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Processes
Special Issues
Numerical Simulation and Application of Flow in Porous Media
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Numerical Simulation and Application of Flow in Porous Media

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1166

Special Issue Editors

Prof. Dr. Chengyong Li

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Guest Editor
College of Energy, Chengdu University of Technology, Chengdu 610059, China
Interests: porous media flow; numerical simulation; applications in petroleum engineering
Dr. Daobing Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Petroleum University of Chemical Engineering, Beijing, China
Interests: hydraulic fracturing mechanisms of geothermal reservoirs; shale gas; tight oil and gas reservoirs; rock mechanics; efficient finite element numerical computation methods
Special Issues, Collections and Topics in MDPI journals
Dr. Shaoyang Geng

E-Mail Website
Guest Editor
College of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Interests: seepage mechanism and numerical simulation of oil and gas reservoirs; computational fluid dynamics; machine learning in the development of oil and gas reservoirs

Special Issue Information

Dear Colleagues,

Flow phenomena in porous media are ubiquitous and critical across a vast spectrum of scientific and engineering disciplines, including groundwater hydrology, petroleum engineering, environmental remediation, geothermal energy, fuel cells, composite materials manufacturing, biological systems, and chemical engineering processes. Accurately predicting and understanding the complex interactions between fluids and the intricate pore geometries of natural and synthetic porous materials remains a significant challenge. Numerical simulation has emerged as an indispensable tool for tackling this complexity, providing insights that are often unattainable through analytical methods or experimentation alone.

This Special Issue, “Numerical Simulation and Application of Flow in Porous Media”, aims to compile high-quality, original research and review articles that showcase the latest advancements and innovative applications in this rapidly evolving field. We seek contributions that push the boundaries of numerical methods, enhance our fundamental understanding of flow physics in porous systems, and demonstrate tangible solutions to real-world problems.

Topics include, but are not limited to:

  • Advanced Numerical Methods: Novel discretization schemes (Finite Volume, Finite Element, Lattice Boltzmann, Smoothed Particle Hydrodynamics, Pore Network Modeling), multiscale and multiphysics coupling strategies, high-performance computing applications, machine learning-enhanced simulations, uncertainty quantification, and model reduction techniques specific to porous media flow.
  • Fundamental Flow Physics Modeling: Simulation of single-phase and multiphase flow, non-Newtonian fluid flow, reactive transport, coupled thermo-hydro-mechanical-chemical processes, flow in fractured and deformable porous media, and micro-scale (pore-scale) to macro-scale (continuum-scale) bridging.
  • Innovative Applications: Case studies and simulations addressing critical challenges in energy resources (oil/gas recovery, CO2 sequestration, geothermal, hydrogen storage), water resources management (contaminant transport, saltwater intrusion, managed aquifer recharge), environmental engineering (soil remediation, landfill design), materials science (composites processing, filtration), biomedical engineering (tissue perfusion, drug delivery), and emerging technologies (fuel cells, batteries, microfluidics).

Thanks and I hope you consider participating in this Special Issue.

Prof. Dr. Chengyong Li
Dr. Daobing Wang
Dr. Shaoyang Geng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • porous media flow
  • numerical simulation
  • computational modeling
  • multiphase flow
  • pore-scale modeling
  • multiscale simulation
  • applications in petroleum engineering

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

28 pages, 5791 KB  
Article
Interpretable Machine Learning for Shale Gas Productivity Prediction: Western Chongqing Block Case Study
by Haijie Zhang, Ye Zhao, Yaqi Li, Chaoya Sun, Weiming Chen and Dongxu Zhang
Processes 2025, 13(10), 3279; https://doi.org/10.3390/pr13103279 - 14 Oct 2025
Viewed by 350
Abstract
The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the application of data-driven methods. To bridge this gap, we [...] Read more.
The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the application of data-driven methods. To bridge this gap, we develop an interpretable framework by combining grey relational analysis (GRA) with three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVR), and eXtreme Gradient Boosting (XGBoost). Utilizing small-sample data from 87 shale gas wells in the study area, eight key controlling factors were identified, namely, total fracturing fluid volume, proppant intensity, average tubing head pressure, pipeline transfer pressure, casing head pressure, ceramic proppant fraction, fluid placement intensity, and flowback recovery ratio. These factors were used to train, optimize, and validate a productivity prediction model tailored for deep shale gas horizontal wells. The results demonstrate that XGBoost delivers the highest predictive accuracy and generalization capability, achieving an R2 of 0.907 for productivity prediction—surpassing RF and SVR by 12.11% and 131.38%, respectively. Integrating SHapley Additive exPlanations (SHAP) interpretability analysis further enabled immediate post-fracturing productivity assessment and engineering parameter optimization. This research provides a reliable, data-driven strategy for predicting productivity and optimizing operations within the studied block, offering a valuable template for development in geologically similar areas. Full article
(This article belongs to the Special Issue Numerical Simulation and Application of Flow in Porous Media)
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17 pages, 3215 KB  
Article
Research on the Optimization of the Volume Fracturing Shut-in and Drainage System of Unconventional Reservoirs in the Erlian Block
by Ning Li, Xinfang Ma, Liu Xu, Changjun Long, Guohua Liu, Shuzhi Xiu and He Ma
Processes 2025, 13(10), 3258; https://doi.org/10.3390/pr13103258 - 13 Oct 2025
Viewed by 213
Abstract
Aiming at unclear imbibition replacement mechanisms and flowback/production strategies in unconventional reservoirs of the Erlian Block, this study proposes a systematic approach integrating “imbibition-flowback-productivity synergy” to optimize post-fracturing shut-in and production regimes. By developing numerical models incorporating geological and engineering factors, we analyzed [...] Read more.
Aiming at unclear imbibition replacement mechanisms and flowback/production strategies in unconventional reservoirs of the Erlian Block, this study proposes a systematic approach integrating “imbibition-flowback-productivity synergy” to optimize post-fracturing shut-in and production regimes. By developing numerical models incorporating geological and engineering factors, we analyzed fluid dynamics during both the shut-in and production phases. Concurrently, crude oil displacement-fracturing fluid imbibition replacement experiments were conducted to guide parameter optimization. The results indicate that optimized shut-in time and production rates substantially increase recovery efficiency while mitigating reservoir damage and proppant flowback. The well shut-in time of the Erlian Block can achieve the optimal shut-in replacement effect in about 20–25 days. The optimized flowback rate of the unconventional reservoir in the Erlian Block is 25–30 m3/d. The findings offer theoretical insights and practical recommendations for the efficient development of unconventional resources. Full article
(This article belongs to the Special Issue Numerical Simulation and Application of Flow in Porous Media)
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22 pages, 5839 KB  
Article
Research and Application of Deep Coalbed Gas Production Capacity Prediction Models
by Aiguo Hu, Kezhi Li, Changyu Yao, Xinchun Zhu, Hui Chang, Zheng Mao, He Ma and Xinfang Ma
Processes 2025, 13(10), 3149; https://doi.org/10.3390/pr13103149 - 1 Oct 2025
Viewed by 379
Abstract
The accurate prediction of single-well production performance necessitates considering the multiple factors influencing the dynamic changes in coal seam permeability during deep coalbed methane (CBM) extraction. This study focuses on Block D of the Ordos Basin. The Langmuir monolayer adsorption model was selected [...] Read more.
The accurate prediction of single-well production performance necessitates considering the multiple factors influencing the dynamic changes in coal seam permeability during deep coalbed methane (CBM) extraction. This study focuses on Block D of the Ordos Basin. The Langmuir monolayer adsorption model was selected to describe gas adsorption behavior, and a productivity prediction model for deep CBM was developed by coupling multiple dynamic effects, including stress sensitivity, matrix shrinkage, gas slippage, and coal fines production and blockage. The results indicate that the stress sensitivity coefficients of artificial fracture networks and cleat fractures are key factors affecting the accuracy of CBM productivity predictions. Under accurate stress sensitivity coefficients, the predicted daily gas production rates of the productivity model for single wells showed errors ranging from 1.89% to 14.22%, with a mean error of 8.15%, while the predicted daily water production rates had errors between 0.35% and 17.66%, with a mean error of 8.68%. This demonstrates that the established productivity prediction model for deep CBM aligns with field observations. The findings can provide valuable references for production performance analysis and development planning for deep CBM wells. Full article
(This article belongs to the Special Issue Numerical Simulation and Application of Flow in Porous Media)
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