Machine Learning-Enabled Reservoir Dynamics Prediction and Recovery Factor Optimization
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".
Deadline for manuscript submissions: 23 February 2026 | Viewed by 188
Special Issue Editors
Interests: petroleum engineering; reservoir modeling; geomechanics; mining; machine learning and explainable AI
Interests: artificial intelligence; data-driven optimization; entropy generalize minimization; energy sustainability
Special Issue Information
Dear Colleagues,
Machine learning, deep learning, and artificial intelligence-enabled reservoir dynamics prediction and recovery factor optimization are crucial for maximizing and sustainable hydrocarbon production with environmentally friendly impacts and nominal costs. In addition, data-driven models using the above techniques lead to more efficient and profitable operations, reservoir forecasting, as well as a more sustainable approach to oil and gas resource management.
This Special Issue, entitled “Machine Learning-Enabled Reservoir Dynamics Prediction and Recovery Factor Optimization”, requests high-quality and novel research contribution and innovative review works focusing on dynamic model development, optimization processes, and software applications in the fields of conventional and unconventional reservoir systems, production optimization, and enhanced oil and gas recovery prediction by coupling real-time data and ML/DL/explainable AI techniques.
Prospective research topics include, but are not limited to, the following areas:
- Physics-informed machine learning-based predictive models, feature selection and dimensionality reduction techniques for reservoir behavior analysis and near-wellbore geomecahnics.
- Efficacy of hybrid/metaheuristic optimization techniques to reservoir dynamics and oil recovery prediction.
- Genetic algorithm and advance novel approach-based improved model for predicting oil recovery.
- Validation and field case studies of ML/DL/XAI guided robust models to hydrocarbon reservoir description and forcasting.
- Application of commercial simulation tools for reservoir dynamics model and oil recovery performance.
- Innovative guidelines and workflows for integrating ML/DL/XAI into digital oilfield and reservoir management practices.
Dr. Mohammad Islam Miah
Dr. Murtada A. Elhaj
Dr. Arifur Rahman
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
- machine/deep learning and explainable AI
- data-driven optimization
- reservoir dynamics and geomechanics
- unconventional and geothermal reservoirs
- production forecasting
- enhanced oil recovery techniques
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