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Special Issue "Rock Physics, Well Logging, and Formation Evaluation in Energy Exploration Systems"
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".
Deadline for manuscript submissions: 10 December 2023 | Viewed by 3274
Special Issue Editors
Interests: formation evaluation; unconventional reservoirs; well logging; applied nuclear magnetic resonance
Interests: digital rock physics; shale oil and gas; formation evaluation
Interests: experimental and theoritical geosciences
Special Issues, Collections and Topics in MDPI journals
Interests: formation evaluation; well logging; unconventional reservoirs; machine learning; CCUS; digital core
Special Issue Information
Well logging plays a very important role in oil and gas exploration and development since it appeared in 1927. It can help to indicate effective formations, offer reliable formation parameters and identify fluids. In the last decade, as more and more unconventional oil and gas, e.g., shale oil/gas and tight oil/gas, are discovered, common well logging inversion and interpretation techniques face great challenges. Formation evaluation methods also cannot work. For complex reservoir characterization, validity evaluation and “sweet spot” prediction, it is urgent to put forward innovative evaluation methods. In addition, small pore space, poor connectivity and weak fluid response lead to low formation parameter (especially permeability and water saturation) calculation and hydrocarbon-beariong identification accuracy. The emergence of digital rock physics techniques and deep learning methods provides a new direction to solve the problem of complex formation evaluation.
This Special Issue on ‘Rock Physics, Well Logging, and Formation Evaluation in Energy Exploration Systems’ seeks high-quality works focusing on the latest novel advances for conventional and unconventional reservoir evaluation based on rock physics and well logging. Topics include, but are not limited to:
- Conventional and unconventional reservoir characterization based on well logging techniques;
- Shale oil/gas, tight oil/gas identification and “sweet spot” prediction;
- Unconventional reservoir conduction mechanism and parameter evaluation;
- Application of deep learning methods in formation evaluation;
- Digital rock physics or NMR techniques for complex formation evaluation.
Prof. Dr. Liang Xiao
Dr. Xin Nie
Prof. Dr. Mehdi Ostadhassan
Dr. Hongyan Yu
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 2000 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.
- formation evaluation
- pore structure
- validity characterization
- deep learning
- digital rock physics