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Processes
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Intelligent Reservoir Characterization Technology and Numerical Simulation
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Intelligent Reservoir Characterization Technology and Numerical Simulation

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2400

Editors

Prof. Dr. Zhuwen Wang

E-Mail Website
Guest Editor
College of Geo-exploration Science and Technology, Jilin University, 938 Ximinzhu Street, Changchun 130046, China
Interests: intelligent reservoir evaluation; acoustic logging; time-frequency analysis
Prof. Dr. Min Xiang

E-Mail Website
Guest Editor
School of Mining Engineering and Geology, Xinjiang Institute of Engineering, Urumqi 830001, China
Interests: reservoir evaluation; deep learning; geophysical numerical simulation
Dr. Jian Lei

E-Mail Website
Guest Editor Assistant
School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China
Interests: well logging; reservoir evaluation; reservoir petrophysics; unconventional resource

Special Issue Information

Dear Colleagues,

The development of energy exploration and production technologies has long been a global focus, as energy serves as a core driver of modern society. Currently, against the backdrop of the global energy structure transition and carbon neutrality goals, the sustainable development of oil and gas resources is not only crucial for energy security but also closely linked to environmental protection, efficient resource utilization, and economic benefits. At the same time, the future of energy systems increasingly emphasizes emerging fields such as carbon capture, utilization, and storage (CCUS) and hydrogen energy, which are key components in building a clean, low-carbon, safe, and efficient energy system. With the rapid development of artificial intelligence, big data, and high-performance computing, intelligent reservoir characterization and numerical simulation have become cutting-edge directions in traditional oil and gas, CCUS, and hydrogen-related geology, providing critical technological support for the digital and intelligent transformation of the energy industry.

This Special Issue, "Intelligent Reservoir Characterization Technology and Numerical Simulation", will focus on core methodological advancements in this field, particularly intelligent reservoir characterization techniques and numerical simulation methods. We encourage submissions exploring the application of intelligent algorithms in reservoir characterization, high-precision numerical simulation methods and algorithms, multi-physics coupling technologies, and data-driven integration with physical models, aiming to advance the characterization and simulation of subsurface resources toward greater precision, intelligence, and efficiency.

Topics of interest for this Special Issue include, but are not limited to, the following:

  1. Intelligent reservoir characterization methods and technologies;
  2. High-precision numerical simulation algorithms for complex geological conditions;
  3. Reservoir simulation theories and methods under multi-physics coupling;
  4. Data-physical model collaborative-driven reservoir prediction techniques;
  5. Intelligent methods for characterization and CO₂ migration simulation of carbon storage target formations;
  6. Multi-scale intelligent characterization and flow simulation technologies for unconventional reservoirs.

We cordially invite you to submit original research articles, reviews, or case studies to share your innovative findings and insights in this field. Thank you for your attention and support for this Special Issue.

Sincerely,

Prof. Dr. Zhuwen Wang
Prof. Dr. Min Xiang
Guest Editors

Dr. Jian Lei
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 semimonthly 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

  • intelligent reservoir characterization
  • numerical simulation
  • multi-physics field coupling
  • data–physical model integration
  • unconventional reservoirs
  • multi-scale flow simulation
  • carbon storage

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Published Papers (5 papers)

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Research

25 pages, 18553 KB  
Article
Analysis of Key Factors Controlling Fractured Wells Productivity in Tight Gas Condensate Reservoirs Based on Machine Learning Surrogate Models and SHAP
by Xinyu Chen, Gang Luo, Yan Dong, Jiacheng Dang, Guoquan Zhu and Shaoyang Geng
Processes 2026, 14(13), 2216; https://doi.org/10.3390/pr14132216 - 7 Jul 2026
Viewed by 370
Abstract
To address the complex factors affecting the productivity of fractured horizontal wells in tight condensate gas reservoirs, as well as the high computational costs and opaque mechanism interpretation associated with traditional numerical simulations, this study proposes and implements a quantitative evaluation method for [...] Read more.
To address the complex factors affecting the productivity of fractured horizontal wells in tight condensate gas reservoirs, as well as the high computational costs and opaque mechanism interpretation associated with traditional numerical simulations, this study proposes and implements a quantitative evaluation method for the main productivity-controlling factors. This method integrates a machine learning surrogate model with the Shapley additive explanations (SHAP) interpretability framework. First, based on 3D geological modeling and fracture propagation simulation, a high-dimensional parameter set encompassing reservoir geology, artificial fractures, and fluid properties was constructed. Subsequently, representative samples were generated through an orthogonal experimental design. On this basis, machine learning algorithms, including Support Vector Machines (SVM), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost), were utilized to construct low-cost, high-precision surrogate models targeting initial productivity and Estimated Ultimate Recovery (EUR). These surrogate models effectively substituted the computationally expensive fully coupled numerical simulations. Furthermore, SHAP values were applied to the trained surrogate models to conduct both global and local interpretability analyses. This approach not only quantifies the magnitude and direction of each input parameter’s contribution to the productivity predictions, but also reveals their non-linear mechanisms and interaction effects. The results indicate that reservoir properties and gas saturation are the fundamental factors determining the productivity of fractured horizontal wells, while fracture conductivity and fracture half-length are the key engineering factors. Furthermore, there exist significant synergistic or antagonistic effects between the geological and engineering parameters. The integrated “parametric modeling–surrogate model construction—SHAP interpretability analysis” workflow established in this study provides a highly efficient, transparent, and physically insightful novel approach for the rapid optimization of fracturing designs and the mechanistic analysis of main productivity-controlling factors in tight condensate gas reservoirs. Full article
(This article belongs to the Special Issue Intelligent Reservoir Characterization Technology and Numerical Simulation)
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18 pages, 19253 KB  
Article
GPR Noise Reduction Network Based on Multi-Domain Constrained TransUNet
by Xintong Liu, Shanyao Gao, Xianghao Liu, Chaoyu Jiang and Xusheng Wang
Processes 2026, 14(12), 1981; https://doi.org/10.3390/pr14121981 - 18 Jun 2026
Viewed by 309
Abstract
Deep learning has been widely applied to denoising ground-penetrating radar (GPR) signals. However, most existing methods lack physical constraints consistent with GPR data characteristics, especially in the frequency domain, leading to the loss of weak reflections and blurred reconstruction. Conventional networks also treat [...] Read more.
Deep learning has been widely applied to denoising ground-penetrating radar (GPR) signals. However, most existing methods lack physical constraints consistent with GPR data characteristics, especially in the frequency domain, leading to the loss of weak reflections and blurred reconstruction. Conventional networks also treat GPR denoising as a generic image restoration task without explicit weak-signal enhancement. To address these issues, this paper proposes a frequency-domain multi-scale loss function to introduce physical constraints into network training. Combined with traditional loss functions, the proposed method effectively improves the fidelity of weak reflection recovery. A multi-domain constrained TransUNet is further developed for GPR noise reduction. Experiments on synthetic data and field GPR data demonstrate that the proposed method achieves stronger robustness and competitive denoising performance. Full article
(This article belongs to the Special Issue Intelligent Reservoir Characterization Technology and Numerical Simulation)
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31 pages, 12708 KB  
Article
Inversion of Two-Dimensional In Situ Stress Field Constrained by Multisource Data: A Case Study of Logging-Seismic Integrated Fault Identification
by Kai Wang, Xin Nie, Xiaojiang Wang, Fei Wang, Jianxun Liu, Tong Wang and Fan Yong
Processes 2026, 14(10), 1567; https://doi.org/10.3390/pr14101567 - 13 May 2026
Viewed by 446
Abstract
In situ stress field inversion is a fundamental challenge in geothermal resource development, oil and gas exploration, and mine safety assessment. To address the non-uniqueness and limited accuracy of traditional single-data-source inversion approaches, this study proposes a two-dimensional in situ stress field inversion [...] Read more.
In situ stress field inversion is a fundamental challenge in geothermal resource development, oil and gas exploration, and mine safety assessment. To address the non-uniqueness and limited accuracy of traditional single-data-source inversion approaches, this study proposes a two-dimensional in situ stress field inversion method constrained by multi-source data, based on integrated well-seismic fault identification. By incorporating dynamic and static mechanical parameters from well logs and employing both a combined spring model and an anisotropic model, a fault-constrained stress field inversion framework is established. Deep learning and optimization algorithms are utilized to integrate the vertical constraints from well logging data with the lateral continuity characteristics of seismic data, enabling high-resolution reconstruction of the in situ stress field. Taking the complex fault-developed geothermal field in the Xiong’an New Area of the Jizhong Depression, Bohai Bay Basin, as a case study, the proposed method demonstrates a marked reduction in inversion error and a substantial improvement in both fault localization accuracy and stress characterization reliability. Full article
(This article belongs to the Special Issue Intelligent Reservoir Characterization Technology and Numerical Simulation)
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23 pages, 5625 KB  
Article
A Novel Approach for Rock Mechanical Parameter Prediction in Deep Shale Gas Reservoirs Based on VMD-CNN-BiLSTM-AT
by Feng Deng, Jin Wu, Chengyong Li, Yi Liu, Shuo Zhai, Shaoyang Geng, Liuting Chen and Yang Zeng
Processes 2026, 14(10), 1524; https://doi.org/10.3390/pr14101524 - 8 May 2026
Viewed by 359
Abstract
The commercial development of deep shale gas reservoirs depends mainly on hydraulic fracturing. Concurrently, the accurate prediction of rock mechanical parameters is critical to informed decision-making and the optimization of hydraulic fracturing parameters. Traditional methods for developing deep shale reservoirs primarily rely on [...] Read more.
The commercial development of deep shale gas reservoirs depends mainly on hydraulic fracturing. Concurrently, the accurate prediction of rock mechanical parameters is critical to informed decision-making and the optimization of hydraulic fracturing parameters. Traditional methods for developing deep shale reservoirs primarily rely on empirical formulas based on compressional and shear-wave velocities. However, acquiring shear wave data is challenging, and the accuracy of such formulas is often low. Conventional machine learning algorithms exhibit limited predictive accuracy and generalization due to the significant heterogeneity of rock-mechanical data. To efficiently predict rock mechanical parameters with high precision in deep shale formations and to improve guidance for optimizing hydraulic fracturing designs in the deep shale reservoirs of Western Chongqing, this study integrates logging data with laboratory rock-mechanical test data. This research moves beyond simplistic model stacking and utilizes a customized architectural design tailored to the three core characteristics of deep shale in the Western Chongqing Block: high pressure, high organic content, and high brittle mineral content. Specifically, the Variational Mode Decomposition (VMD) is utilized to isolate high-frequency logging fluctuations induced by the strong heterogeneity of high brittle mineral content. The Convolutional Neural Network (CNN) acts as a spatial feature extractor to capture the intricate spatial distribution patterns associated with high organic content. Subsequently, the Bidirectional Long Short-Term Memory (BiLSTM) network models the long-range sequential depth dependencies, reflecting the continuous geomechanical evolution under high-pressure compaction gradients. Finally, the Attention (AT) mechanism dynamically prioritizes the most sensitive logging responses to rock mechanical properties under these complex geological constraints. The proposed VMD-CNN-BiLSTM-AT model was then used to estimate Young’s modulus and Poisson’s ratio in the Western Chongqing Block. The testing phase yielded strong predictive performance, achieving R2 scores of 0.966 and 0.96 for Young’s modulus and Poisson’s ratio, respectively, which were approximately 10% higher than those of other conventional models. Therefore, the proposed model supports data-driven fracturing optimization in deep shale plays by precisely predicting mechanical properties. Full article
(This article belongs to the Special Issue Intelligent Reservoir Characterization Technology and Numerical Simulation)
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15 pages, 1764 KB  
Article
Numerical Simulation of Elastic Waves in VTI Media Using a 17-Point Finite Difference Scheme
by Xiaopeng Yue, Chongwang Yue and Yayun Fu
Processes 2026, 14(8), 1283; https://doi.org/10.3390/pr14081283 - 17 Apr 2026
Cited by 1 | Viewed by 391
Abstract
To optimize the stiffness matrix structure for frequency-domain elastic wave forward modeling in 2D VTI (transversely isotropic with a vertical symmetry axis) media—thereby reducing memory consumption and improving computational efficiency—we simplify the conventional 25-point finite-difference scheme to derive a 17-point frequency-domain finite-difference scheme. [...] Read more.
To optimize the stiffness matrix structure for frequency-domain elastic wave forward modeling in 2D VTI (transversely isotropic with a vertical symmetry axis) media—thereby reducing memory consumption and improving computational efficiency—we simplify the conventional 25-point finite-difference scheme to derive a 17-point frequency-domain finite-difference scheme. This approach reformulates the finite-difference operators for the partial derivatives and acceleration terms in the elastic wave equations, reducing the number of grid points involved in the computation by 30% compared to the 25-point scheme. The optimized matrix construction leverages sparse matrix storage techniques, decreasing memory usage by approximately 27%. Numerical validation, conducted using a double-layer VTI medium model and the Marmousi model with three major faults and an anticline containing limestone layers at the base of the faults, demonstrates that the 17-point finite-difference scheme maintains comparable accuracy while requiring 14% less computation time and featuring a 25% reduction in nonzero elements within the impedance matrix. Comparisons of wavefield snapshots and receiver components (horizontal component U and vertical component V) support this conclusion. These improvements enable the use of more efficient iterative solvers. Full article
(This article belongs to the Special Issue Intelligent Reservoir Characterization Technology and Numerical Simulation)
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