only the substantial cost reduction, but also the significant benefits which reduced operational constraints and complexity.
Leveraging Seismic Inversion for Enhanced Reservoir Characterization and Field Development Planning: A X Field, Sarawak Basin Case Study
Authors: Yong C. L. , Aly O. , Kholiq A. , Murineddu A. , Tan S. , Fahmi F. , Zainuddin N. , Phonphetrassameekul N. 
Published: February 2025

Abstract

Objectives
This paper presents a study to understand the reservoir properties distribution from seismic data and to enhance the seismic interpretation model for well planning. The objective of the study is to minimize the drilling risk associated with the high-rapid spatial heterogeneity and compartmentalization of the current reservoir rocks. Seismic inversion and rock physics are powerful tools that deliver information about intra-well rocks’ elastic attributes and reservoir properties such as porosity, saturation, and rock lithology classification.
The paper presents detailed and comprehensive technical workflow of a surface seismic and rock physics inversion study that has been carried out on the current project to characterize the reservoir properties from the seismic.

Challenges
There are several challenges related to the characterization of reservoir rocks. The reservoir rocks are affected by a series of complex faulting systems. In addition, the reservoir rocks are characterized by a high-horizontal and vertical heterogeneity of its reservoir properties distribution (porosity, saturation, and rock facies), which is challenging to resolve using seismic imaging techniques alone.
It was important to go beyond seismic imaging techniques to better characterize the reservoir rocks in terms of reservoir properties such as effective porosity, water saturation, and rock classification.
Other challenges related to the acquired data were also encountered: Geophysical logs, including sonic and density logs, measure rock properties near the borehole. The reliability of these logs is affected by borehole diameter and shape, as well as drilling fluid invasion. Acoustic well logs need to be conditioned before using them in geophysical analysis because any error is going to be propagated through the subsequent processes.
Surface seismic data quality is impacted by residual noise, gather misalignment, and improper amplitude balancing. To optimize seismic data for inversion studies, processes including principal component analysis (PCR), nonrigid matching (NRM), and offset spectral matching (OSM) are applied to improve the quality of seismic data. Spectral balancing was also applied to maintain seismic amplitude fidelity.
Enhance Well Mastering with Machine Learning-Based Entity Matching
Authors: Zhang Xiaoniu , Ludena Teoty Alexandra Angulo , Shumaker Niven 
Published: February 2025

Abstract
In the oil and gas industry, data is sourced from multiple providers and software systems, covering subsurface, drilling, completion reporting, and well and reservoir analysis. Integrating this data for complex analysis and decision-making presents a persistent challenge, particularly in maintaining accurate and consistent well data (well mastering) due to inconsistencies across various sources. Well entity matching across different data formats, standards, and quality levels often leads to incomplete or incorrect data consolidation when relying on traditional methods like unique identifiers [e.g., well unique identifier (UWI)] or rule-based systems. These conventional approaches often suffer from low recall and poor adaptability to new or evolving data patterns, causing valuable information collected over the years to be overlooked or lost.
To overcome these limitations, we propose a machine learning (ML) approach that enhances well entity matching by improving flexibility, adaptability, and coverage. Our solution introduces two ML-based processes designed to predict well entity matches with confidence scores based on well-defined attributes: Pretrained ML model for entity matching: This model is meticulously developed to enhance matching efficiency, coverage, and accuracy across a variety of data sources, ensuring robust performance even in complex scenarios.Active learning for enhanced precision: Incorporating client-specific expertise, this iterative active learning process enables the model to refine its accuracy by leveraging domain knowledge and client-provided labels, significantly boosting both the coverage and quality of entity matching.
Our initial results demonstrate that our machine learning models outperform traditional rule-based methods, significantly improving recall and coverage while maintaining strong precision. This approach enhances the well mastering process by providing scalable, efficient, and accurate data integration for business applications. The most notable benefit of this technology is the considerable improvement in Recall and F1 scores.
Quantitative Evaluation of The Influence of Long-Term Water Drive on Pore Structure in Different Types of Reservoirs
Authors: Chen Mingyang , Hou Yawei , Li Wenbin , Wang Pengfei , Fan Jiale , Liu Xiaohan 
Published: February 2025

Abstract
The P Oilfield in the Bohai Sea has now entered the later stage of development, with a rapid increase in water cut and an accelerated decline rate. Within the loosely consolidated sandstone reservoirs, long-term water drive has caused changes in the pore structure, further exacerbating reservoir heterogeneity. To quantitatively characterize this impact, a novel experimental method utilizing high-speed water drive of rock cores at different pump rates, coupled with monitoring using nuclear magnetic resonance (NMR) imaging, has been proposed. This method allows for a quantitative evaluation of the changes in pore structure in the loosely consolidated sandstone caused by long-term water drive. The study has identified three classes of reservoirs and their respective patterns of changes in pore-throat size at different water drive rates and stages, as well as the mechanisms affecting pore-throat connectivity. The research findings indicate that in Class I and Class II high-quality reservoirs, when the flow rate exceeds 2 ml/min, water drive leads to the detachment of particles from the framework, resulting in an enlargement of the large pore-throat radius and a reduction in the small pore-throat radius due to particle retention and plugging, thus intensifying the heterogeneity. Ultimately, by displacing the rock samples at a flow rate of 6 ml/min to 1000 PV, the median pore-throat radius increased by 20% to 70%. In Class III reservoirs, by displacing the rock samples at a flow rate of 4 ml/min to 500 PV, pore-throat plugging occurred, resulting in a 90% decrease in the overall median pore-throat radius and a deterioration in pore-throat connectivity. This study enables the quantitative evaluation of the long-term impact of water drive on reservoir pore structure, provides a clear understanding of the water drive intensity in different types of reservoirs, and helps mitigate the effects of water injection development on reservoir heterogeneity. It offers new insights for the development of similar loosely consolidated sandstone reservoirs.
Machine Learning-Based Model for Prediction Permeability in Porous Media: Method and Application to Unconventional Reservoirs
Authors: Li Peiyu , Song Zhaojie , Song Yilei , Han Xiao , Wang Jiaqi , Jia Zhihao , Ren Haochen 
Published: February 2025

Abstract
Accurately predicting permeability in porous media is crucial for various engineering fields, including petroleum engineering, geology, and environmental science. Unlike conventional reservoirs, shale reservoirs predominantly feature micro- to nano-scale pores, making permeability prediction challenging and difficult to obtain through experimental methods. This research presents an innovative model based on machine learning to address these challenges. By leveraging data-driven approaches, this work establishes a workflow for predicting permeability in porous media. The study employs a hybrid CNN-BiLSTM-Attention model, incorporating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism to predict permeability using pore-throat parameters. The dataset, generated through a Quartet Structure Generation Set method and pore network models, consists of 600 randomly created porous media samples. Key finding include: (1) the proposed CNN-BiLSTM-Attention model outperforms traditional models (MLP, CNN, and CNN-BiLSTM), with RMSE, MAE, and R2 values of 0.0076, 0.0058, and 0.97, respectively; (2) the most influential factors affecting permeability are the mean pore radius, mean throat radius, and porosity; and (3) the model successfully predicts permeability in shale oil reservoir samples, closely matching with experimental and pore network model results. This study offers a highly efficient and accurate machine learning model for permeability prediction, particularly suited to unconventional reservoirs, providing potential applications in reservoir evaluation and enhanced oil recovery strategies.
Maximizing Sales From a 40-year-old Integrated Oil-gas-condensate Hub
Authors: Zulhaimi Muhammad Hazwan Afiq 
Published: February 2025

Abstract
This paper illustrates how understanding facility limit, remaining subsurface potentials production profiles as well as optimizing product flow routing in an integrated oil-gas-condensate facility complex for mature fields are key to protecting and further optimizing base volumes while enabling volume gains from new wells. Low-cost and reliable execution of surface modifications via control valve, choke reinstatement and well mix optimization with strong integrated subsurface-surface network understanding are key ingredients in identifying and realizing the value from a potential unoptimized network.
The evaluation includes the use of integrated network simulator and wellbore hydraulic model software application on potential gains from system pressure reduction; followed by assessments to address subsurface-surface uncertainties from the existing network comprising Oil and Non-Associated Gas wells, and impact to network well mix and Gas lift optimization.
Well level performance from well tests, network level performance from platform to separator outlet, gas lift and key surface equipment e.g., separators, pipelines and compressors history were some of the information used for the evaluation. The evaluation recommends splitting oil and gas wells to prevent backpressure impact, optimize the system pressures and utilize the full ullage of the integrated network gas compressor. Based on actual observation, the system pressure reduced by 26% which results in 8% of incremental oil gain.
The integrated network simulator model recommends reduction of gas lift consumption, selective wells to shut in and choke down as we add more wells into the network to manage backpressure impact. Gas lift header pressure controls and well production chokes were reinstated and resized appropriately to enable production optimization. As a result, about 9% of total oil production was recovered from the optimized network, and a further 3% increase was observed from new wells post-network optimization.
This paper will illustrate the challenges in a 40-year-old complex integrated network facility, with combinations of oil, non-associated gas and gas cap blow down production and approaches taken to address the challenged and continue to maximize overall production.
Dissolving the Fracture Space: Modeling the Effects of Reactive Fluids During CO2 Storage
Authors: Rached R. M. 
Published: February 2025

Abstract
Carbon capture and storage involves injecting reactive fluids into subsurface reservoirs, which induce dissolution within fractures. Mineral removal from contacting asperities alters the fracture geometry and affects its mechanical and hydraulic properties. To better understand these processes, this study developed a coupled hydro-chemical numerical model to simulate the evolution of the fracture pore space due to dissolution.
The finite element method (FEM) is used to simulate the advection-diffusion-reaction of the reactive fluids within a fracture. Fluid flow satisfies the Navier-Stokes law for an incompressible fluid. Mineral dissolution occurs at fracture walls and asperities. The geometric evolution of the fracture due to dissolution is simulated using a moving mesh, where the moving mesh velocity is determined by the total volume of dissolved solid at each element. The simplified fracture aperture is simulated in 2D. The applied boundary conditions simulate the injection of different concentrations of acid under various flow rates.
The competing rates of advection, diffusion, and reaction control the evolution of the fracture. The results show that different dissolution regimes are possible for the same initial geometry, with the dimensionless Damkohler number (tadvection/treaction) capturing these variations. The study found that complex fracture topologies generate heterogeneous flow fields, with fast advection-control in the center of the fracture coexisting with diffusion-control in slow regions. The findings suggest the need for future work using 3D simulations and coupled mechanical simulations to better understand these phenomena.
This study presents a novel, coupled hydro-chemical FEM model tool to simulate fracture geometry evolution due to dissolution. The results advance the current understanding of the effects of different initial geometries and flow conditions on the dissolution process. The tool reveals the complex interplay of advection and reaction rates that control fracture space evolution, which has important implications for carbon capture and storage operations.
Research and Application of Gravel Packing Sand Control Technology for Horizontal Well Segments
Authors: Xu Fengxiang , Zheng Jiuzhou , Zhang Lun , Ji Zhengxin 
Published: February 2025

Abstract
For loose sandstone long horizontal sections, especially those with shale intervals or large permeability differences, a single-stage generalized gravel packing can easily lead to sand bridging and premature termination of the gravel packing process, resulting in unsatisfactory packing effectiveness. Moreover, for edge-bottom reservoirs, generalized packing can cause serious interlayer crossflow and interlayer interference, making water control more challenging. To address the sand control and water control requirements of horizontal well gravel packing, a segmented packing technology and string have been proposed and developed. The segmented gravel packing technology string for open-hole horizontal wells consists of wellbore tools and service tools. The wellbore tool string incorporates bypass isolation packers for horizontal well segmentation, while the service tool string includes bypass isolation setting tools and bypass valve closing tools. This process involves setting and sealing before packing and finally closing the bypass channel, effectively isolating sand control segments inside and outside the casing. When combined with water control tools (such as AICD, ICD, intelligent production allocation tools, etc.) on sand control screens or central pipes, segmented sand control and water control technology can be achieved.
The core of the segmented gravel packing sand control technology for horizontal wells lies in the bypass isolation packer, which consists of an expandable element and a swellable element. The hydraulic expandable element ensures effective isolation during the packing process, and the swellable element can freely expand after packing to maintain long-term sealing effectiveness. Additionally, the packer is equipped with a bypass channel to allow passage of the packing sand, which can be closed after completion of the packing.
Following the successful development of this technology, it has been applied in over 20 wells in field operations. Field application results have shown stable mechanical performance of the entire string, ensuring safety and reliability during construction. Notably, in well A256H of the Bohai Oilfield, the first successful application offshore in China was achieved. The open-hole section was 246m long, divided into two segments for sand control and water control. The packing efficiency reached 100%, and combined with water control tools, segmented sand control and water control were achieved, resulting in a 5% reduction in water cut.