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IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS. / Korytkin, E.I.; Mitrofanov, G.M.

In: Russian Geology and Geophysics, Vol. 66, No. 9, 01.09.2025, p. 1160-1169.

Research output: Contribution to journal › Article › peer-review

Harvard

Korytkin, EI & Mitrofanov, GM 2025, 'IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS', Russian Geology and Geophysics, vol. 66, no. 9, pp. 1160-1169. https://doi.org/10.2113/rgg20254889

APA

Korytkin, E. I., & Mitrofanov, G. M. (2025). IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS. Russian Geology and Geophysics, 66(9), 1160-1169. https://doi.org/10.2113/rgg20254889

Vancouver

Korytkin EI, Mitrofanov GM. IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS. Russian Geology and Geophysics. 2025 Sept 1;66(9):1160-1169. doi: 10.2113/rgg20254889

Author

Korytkin, E.I. ; Mitrofanov, G.M. / IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS. In: Russian Geology and Geophysics. 2025 ; Vol. 66, No. 9. pp. 1160-1169.

BibTeX

@article{6e707be844de453ea5aa1c00df1349cb,
title = "IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS",
abstract = "The article considers the issues of determining the characteristics of target horizons using methods capable of learning on large volumes of heterogeneous data and high prediction accuracy. The methods are used to solve problems of seismic facies analysis at oil and gas fields, the main purpose of which is to reconstruct the sedimentatry rocks and predict lithofacies in the study area. The object of the study was one of the fields in the Volga–Ural region. An improved Bayesian classifier was used as a tool. It was used to determine promising distribution zones of the productive B2 formation reservoir of the Bobrikovian deposits of the Lower Carboniferous and to assess the hydrocarbon production potential. During the research, the effectiveness of the application of machine learning methods and the proposed improvements was analyzed.",
author = "E.I. Korytkin and G.M. Mitrofanov",
note = "Korytkin E. I., Mitrofanov G. M. IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS / E. I. Korytkin, G. M. Mitrofanov // Russian Geology and Geophysics. - 2025. - Т. 66. № 9. - С. 1160–1169. DOI: 10.2113/rgg20254889 ",
year = "2025",
month = sep,
day = "1",
doi = "10.2113/rgg20254889",
language = "English",
volume = "66",
pages = "1160--1169",
journal = "Russian Geology and Geophysics",
issn = "1068-7971",
publisher = "Фонд {"}Центр поддержки науки и культуры{"}",
number = "9",

}

RIS

TY - JOUR

T1 - IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS

AU - Korytkin, E.I.

AU - Mitrofanov, G.M.

N1 - Korytkin E. I., Mitrofanov G. M. IMPROVING THE ACCURACY OF RESERVOIR PROPERTIES PREDICTION USING MACHINE LEARNING METHODS / E. I. Korytkin, G. M. Mitrofanov // Russian Geology and Geophysics. - 2025. - Т. 66. № 9. - С. 1160–1169. DOI: 10.2113/rgg20254889

PY - 2025/9/1

Y1 - 2025/9/1

N2 - The article considers the issues of determining the characteristics of target horizons using methods capable of learning on large volumes of heterogeneous data and high prediction accuracy. The methods are used to solve problems of seismic facies analysis at oil and gas fields, the main purpose of which is to reconstruct the sedimentatry rocks and predict lithofacies in the study area. The object of the study was one of the fields in the Volga–Ural region. An improved Bayesian classifier was used as a tool. It was used to determine promising distribution zones of the productive B2 formation reservoir of the Bobrikovian deposits of the Lower Carboniferous and to assess the hydrocarbon production potential. During the research, the effectiveness of the application of machine learning methods and the proposed improvements was analyzed.

AB - The article considers the issues of determining the characteristics of target horizons using methods capable of learning on large volumes of heterogeneous data and high prediction accuracy. The methods are used to solve problems of seismic facies analysis at oil and gas fields, the main purpose of which is to reconstruct the sedimentatry rocks and predict lithofacies in the study area. The object of the study was one of the fields in the Volga–Ural region. An improved Bayesian classifier was used as a tool. It was used to determine promising distribution zones of the productive B2 formation reservoir of the Bobrikovian deposits of the Lower Carboniferous and to assess the hydrocarbon production potential. During the research, the effectiveness of the application of machine learning methods and the proposed improvements was analyzed.

UR - https://www.mendeley.com/catalogue/99dbb37d-b232-3364-8348-7186f7b0885a/

U2 - 10.2113/rgg20254889

DO - 10.2113/rgg20254889

M3 - Article

VL - 66

SP - 1160

EP - 1169

JO - Russian Geology and Geophysics

JF - Russian Geology and Geophysics

SN - 1068-7971

IS - 9

ER -

ID: 71567148