Geoenergy Science and Engineering最新文献

{"title":"Effect of surfactants C12PO6 and SW320 on oil/CO2 minimum miscibility pressure of unconventional liquid reservoirs - molecular dynamics simulation study","authors":"Zhenzhen Dong ,&nbsp;Tong Hou ,&nbsp;Weirong Li ,&nbsp;Changbin Hou ,&nbsp;Chenhong Guo ,&nbsp;Zhanrong Yang ,&nbsp;Xueling Ma","doi":"10.1016/j.geoen.2025.213863","DOIUrl":"10.1016/j.geoen.2025.213863","url":null,"abstract":"<div><div>CO₂ flooding has been identified as an effective method for enhancing the recovery rate of unconventional oil and gas. However, in many oilfields, the miscibility pressure of CO₂ with crude oil exceeds the oilfield pressure, preventing them from achieving miscibility. This impedes the desired recovery outcomes. Surfactants present a solution to this challenge, as they can reduce the miscibility pressure between CO₂ and crude oil, thereby elevating the recovery rate. Yet, the microscopic dynamics of how surfactants modulate the MMP in CO₂ flooding within ULRs remain under-explored.</div><div>This research aims to delve into this gap, using molecular dynamics to elucidate the underlying mechanisms and potential benefits of surfactant inclusion in CO₂ flooding applications for unconventional reservoirs.</div><div>Our study, rooted in molecular dynamics, seeks to demystify these dynamics and understand surfactants' role more profoundly. Delving into the CO₂-n-decane system, we discovered that C₁₂PO₆ and SW320 significantly alter the interfacial width by forming a molecular film, which enhances CO₂'s solubility in crude oil. Notably, SW320 emerged as more potent than C₁₂PO₆ in this regard. Both C₁₂PO₆ and SW320 managed to reduce the MMP of the CO₂-n-decane system by more than 15 %. However, in terms of cost-effectiveness, C12PO6 offers a compelling balance between performance and affordability compared to SW320. Further insights revealed that the structure of the C₁₂PO₆ surfactant plays a crucial role in determining its MMP reduction capacity. Intriguingly, the addition of low carbon alcohols, especially n-pentanol, enhances the C₁₂PO₆ surfactant's surface activity, underscoring its superiority over ethanol in reducing interfacial tension.</div><div>In essence, this research offers a microscopic lens to view the intricate dance of surfactants in CO₂ flooding within ULRs. Our findings provide a robust framework for refining recovery strategies in unconventional reservoirs, potentially transforming the landscape of CO₂ flooding methodologies to ensure more sustainable and efficient oil and gas extraction.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"251 ","pages":"Article 213863"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D geological modeling and reservoir production numerical simulation of multiple scenarios in fractured-karstified carbonate reservoirs using high-resolution outcrop characterization","authors":"Simone N. Corrêa ,&nbsp;Francisco H.R. Bezerra ,&nbsp;Vincenzo La Bruna ,&nbsp;Mauro Ida ,&nbsp;Rômulo C. Stohler ,&nbsp;Mathieu A.G. Moriss ,&nbsp;Tarsila B. Dantas ,&nbsp;Fabio L. Bagni ,&nbsp;Renata E.B. Araújo ,&nbsp;Claudio Roisenberg ,&nbsp;Francisco P. Lima-Filho","doi":"10.1016/j.geoen.2025.213866","DOIUrl":"10.1016/j.geoen.2025.213866","url":null,"abstract":"<div><div>This study presents a multidisciplinary workflow to improve understanding of fractured and karstified carbonate reservoirs. The study seeks to uncover geological mechanisms behind fracturing and karstification by integrating analog outcrop fieldwork with regional correlation. This knowledge is crucial for building more accurate geological models and improving the management of these complex reservoirs. We use the Jandaíra Formation in the Potiguar Basin, Brazil, as an analog for fractured and karstified carbonate reservoirs. Our results focus on the Furna Feia cave, characterized by dissolution dome structures and NW-SE-trending conduits. The regional stratigraphic analysis identified lithofacies patterns and correlated system tract cycles to map the most karstified layers, relating cave formation to a regional SB-2 unconformity. Structural analysis revealed that rift faults, caves, and fractures are oriented NW-SE and NE-SW. The 3D regional geological model covers an area measuring 30 km in length and 14 km in width, extending vertically about 150 m. It integrates data from wells, structural mapping, fracture model, and karst features, revealing intensely dissolved layers between two unconformities named SB-1 and SB-2. Based on this, we propose a new approach to karst modeling that combines conduits and karstified fractures following a comprehensive reservoir-scale conceptual model. As a result, four scenarios were simulated to evaluate the water and oil production history, similar to analogous carbonate reservoirs. We conclude that including karst and fractures improves performance and increases production but affects some wells due to premature water breakthroughs. Therefore, effective management and optimized planning for wells are essential for reservoirs containing karst and fractures. This multidisciplinary workflow, which creates geological models and evolves to reservoir production numerical simulation, assesses different scenarios and provides insights into potential outcomes and associated risks that influence decision-making in production development projects.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"251 ","pages":"Article 213866"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse modeling of subsurface flow during CO2-enhanced oil recovery using deep learning approach with adaptive learning strategy","authors":"Aohan Jin ,&nbsp;Wenguang Shi ,&nbsp;Renjie Zhou ,&nbsp;Quanrong Wang ,&nbsp;Zhiqiang Zhao ,&nbsp;Chong Ma","doi":"10.1016/j.geoen.2025.213855","DOIUrl":"10.1016/j.geoen.2025.213855","url":null,"abstract":"<div><div>Inverse modeling plays a crucial role in oil-gas reservoir development for characterizing subsurface geological properties, minimizing prediction uncertainties, and forecasting production sequences. However, balancing the contradiction between computational costs and accuracy remains a challenge in previous inverse modeling approaches. To alleviate such contradictions, this study integrates the adaptive learning (AL) strategy into the iterative ensemble smoother (IES) approach. Unlike traditional numerical simulators, forward modeling of the multiphase flow processes is performed using surrogate models based on a combination of convolutional and recurrent neural networks (CNN-LSTM). The transfer learning technique is adopted to improve the efficiency of the CNN-LSTM surrogate. To test the performance of the proposed AL-CNN-LSTM-based IES approach, two-dimensional CO₂-EOR simulations are conducted with four different sets of permeability fields (<span><math><mrow><msubsup><mi>σ</mi><mrow><mi>ln</mi><mspace></mspace><mi>K</mi></mrow><mn>2</mn></msubsup></mrow></math></span> = 0.15, 0.30, 0.45 and 0.60 mD²) generated by stochastic modeling. Results demonstrate that the transfer learning technique significantly improves the efficiency of the CNN-LSTM surrogate with the average computation time reduced from 639.882 s to 115.212 s. By comparing the real permeability fields with the estimated results obtained from the AL-CNN-LSTM-based IES, the CNN-LSTM-based IES, and the Eclipse-based IES approaches, it is evident that the AL-CNN-LSTM-based IES approach outperforms traditional inversion approaches in terms of computational costs (<span><math><mrow><mi>t</mi><mo>=</mo><mn>302.373</mn><mi>s</mi></mrow></math></span>) and accuracy (<span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi><mo>=</mo><mn>0.167</mn></mrow></math></span>). Furthermore, the newly proposed AL-CNN-LSTM-based IES model demonstrates low sensitivity to permeability field variance, making it applicable for diverse geological scenarios in subsurface multiphase flow problems.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"251 ","pages":"Article 213855"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent multiple parameters optimization methods integrating hydraulic model and SIAs with various constraints for extended reach drilling","authors":"Hailong Jiang ,&nbsp;Tao Zhang ,&nbsp;Yan Xi ,&nbsp;Gonghui Liu ,&nbsp;Jun Li","doi":"10.1016/j.geoen.2025.213846","DOIUrl":"10.1016/j.geoen.2025.213846","url":null,"abstract":"<div><div>Extended reach drilling (ERD) plays a crucial role in deep water and ultra-deep reservoirs exploitation. Cuttings are prone to deposit to the lower side of wellbore when rate of penetration (ROP) is high in ERD. Drilling problems caused by insufficient wellbore cleaning will increase drilling costs and decrease ROP. Therefore, keeping a high ROP and ensuring wellbore cleaning is very important by optimizing hydraulic parameters. This paper proposes intelligent multiple hydraulic parameters optimization methods integrating accurate hydraulic model and particle swarm optimization algorithm (PSO) as well as sparrow search algorithm (SSA) with various constraints to maximize drill bit hydraulic power, which are abbreviated as MPOM-PSO and MPOM-SSA. Rheological parameters of seven rheological models are calculated regressively and the best rheological model is preferred to improve accuracy of pressure loss. Interrelationship between rock breaking and wellbore cleaning as well as constraints of formation pressure, rated pressure of circulation system, rated flow rate of pump and cuttings bed thickness are considered in MPOM-PSO and MPOM-SSA. It overcomes defects of computation-intensive and inability to perform multi-parameters optimization simultaneously compared to traditional optimization methods. The accuracy of hydraulic model is validated by comparing with results calculated by Landmark. The rheological parameter calculation errors of both Power–Law model and Herschell–Bulkley model are less than 1%. In terms of frictional pressure losses in annulus and in drillstring and standpipe pressure, the average errors are 1.8% and 3.5% for Power-law mode and Herschell–Bulkley mode respectively. The efficacy of MPOM-PSO and MPOM-SSA is proved by Case studies and statistic analysis. The maximum errors of optimal flow rate and density are less than 4% and 1% respectively contrasting to traditional method through 50 simulation experiments. However, the variance of optimal flow rate obtained by MPOM-SSA is larger, demonstrating MPOM-PSO is a litter better than MPOM-SSA. Also the optimization speed of MPOM-PSO is increased by more than 25 times. Through the application of MPOM-PSO and MPOM-SSA, hydraulic parameters can be optimized speedy and drilling efficiency of ERD can be improved.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"251 ","pages":"Article 213846"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of domain-adaptive network with data augmentation in lithology identification of buried hill igneous rocks","authors":"Wenlong Liao ,&nbsp;Bin Zhao ,&nbsp;Chuqiao Gao ,&nbsp;Huanhuan Wang ,&nbsp;Hangyu Zhu","doi":"10.1016/j.geoen.2025.213848","DOIUrl":"10.1016/j.geoen.2025.213848","url":null,"abstract":"<div><div>Lithology identification is crucial in oil and gas exploration and development. Although traditional logging techniques and existing intelligent identification technologies have achieved significant progress in identifying lithology within conventional reservoirs, challenges persist in recognizing buried hill igneous rocks. Traditional logging methods rely on empirical rules and static models, which are inadequate for complex geological environments — particularly when severe overlap of logging response values occurs among different lithologies — resulting in poor identification capabilities. While intelligent identification technologies can enhance recognition accuracy by learning complex nonlinear features, they still face issues of insufficient generalization ability and model bias when dealing with data imbalance, feature crossover, and significant data distribution shifts between different wells. To address these limitations, we propose a data-analytically optimized Domain Adversarial Neural Network (DANN) framework for lithology identification. The main contributions of this paper include: (1) proposing an optimized data augmentation strategy to alleviate problems of data imbalance and feature overlap; (2) introducing an automatic feature weighting mechanism within the DANN framework to effectively tackle challenges associated with multi-source feature fusion and data distribution shifts; and (3) validating the proposed method on a real dataset from buried hill reservoirs in the northern South China Sea. The results demonstrate that, compared with traditional logging lithology identification methods and existing intelligent approaches, the proposed method exhibits superior performance in cross-well lithology identification. Additionally, the optimized data augmentation strategy significantly reduces model bias caused by data imbalance and overlapping logging response features, enhancing the overall accuracy of lithology identification.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"251 ","pages":"Article 213848"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy transition, mechanical response and rock fragmentation in percussion drilling: A review","authors":"Yanliang Li ,&nbsp;P.G. Ranjith","doi":"10.1016/j.geoen.2025.213838","DOIUrl":"10.1016/j.geoen.2025.213838","url":null,"abstract":"<div><div>Depleting shallow resources and the growing climate crisis have increased demand for deep resource development and surface waste storage. This necessitates advancements in drilling engineering to address the high costs associated with low drilling rates, particularly in deep hard rock formations. Percussion drilling has emerged as a preferred method for its efficiency in hard rocks. This study reviews the historical development of percussion drilling and discusses existing experimental testing methods, emphasizing the challenges related to automation and data collection. This review article quantifies energy transfer, distribution, and transition during the multiphase interactions in the percussion drilling process. Key factors affecting the mechanical response of the bit-rock interaction during percussion drilling are explored. Finally, the review discusses how these factors influence rock fragmentation performance and damage characteristics. Despite extensive research, key gaps persist in understanding rock failure under high-temperature and in-situ pressure conditions. This review highlights current research gaps and proposes future directions, including the need for comprehensive experimental studies, the development of advanced modeling techniques, and the consideration of deep high-temperature and high-pressure conditions. Addressing these gaps can significantly enhance drilling efficiency and contribute to more sustainable resource extraction strategies. Additionally, the insights and conclusions from this study are not exclusive to drilling engineering but can also provide references for mining, underground space construction, and tunnel excavation fields.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"250 ","pages":"Article 213838"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing post-stack seismic inversion through music-inspired harmony search optimization technique. A case study","authors":"Ravi Kant ,&nbsp;Brijesh Kumar ,&nbsp;S.P. Maurya ,&nbsp;Satya Narayan ,&nbsp;A.P. Singh ,&nbsp;G. Hema","doi":"10.1016/j.geoen.2025.213854","DOIUrl":"10.1016/j.geoen.2025.213854","url":null,"abstract":"<div><div>A novel post-stack seismic inversion algorithm has been developed to estimate acoustic impedances using P-wave reflection seismic data, employing the music-inspired harmony search global optimization (HSO) technique. This optimization seeks to find the global minimum of the objective function, which measures the misfit between synthetic and observed post-stack seismic data. During the iterative inversion process, acoustic impedance models are randomly perturbed, and synthetic seismic data are recalculated to match observed data. To enhance stability, the algorithm uses constraints from a well-log-derived low-frequency impedance model. The proposed algorithm was tested on synthetic and real data to demonstrate its effectiveness in post-stack seismic data inversion. On synthetic test, we found high accuracy of the HSO-generated traces, with average correlations of 0.99, 0.99, 0.97, and 0.96, and RMS errors of 0.12, 0.40, 0.50, and 0.62, for noise levels of 0 %, 10 %, 20 %, and 30 %, respectively. For real data from the Blackfoot Field, Alberta, Canada, the algorithm achieved a 0.93 correlation and 0.22 RMS error, enabling seismic data inversion for acoustic impedance estimation. The inverted section identified low acoustic impedance (8000–9000 m/s∗g/cc), matching the high seismic amplitude anomaly, suggesting a sand channel reservoir between 1040 and 1065 ms two-way travel time. While, high acoustic impedance (9000–12000 m/s∗g/cc) indicating background shale facies. This study explores potential hydrocarbon reservoirs in the Blackfoot Field, Alberta, using HSO-based advanced global optimization for efficient and accurate seismic data inversion.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"250 ","pages":"Article 213854"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting mud weight in carbonate formations using seismic data: A data-driven approach","authors":"Georgy Peshkov ,&nbsp;Kerim Khemraev ,&nbsp;Sergey Safonov ,&nbsp;Nikita Bukhanov ,&nbsp;Ammar Alali ,&nbsp;Mahmoud Abughaban","doi":"10.1016/j.geoen.2025.213850","DOIUrl":"10.1016/j.geoen.2025.213850","url":null,"abstract":"<div><div>Accurate mud weight prediction is crucial for safe and efficient drilling operations in overpressured formations. Especially. Traditional approaches heavily rely on manual seismic interpretation, which is limited by data noise and high dimensionality, and the need for well log data. This study aims to address these challenges by integrating advanced machine learning techniques with seismic and mud weight data, focusing on developing an automated, data-driven workflow that can reliably predict appropriate mud weight trends across large-scale geological fields. The methodology employs a neural network (NN) autoencoder for seismic dimensionality reduction, retaining critical geological features in latent layers. Optimized input features are selected using statistical and interpretability-driven techniques. A k-nearest neighbors model, tuned through grid search, serves as the predictive core, with kriging applied to refine spatial predictions and reduce errors. The approach demonstrates improved geological interpretability and accuracy compared to conventional methods. Applied to a large carbonate field, the workflow effectively predicts spatial mud weight variations, highlighting its scalability and reliability. By combining autoencoding, feature selection, and geostatistical refinement, this methodology offers a robust and interpretable framework for tackling complex geological challenges in drilling operations.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"250 ","pages":"Article 213850"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing robust machine learning techniques to predict oil recovery: A comprehensive field and experimental study","authors":"Wahib Yahya ,&nbsp;Yang Baolin ,&nbsp;Ayman Mutahar AlRassas ,&nbsp;Wang Yuting ,&nbsp;Harith Al-Khafaji ,&nbsp;Riadh Al Dawood","doi":"10.1016/j.geoen.2025.213853","DOIUrl":"10.1016/j.geoen.2025.213853","url":null,"abstract":"<div><div>The volatility in the oil industry driven by significant market demand and notable resource reduction, underscores the crucial requirement for developing a reliable and robust framework to promote oil recovery strategy. This study integrated various robust Machine Learning (ML) algorithms including the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Multilayer Perceptron (MLP) to predict oil recovery based on field and experimental data. Leveraging these models enhances prediction efficiency and reduces reliance on traditional methods. The performance of the integrated ML models with oilfield and experimental datasets, as well as the impact of multiple input parameters against traditional decline curve analysis (DCA) models, was evaluated. The findings reveal that RF, DT, and GBR models have achieved remarkable performance in contrast with other ML models and traditional DCA methods. The RF model has achieved the highest performance, reflected by a coefficient of determination (R²) value of 0.99 for both field Datasets (A) and experimental Datasets (B). More so, we accurately assess the ML model's robustness and performance by leveraging various metrics performance, including the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), to prove the robust alignment with a remarkable merit of accuracy and complexity across the integrated ML models. Ultimately, the results supported the RF model, which obtained the lowest AIC and BIC values among all the models for oil recovery prediction in Datasets (A) and (B).</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"250 ","pages":"Article 213853"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lattice Boltzmann simulation of pre-Darcy flow in porous media","authors":"Sen Wang ,&nbsp;Liyang Chen ,&nbsp;Qihong Feng ,&nbsp;Tangqi Yang ,&nbsp;Li Chen ,&nbsp;Jiyuan Zhang ,&nbsp;Zhengwu Tao ,&nbsp;Zhengjun Zhu","doi":"10.1016/j.geoen.2025.213852","DOIUrl":"10.1016/j.geoen.2025.213852","url":null,"abstract":"<div><div>Fluid flow in porous media, the theoretical basis of which is a linear law proposed by Darcy, plays a vital role in the energy industry. Much evidence suggests that the correlation of velocity and pressure gradient deviates from Darcy's law in the low-flux region due to the existence of viscous (sticky) boundary layers (also termed pre-Darcy flow). Using Darcy's law to characterize the fluid flow process will impede the efficient exploitation of unconventional resources and energy utilization. However, the pore-scale simulation method of pre-Darcy flow was less reported. Based on microtube experiments, we first built a mathematical model of boundary layer thickness, accounting for the effect of pressure gradient and the viscosity difference of distinct regions. Then we incorporated this model into a lattice Boltzmann framework to simulate the pre-Darcy flow and analyzed the influences of different factors. Our results indicate that the boundary layer thickness and throat aperture dominate the pre-Darcy flow behavior, but the boundary layer viscosity shows less impact. As the boundary layer thickness increases, the apparent liquid permeability of porous media decreases, and the pseudo-threshold pressure gradient alters distinctly. In comparison to classical Darcy flow, the streamlines in the pre-Darcy flow are concentrated in the central region of the throat and may redistribute in some throats under the boundary layer effect. This study advances our understanding of pre-Darcy flow and provides a useful methodology to simulate the process in porous media, which is favorable for understanding the transport physics in shale and tight matrices, and vital for accurate dynamic performance prediction and production optimization in unconventional reservoirs.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"250 ","pages":"Article 213852"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}