Machine learning for reservoir potential assessment: a critical review of established and promising methods
- DOI
- 10.18799/24131830/2026/8/5657
- Published
- 2026-08-31
- Container
- Bulletin of the Tomsk Polytechnic University Geo Assets Engineering
- Publisher
- National Research Tomsk Polytechnic University
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.18799/24131830/2026/8/5657,
title = {Machine learning for reservoir potential assessment: a critical review of established and promising methods},
author = {Sergey A. Piskunov and Maxim S. Truhachev and Valery S. Rukavishnikov and Davoodi Shadfar},
year = {2026},
journal = {Bulletin of the Tomsk Polytechnic University Geo Assets Engineering},
doi = {10.18799/24131830/2026/8/5657},
url = {https://doi.org/10.18799/24131830/2026/8/5657}
}RIS
TY - JOUR TI - Machine learning for reservoir potential assessment: a critical review of established and promising methods AU - Sergey A. Piskunov AU - Maxim S. Truhachev AU - Valery S. Rukavishnikov AU - Davoodi Shadfar PY - 2026 JO - Bulletin of the Tomsk Polytechnic University Geo Assets Engineering DO - 10.18799/24131830/2026/8/5657 UR - https://doi.org/10.18799/24131830/2026/8/5657 ER -
APA
Piskunov, S. A., Truhachev, M. S., Rukavishnikov, V. S., & Shadfar, D. (2026). Machine learning for reservoir potential assessment: a critical review of established and promising methods. Bulletin of the Tomsk Polytechnic University Geo Assets Engineering. https://doi.org/10.18799/24131830/2026/8/5657
Source records
- crossref · retrieved 2026-11-10T16:49:04.196Z