Petrophysical Formation Evaluation and Well Logging in Energy Exploration Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 25 May 2026 | Viewed by 24
Special Issue Editors
Interests: formation evaluation; unconventional reservoirs; well logging; applied nuclear magnetic resonance
Special Issues, Collections and Topics in MDPI journals
Interests: formation evaluation; well logging; unconventional reservoirs; machine learning; CCUS; digital core
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Unconventional oil and gas reservoirs—such as shale oil/gas, coalbed methane, and tight oil/gas—often exhibit response characteristics that differ significantly from those of conventional reservoirs. Traditional petrophysical methods and formation evaluation technologies lose their effectiveness in these settings. As a results, geologists have placed higher demands on well log evaluation, interpretation methods, models, and related technologies for unconventional oil and gas resources characterization, “sweet spot” prediction, effectiveness evaluation, and productivity research. To advance both conventional and unconventional reservoir characterization, it is necessary to develop new technologies related to petrophysical experiments, well logging data processing and interpretation, theoretical modeling, and digital rock analysis.
This Special Issue seeks high-quality contributions focusing on the latest advances in petrophysical characterization and well logging evaluation techniques for both conventional and unconventional reservoirs. Topics include, but are not limited to, the following:
- Conventional and unconventional reservoir characterization and effectiveness prediction;
- Application of CT scanning technology in reservoir evaluation;
- Fractured reservoirs evaluation based on advanced well logging techniques;
- Unconventional reservoir flow mechanisms, dynamic-static integration, and productivity prediction;
- Application of machine learning methods in conventional and unconventional reservoirs.
Prof. Dr. Liang Xiao
Prof. Dr. Hongyan Yu
Prof. Dr. Cheng Feng
Guest Editors
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Keywords
- pore structure
- sedimentary microfacies
- unconventional reservoirs characterization
- effectiveness prediction
- productivity estimation
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