Journal V.24.No.4.2022
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Localization and development of residual oil reserves using geochemical studies based on neural network algorithms

V.A. Sudakov, R.I. Safuanov, A.N. Kozlov, T.M. Poryvaev, A.A. Zaikin, R.A. Zinyukov, A.A. Lutfullin, I.Z. Farkhutdinov, I.Z. Tylyakov

Original article

DOI https://doi.org/10.18599/grs.2022.4.4

50-64
rus.

open access

Under a Creative Commons license
At the late stage of field development, residual oil reserves undergo a significant change from mobile to sedentary and stationary. These reserves are mainly located in technogenically and production altered, watered layers and areas of deposits.

Localization and development of such sources of hydrocarbons is an effective method of increasing the final oil recovery factor in mature fields, due to the presence of a ready-made developed infrastructure for production, transportation and refining, as well as the availability of highly qualified personnel.

This article considers an approach that allows, based on neural network algorithms, the estimation the volumes and localization of residual oil reserves in multi-layer deposits in combination with the analysis of geochemical studies of reservoir fluids. The use of machine learning algorithms allows a targeted approach to the development of residual reserves by automated selection of wellwork. This approach significantly reduces the manual labor of specialists for data processing and decision-making time.
 
software package, convolutional neural network, neural network algorithms, oil field, localization of oil reserves, geochemical studies, selection of geological and technical measures
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Vladislav A. Sudakov – Deputy Director of the Institute for Innovations, Director of Hard-to-Recover Reserves Simulation Research and Educational Center, Institute of Geology and Petroleum Technology, Kazan Federal University
Bolshaya Krasnaya str., 4, Kazan, 420111, Russian Federation
 
Rinat I. Safuanov – Researcher, Hard-to-Recover Reserves Simulation Research and Educational Center, Institute of Geology and Petroleum Technology, Kazan Federal University
Bolshaya Krasnaya str., 4, Kazan, 420111, Russian Federation
 
Aleksey N. Kozlov – Junior Researcher, Hard-to-Recover Reserves Simulation Research and Educational Center, Institute of Geology and Petroleum Technology, Kazan Federal University
Bolshaya Krasnaya str., 4, Kazan, 420111, Russian Federation

Timur M. Porivaev – Engineer, Hard-to-Recover Reserves Simulation Research and Educational Center, Institute of Geology and Petroleum Technology, Kazan Federal University
Bolshaya Krasnaya str., 4, Kazan, 420111, Russian Federation
 
Artem A. Zaikin – Researcher, Hard-to-Recover Reserves Simulation Research and Educational Center, Institute of Geology and Petroleum Technology, Kazan Federal University
Bolshaya Krasnaya str., 4, Kazan, 420111, Russian Federation
 
Rustam A. Zinykov – Junior Researcher, Hard-to-Recover Reserves Simulation Research and Educational Center, Institute of Geology and Petroleum Technology, Kazan Federal University
Bolshaya Krasnaya str., 4, Kazan, 420111, Russian Federation
 
Azat A. Lutfullin – Cand. Sci. (Engineering), Deputy Head of the Department of Field Development, Tatneft-Dobycha, Tatneft PJSC
Lenin str., 75, Almetyevsk, 423450, Russian Federation
 
Ildar Z. Farhutdinov – Head of Oil and Gas Fields Development Department, Tatneft PJSC
Telman str., 88, Almetyevsk, 423462, Russian Federation
 
Ilgiz Z. Tylyakov – Leading Specialist, Oil and Gas Fields Development Department, Tatneft PJSC
Telman str., 88, Almetyevsk, 423462, Russian Federation
 

For citation:

Sudakov V.A., Safuanov R.I., Kozlov A.N., Poryvaev T.M., Zaikin A.A., Zinyukov R.A., Lutfullin A.A., Farkhutdinov I.Z., Tylyakov I.Z. (2022). Localization and development of residual oil reserves using geochemical studies based on neural network algorithms. Georesursy = Georesources, 24(4), pp. 50–64. https://doi.org/10.18599/grs.2022.4.4

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