AI-Driven Reservoir Characterization and Predictive Simulation in Shale Plays

Dear Colleagues,

Shale oil and gas reservoirs have emerged as pivotal components in the global energy landscape, making significant contributions to energy security and the transition toward a lower-carbon future. However, the efficient and sustainable development of these unconventional resources is hampered by substantial challenges, including their intricate pore structures, ultra-low permeability, and strong heterogeneity. Accurately characterizing the complex properties of shale formations and developing high-fidelity predictive simulation techniques are, therefore, fundamental to optimizing production strategies and enhancing recovery rates. In this context, artificial intelligence (AI) has evolved into an indispensable and transformative driving force—its ability to process massive multi-dimensional data, uncover hidden correlations, and enable intelligent prediction makes it critical for addressing these inherent challenges, revolutionizing the efficiency, accuracy, and reliability of related technologies.

This Special Issue, titled “AI-Driven Reservoir Characterization and Predictive Simulation in Shale Plays”, focuses on showcasing cutting-edge innovations that leverage AI to advance the understanding and development of shale oil and gas systems. We solicit high-quality original research and review articles centered on AI-integrated experimental characterization, theoretical modeling, and predictive numerical simulation. The scope of this issue encompasses, but is not limited to, the following topics:

• Multi-scale pore structure characterization (e.g., AI-driven nano/micro-scale imaging interpretation, machine learning-optimized pore network modeling, digital core characterization, etc.);
• Evaluation of shale petrophysical and geomechanical properties (e.g., prediction of porosity, permeability, organic matter content, brittleness, and anisotropy via machine learning algorithms or deep neural networks);
• Analysis of fluid behavior in shale nanopores (e.g., prediction of adsorption/desorption, simulation of phase behavior, modeling of confinement effects, and AI-assisted analysis of fluid-rock interactions);
• Advanced numerical simulation and modeling (e.g., multi-physics coupling simulation, machine learning-driven discrete fracture networks modeling, upscaling methods, and AI-enabled data-driven simulation);
• Hydraulic fracture propagation prediction and complex fracture network characterization (e.g., real-time prediction of fracture propagation via AI models, characterization of complex fracture networks);
• Enhanced oil recovery (EOR) mechanisms and simulation in shale reservoirs (e.g., AI-driven design of EOR schemes, simulation of EOR effectiveness);
• Integration of characterization data into predictive simulation models (e.g., data fusion for history matching, intelligent production forecasting based on integrated data with AI support).

We warmly invite submissions of original research and review articles that explore the innovative application of AI in shale reservoir characterization and predictive simulation. Specifically, works that demonstrate how AI can deepen the understanding of shale reservoir behaviors, improve the predictive capability of reservoir performance, and drive breakthroughs in efficient and sustainable shale oil and gas development are highly encouraged.

Dr. Hongyan Qu
Dr. Dongqi Ji
Dr. Zhiye Gao
Dr. Jie Zeng
Dr. Yan Peng
Guest Editors

Manuscript Submission Information

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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 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.

• shale
• artificial intelligence (AI)
• pore structure
• numerical simulation
• fracture propagation
• enhanced oil recovery
• fluid migration