G.V. Khusainov, A.S. Kovalkova
DOI: https://doi.org/10.25689/NP.2024.4.104-120
Abstract
The applicability of using machine learning algorithms to solve problems in the field of seismic interpretation is an urgent issue. This article presents a comparison of the results of testing machine learning algorithms integrated into IP-Seismic software. The obtained results can be used to build trends in the modeling process, evaluate various scenarios and analyze the spread of initial geological reserves.
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Authors
G.V. Khusainov, Leading Specialist, Tyumen Oil Research Center LLC
79/1, Osipenko Str., 625000, Tyumen, Russian Federation
E-mail: GV_Khusainov2@tnnc.rosneft.ru
A.S. Kovalkova, Leading Specialist, Tyumen Oil Research Center LLC
79/1, Osipenko Str., 625000, Tyumen, Russian Federation
E-mail: AS_Kovalkova@tnnc.rosneft.ru
For citation:
G.V. Khusainov, A.S. Kovalkova Ispol'zovaniye neyronnykh setey Kolmogorova pri prognozirovanii kollektorskikh svoystv na primere mestorozhdeniy Zapadnoy Sibiri [The use of Kolmogorov neural networks in prediction of reservoir properties on the example of deposits of Western Siberia]. Neftyanaya Provintsiya, No. 4(40), 2024. pp. 104-120. DOI https://doi.org/10.25689/NP.2024.4.104-120. EDN PTYGQQ (in Russian)