Journal V.24.No.2.2022
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Machine learning applications for well-logging interpretation of the Vikulov Formation

V.I. Sakhnyuk, E.V. Novickov, A.M. Sharifullin, V.S. Belokhin, A.P. Antonov, M.U. Karpushin, M.A. Bolshakova, S.A. Afonin, R.S. Sautkin, A.A. Suslova

Original article

DOI https://doi.org/10.18599/grs.2022.2.21

230-238
rus.

open access

Under a Creative Commons license
Nowadays well logging curves are interpreted by geologists who preprocess the data and normalize the curves for this purpose. The preparation process can take a long time, especially when hundreds and thousands of wells are involved. This paper explores the applicability of Machine Learning methods to geology tasks, in particular the problem of lithology interpretation using well-logs, and also reveals the issue of the quality of such predictions in comparison with the interpretation of specialists. The authors of the article deployed three groups of Machine Learning algorithms: Random Forests, Gradient Boosting and Neural Networks, and also developed its own metric that takes into account the geological features of the study area and statistical proximity of lithotypes based on log curves values.
 
machine learning, well logging, logging interpretation
 
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Vladlen I. Sakhnyuk – Graduate student, Petroleum Geology Department, Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Evgeniy V. Novikov – Graduate student, Petroleum Geology Department, Lomonosov Moscow State University  
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Alexander M. Sharifullin – Graduate student, Petroleum Geology Department, Lomonosov Moscow State University  
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Vasiliy S. Belokhin – PhD (Physics and Mathematics), Researcher, Petroleum Geology Department, Lomonosov Moscow State University  1, Leninskie gory, Moscow, 119234, Russian Federation
 
Alexey P. Antonov – PhD (Physics and Mathematics), Associate Professor of Mathematical Analysis Department, Head of Rosneft Research Center, Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Mikhail U. Karpushin – Geologist, Researcher, Petroleum Geology Department, Lomonosov Moscow State University  
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Maria A. Bolshakova – PhD (Geology and Mineralogy), Senior Researcher, Petroleum Geology Department, Lomonosov Moscow State University 
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Sergey A. Afonin – PhD (Physics and Mathematics), Associate Professor, Department of Computational Mathematics, Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Roman S. Sautkin – PhD (Geology and Mineralogy), Senior Researcher, Petroleum Geology Department, Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation
 
Anna A. Suslova – PhD (Geology and Mineralogy), Leading Researcher, Petroleum Geology Department, Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation
 

For citation:

Sakhnyuk V.I., Novickov E.V., Sharifullin A.M., Belokhin V.S., Antonov A.P., Karpushin M. U., Bolshakova M.A., Afonin S.A., Sautkin R.S., Suslova A.A. (2022). Machine learning applications for well-logging interpretation of the Vikulov Formation. Georesursy = Georesources, 24(2), pp. 230–238. DOI: https://doi.org/10.18599/grs.2022.2.21

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