AI PLATFORM FOR MANAGING THE DEVELOPMENT OF HARD-TO-RECOVER OIL RESERVES

  • M.A. Serkov Yaroslav-the-Wise Novgorod State University, Veliky Novgorod, Russia
DOI: https://doi.org/10.34680/BENEFICIUM.2026.3(60).98-109

Abstract

Hard-to-recover reserves account for a growing share of the Russian resource base, and the cost of a managerial error at such fields exceeds that at conventional ones: geological uncertainty is not removed by a single exploration cycle, and design decisions have to be revised as development proceeds. Hence the question of how to manage field development when data, computational models and decisions are dispersed across functional units. The aim of the paper is to propose a conceptual architecture of an AI platform for managing the development of hard-to-recover oil reserves and to define the economic, organisational and legal conditions of its viability. The objectives are: to systematise AI functions across the stages of the field life cycle; to identify the features that distinguish a platform from an integrated information system; to describe economic, organisational and legal constraints; and to specify a module for Arctic and other remote assets. The empirical base comprises twenty peer-reviewed publications of 2021-2025 from Scopus, Web of Science and eLIBRARY, a federal strategic planning document, and six publicly disclosed cases of Russian oil and gas companies. The methods are content analysis using a four-category coding scheme, comparative case analysis, structural-functional modelling, and economic-legal interpretation. The study shows that the algorithms currently in use deliver gains within a single function – seismic interpretation, flow rate forecasting, equipment diagnostics – without linking these gains to one another. A six-level architecture is proposed: data, computing infrastructure, digital twins, AI models, management services, and participation rules. The paper specifies how the platform transfers algorithms between fields and reduces the cost of connecting each subsequent service, and substantiates four groups of effect indicators: production, cost, time and risk. The results are applicable in designing corporate platforms and sectoral data exchange formats; further validation requires testing on a specific well stock.

Keywords: Arctic territories, artificial intelligence, data governance, digital platform, digital twin, hard-to-recover reserves, oil production, platform economy

Funding: the research had no sponsorship (own resources).

References

  • Serkov M.A. Ways for the Extraction of Hard-To-Recover Oil Reserves // Economic Sciences. 2020. Vol. 5(186). Pp. 111-113. (In Russ.). DOI: 10.14451/1.186.111
  • Kryukov V.A., Tokarev A.N. Creation of Conditions for the Development of Hard-to-Recover Oil Reserves: Regional Aspects // Economy of Region. 2022. Vol. 18(3). Pp. 755-769. (In Russ.). DOI: 10.17059/ekon.reg.2022-3-10
  • Jovanovic M., Sjödin D., Parida V. Co-evolution of Platform Architecture, Platform Services, and Platform Governance: Expanding the Platform Value of Industrial Digital Platforms // Technovation. 2022. Vol. 118. Pp. 1-14. DOI: 10.1016/j.technovation.2020.102218
  • Madanaguli A., Parida V., Sjödin D., Oghazi P. Literature Review on Industrial Digital Platforms: A Business Model Perspective and Suggestions for Future Research // Technological Forecasting and Social Change. 2023. Vol. 194(7). Pp. 1-17. DOI: 10.1016/j.techfore.2023.122606
  • Costabile C. Digital Platform Ecosystem Governance of Private Companies: Building Blocks and a Research Agenda Based on a Multidisciplinary, Systematic Literature Review // Data and Information Management. 2024. Vol. 8(1). Pp. 1-13. DOI: 10.1016/j.dim.2023.100053
  • Pauli T., Marx E., Fielt E. Inhibitors and Enablers of Leverage in Digital Industrial Platform Ecosystems // Business & Information Systems Engineering. 2025. Vol. 67(5). Pp. 711-731. DOI: 10.1007/s12599-024-00896-1
  • Möller F., Jussen I., Springer V. Industrial Data Ecosystems and Data Spaces // Electronic Markets. 2024. Vol. 34(1). DOI: 10.1007/s12525-024-00724-0
  • Farahani B., Monsefi A.K. Smart and Collaborative Industrial IoT: A Federated Learning and Data Space Approach // Digital Communications and Networks. 2023. Vol. 9(2). Pp. 436-447. DOI: 10.1016/j.dcan.2023.01.022
  • Tariq Z., Aljawad M.S., Hasan A. A Systematic Review of Data Science and Machine Learning Applications to the Oil and Gas Industry // Journal of Petroleum Exploration and Production Technology. 2021. Vol. 11. Pp. 4339-4374. DOI: 10.1007/s13202-021-01302-2
  • Samnioti A., Gaganis V. Applications of Machine Learning in Subsurface Reservoir Simulation - A Review - Part II // Energies. 2023. Vol. 16(18). Pp. 1-53. DOI: 10.3390/en16186727
  • Rahmanifard H., Gates I. A Comprehensive Review of Data-Driven Approaches for Forecasting Production from Unconventional Reservoirs: Best Practices and Future Directions // Artificial Intelligence Review. 2024. Vol. 57(8). Pp. 1-40. DOI: 10.1007/s10462-024-10865-5
  • Fan D., Lai S., Sun H. Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir // Energies. 2025. Vol. 18(4). Pp. 1-25. DOI: 10.3390/en18040842
  • Zakharov L.A., Martyushev D.A., Ponomareva I.N. Predicting Dynamic Formation Pressure Using Artificial Intelligence Methods // Journal of Mining Institute. 2022. Vol. 253. Pp. 23-32. (In Russ.). DOI: 10.31897/PMI.2022.11
  • Zemenkova M.Yu., Chizhevskaya E.L., Zemenkov Yu.D. Intelligent Monitoring of the Condition of Hydrocarbon Pipeline Transport Facilities Using Neural Network Technologies // Journal of Mining Institute. 2022. Vol. 258. Pp. 933-944. (In Russ.). DOI: 10.31897/PMI.2022.105
  • Knebel F.P., Trevisan R., Nascimento G.S. A Study on Cloud and Edge Computing for the Implementation of Digital Twins in the Oil & Gas Industries // Computers & Industrial Engineering. 2023. Vol. 182. Pp. 1-22. DOI: 10.1016/j.cie.2023.109363
  • Meza E.B.M., Souza D.G.B., Copetti A. Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis // Sensors. 2024. Vol. 24(19). Pp. 1-25. DOI: 10.3390/s24196457
  • Póvoas M.S., Moreira J.F., Martins Neto S.V. Artificial Intelligence in the Oil and Gas Industry: Applications, Challenges, and Future Directions // Applied Sciences. 2025. Vol. 15(14). Pp. 1-16. DOI: 10.3390/app15147918
  • Zhdaneev O.V. Technological Sovereignty of the Russian Federation Fuel and Energy Complex // Journal of Mining Institute. 2022. Vol. 258. Pp. 1061-1078. (In Russ.). DOI: 10.31897/PMI.2022.107
  • Energeticheskaya strategiya Rossiyskoy Federatsii na period do 2050 goda [Energy Strategy of the Russian Federation for the Period up to 2050]: approved by Decree of the Government of the Russian Federation No. 908-r dated 12.04.2025 (2025). Government of the Russian Federation. (In Russ.). URL: https://static.government.ru/media/files/LWYfSENa10uBrrBoyLQqAAOj5eJYlA60.pdf (accessed on 28.07.2026).
  • "Gazprom neft" i Universitet Innopolis sozdadut II-platformu dlya issledovaniya nedr [Gazprom Neft and Innopolis University to Create an AI Platform for Subsurface Research] (2025). Gazprom Neft PJSC. (In Russ.). URL: https://digital.gazprom-neft.ru/news/id_10072025 (accessed on 28.07.2026).
  • "Bashneft" podklyuchila k sisteme "Tsifrovoe mestorozhdenie" bolee 8 tysyach skvazhin [Bashneft Connects More Than 8,000 Wells to the "Digital Field" System] (2025). Rosneft Oil Company PJSC. (In Russ.). URL: https://www.rosneft.ru/press/news/item/222168/ (accessed on 28.07.2026).
  • "Rosneft" sovershenstvuet metody issledovaniya skvazhin [Rosneft Improves Well Testing Methods] (2025). Rosneft Oil Company PJSC. (In Russ.). URL: https://www.rosneft.ru/press/news/item/222577/ (accessed on 28.07.2026).
  • "Rosneft" vnedryaet iskusstvennyy intellekt dlya optimizatsii protsessov dobychi [Rosneft Implements Artificial Intelligence to Optimise Production Processes] (2022). Rosneft Oil Company PJSC. (In Russ.). URL: https://www.rosneft.ru/press/news/item/212201/ (accessed on 28.07.2026).
  • "Rosneft" sovershenstvuet programmnoe obespechenie dlya razrabotki gazovykh mestorozhdeniy [Rosneft Improves Software for Gas Field Development] (2021). Rosneft Oil Company PJSC. (In Russ.). URL: https://www.rosneft.ru/press/news/item/205901/ (accessed on 28.07.2026).
  • Matrokhina K.V., Trofimets V.Ya., Mazakov E.B. Development of Methodology for Scenario Analysis of Investment Projects of Enterprises of the Mineral Resource Complex // Journal of Mining Institute. 2023. Vol. 259. Pp. 112-124. (In Russ.). DOI: 10.31897/PMI.2023.3
  • Cherepovitsyn A.E., Tsvetkov P.S., Evseeva O.O. Critical Analysis of Methodological Approaches to Assessing Sustainability of Arctic Oil and Gas Projects // Journal of Mining Institute. 2021. Vol. 249. Pp. 463-479. (In Russ.). DOI: 10.31897/PMI.2021.3.15
  • Streletskiy D.A., Clemens S., Lanckman J.-P., Shiklomanov N.I. The Costs of Arctic Infrastructure Damages Due to Permafrost Degradation. Environmental Research Letters. 2023. Vol. 18(1). Pp. 1-13. DOI: 10.1088/1748-9326/acab18
  • "Gazprom neft" sozdast tsifrovuyu model' vechnoy merzloty v Zapolyar'e [Gazprom Neft to Create a Digital Model of Permafrost in the Arctic] (2025). Gazprom Neft PJSC. (In Russ.). URL: https://digital.gazprom-neft.ru/news/id_22052025 (accessed on 28.07.2026).

About the Author

Maxim A. Serkov – Cand. Sci. (Law); Senior Researcher, Yaroslav-the-Wise Novgorod State University, Veliky Novgorod, Russia. E-mail: tiempo10@mail.ru. SPIN RINTS 9945-6500. ORCID 0009-0000-5796-4386.

For citation: Serkov M.А. AI Platform for Managing the Development of Hard-to-Recover Oil Reserves // BENEFICIUM. 2026. Vol. 3(60). Pp. 98-109. (In Russ.). DOI: 10.34680/BENEFICIUM.2026.3(60).98-109

Published
2026-09-06
Issue
No 3 (2026)
Section
PLATFORM SOLUTIONS AND AI TECHNOLOGIES IN MODERN INDUSTRY MANAGEMENT