Modern Technologies for Ranking Territories by Hydrocarbon Prospectivity Using Machine Learning

Danis Nurgaliev; Eduard Ziganshin; Timur Safin; Fagim Garaev

doi:10.2991/978-94-6239-668-5_84

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Modern Technologies for Ranking Territories by Hydrocarbon Prospectivity Using Machine Learning

Authors

Danis Nurgaliev¹, Eduard Ziganshin¹^{, *}, Timur Safin¹, Fagim Garaev¹

¹Institute of Geology and Petroleum Technology, Kazan Federal University, Kazan, Russia

^*Corresponding author. Email: erziganshin@kpfu.ru

Corresponding Author

Eduard Ziganshin

Available Online 14 May 2026.

DOI: 10.2991/978-94-6239-668-5_84 How to use a DOI?
Keywords: Magnetic Surveying; Geochemical Hydrocarbon Exploration; Machine Learning; Non-Seismic Methods; Petroleum Prospecting
Abstract: This article presents a geological and geophysical technology designed to assess the oil and gas potential of territories without the use of expensive seismic or other conventional methods. The technology is based on the integration of geological, geophysical, and geochemical data using original mathematical algorithms developed at Kazan Federal University (Russia). Its modular design allows the research program to be adapted to specific geological conditions, the degree of exploration maturity, and available financial resources, thus ensuring an optimal balance between cost and informational value. The novelty of the work lies in the application of machine-learning algorithms for an integrated prospectivity assessment. Feature selection was performed using a correlation matrix that accounts for nonlinear relationships.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Current Problems in Engineering and Applied Sciences (ICCPEAS 2025)
Series: Advances in Engineering Research
Publication Date: 14 May 2026
ISBN: 978-94-6239-668-5
ISSN: 2352-5401
DOI: 10.2991/978-94-6239-668-5_84 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Danis Nurgaliev
AU  - Eduard Ziganshin
AU  - Timur Safin
AU  - Fagim Garaev
PY  - 2026
DA  - 2026/05/14
TI  - Modern Technologies for Ranking Territories by Hydrocarbon Prospectivity Using Machine Learning
BT  - Proceedings of the International Conference on Current Problems in Engineering and Applied Sciences (ICCPEAS 2025)
PB  - Atlantis Press
SP  - 801
EP  - 808
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-668-5_84
DO  - 10.2991/978-94-6239-668-5_84
ID  - Nurgaliev2026
ER  -

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