Advances in Oil and Gas Reservoir Characterization, Modeling, and Development Optimization

Dear Colleagues,

Accurate reservoir characterization and reliable modeling are essential for improving oil and gas recovery, optimizing field development strategies, and supporting the transition toward more efficient and sustainable energy production. With the increasing complexity of conventional, unconventional, offshore, and mature reservoirs, there is a growing need to integrate dynamic geological, geophysical, petrophysical, experimental, numerical simulation, and production data to better characterize reservoir heterogeneity, fluid flow behavior, multiphase displacement mechanisms, and dynamic reservoir evolution during development.

In recent years, artificial intelligence, deep learning, machine learning, digital twins, and data-driven modeling have provided new opportunities for reservoir characterization, simulation, evaluation, and optimization. AI-based methods can effectively extract complex nonlinear relationships from multi-source reservoir data, accelerate reservoir simulation and history matching, improve remaining oil prediction, identify dominant flow channels, support intelligent production optimization, and promote the development of next-generation reservoir engineering theories and technologies. Meanwhile, the integration of data-driven approaches with physical mechanisms, numerical simulation, and domain knowledge is becoming increasingly important for improving model accuracy, interpretability, robustness, and engineering applicability.

This Special Issue, “Advances in Oil and Gas Reservoir Characterization, Modeling, and Development Optimization”, invites the submission of high-quality research articles and reviews focusing on innovative theories, methods, technologies, and applications for reservoir description, numerical simulation, performance evaluation, and development optimization. Emphasis will be placed on the integration of geological knowledge, flow mechanisms, production dynamics, numerical simulation, and artificial intelligence to improve the accuracy, interpretability, and efficiency of reservoir characterization, performance prediction, remaining oil evaluation, and development optimization. Particular attention will be given to AI-enabled reservoir engineering, deep learning-based reservoir characterization, data- and mechanism-driven simulation, intelligent decision-making, and theoretical innovations that bridge physical mechanisms and artificial intelligence.

Topics of interest include, but are not limited to, the following:

• Geological, petrophysical, and geomechanical reservoir characterization;
• Reservoir heterogeneity, pore structure, and flow-unit evaluation;
• Multiphase flow, displacement mechanisms, and enhanced oil recovery processes;
• Numerical simulation, history matching, and reservoir performance prediction;
• Digital rock physics, pore-scale modeling, and multiscale reservoir modeling;
• Data-driven reservoir modeling, machine learning, and artificial intelligence applications;
• Data- and mechanism-driven reservoir simulation and performance prediction;
• Physics-informed deep learning and hybrid physics–AI reservoir modeling;
• Data assimilation, intelligent history matching, and uncertainty quantification;
• AI-assisted multiphase flow modeling and enhanced oil recovery optimization;
• Digital twin reservoirs and intelligent reservoir management;
• Remaining oil evaluation, dominant flow channel identification, and flow-field characterization;
• Production optimization, injection–production regulation, and intelligent decision-making;
• Unconventional oil and gas reservoir evaluation and development optimization;
• Offshore and mature field characterization, remaining oil evaluation, and production optimization;
• Coupled flow–geomechanics–thermal–chemical modeling in oil and gas reservoirs.

Dr. Xianmin Zhang
Guest Editor

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.