Vol. 9 pp. 525-540

A.R. Shaymardanov, S.A. Krasnoperova

 

FROM STATISTICS TO INTELLIGENT SCREENING: DIGITALIZATION OF THE CANDIDATE WELL SELECTION PROCESS FOR HYDRAULIC FRACTURING USING MACHINE LEARNING

 

Annotation. The article addresses the problem of improving the success rate of hydraulic fracturing (HF) at the late stage of developing high-viscosity oil fields. Using the Visean object of the Arlanskoye oil field as an example, an analysis of the geological and technological factors affecting operational efficiency was performed. A comparison was made between classical linear regression models and machine learning algorithms (Random Forest). It has been proven that the use of ML algorithms makes it possible to account for nonlinear dependencies (e.g., risks of water breakthrough through thin clay bridges) and to increase the accuracy of production rate forecasting. An algorithm for automated ranking of candidate wells (Success Score) is proposed, which reduces the risks of premature water flooding and optimizes the work of engineering services.

Keywords: hydraulic fracturing (HF), machine learning, Random Forest, digitalization, candidate well, Arlanskoye field, high-viscosity oil, scoring model.

For citation: Shaymardanov A.R., Krasnoperova S.A. [From statistics to intelligent screening: digitalization of the candidate well selection process for hydraulic fracturing using machine learning] Upravlenie tekhnosferoi, 2026, vol. 9, issue 2, рр. 525–540. DOI: 10.35634/UdSU.2026.69.83.016. (In Russ.).

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About the Authors

Shaimardanov A.R.

Master's student, M.S. Gutseriev Institute of Oil and Gas, Federal State Budgetary Educational Institution of Higher Education "Udmurt State University", 426034, Universitetskaya str., 1, Izhevsk, Russia.

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Krasnoperova Svetlana Anatolyevna

Candidate of Biological Sciences, Associate Professor of the Department of Oil and Gas Geology, Associate Professor, M.S. Gutseriev Institute of Oil and Gas, Federal State Budgetary Educational Institution of Higher Education "Udmurt State University", 426034, Universitetskaya str., 1, Izhevsk, Russia.

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