Exploring the prospects and challenges of petrophysics research from the perspective of materials physics
Rocks are the most prevalent material on solid planets, and using physical methods is the most practical and feasible way to explore planetary resources. This is also one of the most commonly used methods for oil and gas exploration on the Earth. However, many challenges remain to be addressed in practice. Based on the practice of oil and gas physical exploration, rock physicists need to answer several questions. This work aims to address these issues by exploring similarities between materials physics and petrophysics. There are some special phenomena in rock physics that can be explained by principles borrowed from materials physics. This work also examines the similarities of porous media in different physical methods, conducts comparative studies on the physical properties of porous media, and analyzes the potential for future exploration using comparative physical property methods. In addition to being applied in petroleum engineering logging and core analysis, petrophysics can also provide insights for the development of new energy and materials, offering valuable guidance for upgrading the energy structure. Document Type: Perspective Cited as: He, J., Dang, L., Wang, L., Kang, Q., Zhang, B., Zhang, C. Exploring the prospects and challenges of petrophysics research from the perspective of materials physics. Advances in Geo-Energy Research, 2025, 16(2): 95-98. https://doi.org/10.46690/ager.2025.05.02
Authors
- Benjian Zhang (ORCID: https://orcid.org/0000-0001-5661-8561)
- Jiahuan He (ORCID: https://orcid.org/0000-0001-7331-9328)
- L. Minh Dang (ORCID: https://orcid.org/0000-0001-7691-3453)
- Liwei Wang (ORCID: https://orcid.org/0000-0002-6345-032X)
- Chun Zhang (ORCID: https://orcid.org/0000-0002-7104-3716)
- Qiang Kang (ORCID: https://orcid.org/0000-0002-6154-3514)
Institutions
- Gas Technology Institute (US)
Publication Details
- Journal
- ADVANCES IN GEO-ENERGY RESEARCH
- Published
- 2025-01-22
- DOI
- https://doi.org/10.46690/ager.2025.05.02
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.55