Application of Machine Learning in Geo-Energy Exploration Processes
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".
Deadline for manuscript submissions: 10 November 2025 | Viewed by 39
Special Issue Editor
Interests: artificial intelligence; three-dimensional geological modeling; numerical simulation; carbon dioxide sequestration; evaluation of energy storage for underground gas storage in depleted reservoirs
Special Issue Information
Dear Colleagues,
Geo-energy comprises diverse subsurface resources including geothermal reservoirs, conventional hydrocarbons (petroleum and natural gas), and unconventional deposits (coalbed methane, shale gas, shale oil, tight gas, and so on). With the continuous upgrading of energy demand and the increasingly urgent environmental problems, the optimization of exploration methodologies, exploitation techniques, and utilization strategies for these subsurface resources has become an imperative task. The advent of machine learning (ML) has become a powerful tool for addressing multifaceted technical challenges across subsurface energy systems, from geophysical exploration, intelligent reservoir characterization, three-dimensional geological modeling, enhanced recovery, and reduced environmental impact.
This Special Issue on the “Application of Machine Learning in Geo-Energy Exploration Processes” seeks high-quality work focusing on using ML as an important component of geo-energy exploration, development, and monitoring, or that presents the development of new and better ML models.
Topics include, but are not limited to, the following:
- Intelligent resource exploration;
- Intelligent reservoir characterization;
- Development optimization;
- Energy transformation technology;
- Environmental monitoring;
- Interdisciplinary data fusion;
- Extraterrestrial energy system.
Prof. Dr. Zhi Zhong
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 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 learning
- deep learning
- subsurface energy systems
- reservoir characterization
- seismic interpretation
- carbon capture storage
- enhanced recovery
- sustainable energy transition
- digital transformation
- space geo-energy
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