Advanced Technologies for the Exploration of Conventional and Unconventional Oil and Gas

Dear Colleagues,

The exploration and development of oil and gas resources—both conventional and unconventional—remain critical to global energy security and economic stability. With the growing complexity of reservoirs (e.g., deep formations, shale, tight sandstones, and carbonate reservoirs) and the demand for efficient, low-carbon extraction, advanced technologies have become indispensable. From high-precision geophysical exploration to intelligent drilling, and from reservoir modeling to AI-driven production optimization, technological innovation is reshaping every stage of the oil and gas value chain.

This Special Issue, "Advanced Technologies for the Exploration of Conventional and Unconventional Oil and Gas", aims to showcase cutting-edge research and practical applications that advance the understanding and utilization of oil and gas resources. We welcome contributions that bridge theoretical innovation with industrial practice, covering both foundational technologies and transformative breakthroughs.

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

1. Exploration and development of conventional oil and gas reservoirs (e.g., structural traps, stratigraphic reservoirs);
2. Unconventional resource characterization (shale oil/gas, coalbed methane, tight oil/gas);
3. Reservoir formation mechanisms and evaluation methods;
4. Petroleum engineering and multiphase flow in porous media;
5. Intelligent logging and well testing techniques;
6. AI and machine learning in reservoir modeling, production prediction, and risk assessment;
7. Drilling engineering innovations (automated rigs, directional drilling, low-damage fracturing);
8. Oil and gas big data platforms and digital twins;
9. Smart agents for real-time reservoir monitoring and optimization;
10. Pipeline transportation and storage technologies for high-efficiency resource utilization;
11. Environmental protection and low-carbon technologies in exploration and production.

We encourage submissions of original research articles, review articles, and short communications that highlight technological advancements, case studies, or methodological breakthroughs. Authors are invited to include supplementary materials such as datasets, code repositories, or simulation models to enhance the reproducibility and impact of their work.

Prof. Dr. Qiang Yu
Dr. Hexin Huang
Dr. Kai Qi
Dr. Jie Hu
Dr. Yuqi Wu
Guest Editors

Manuscript Submission Information

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