Subject. This article discusses the importance of artificial intelligence systems in the management of oil companies. Objectives. The article aims to develop an algorithm for making a management decision based on the use of artificial intelligence tools and practical recommendations for its application. Methods. For the study, we used complex, comparative, and logical analyses. Results. The article presents an algorithm for making management decisions by the oil and gas company’s management team, involving the use of artificial intelligence systems. Relevance. The results of the study can be used by specialists of enterprises of the fuel and energy complex responsible for the introduction of artificial intelligence systems into the production process.
Rustamov A.R., Penkov G.M., Petrakov D.G., Rustamova M.A. [Modern methods of using machine learning as a tool for oil production forecasting]. Nedropol'zovanie = Perm Journal of Petroleum and Mining Engineering, 2024, vol. 24, no. 1, pp. 44–50. URL: Link (In Russ.)
Martyushev D.A., Ponomareva I.N., Zakharov L.A., Shadrov T.A. [Application of machine learning for forecasting formation pressure in oil field development]. Izvestiya Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov = Bulletin of Tomsk Polytechnic University. Geo Assets Engineering, 2021, vol. 332, no. 10, pp. 140–149. (In Russ.) URL: Link
Drobakhina A.N. [Information technology in education: artificial intelligence]. Problemy sovremennogo pedagogicheskogo obrazovaniya = Problems of Modern Pedagogical Education, 2021, no. 70, pp. 125–128. URL: Link (In Russ.)
Zhilov R.A. [Intelligent data clustering methods]. Izvestiya Kabardino-Balkarskogo nauchnogo tsentra Rossiiskoi akademii nauk = News of Kabardino-Balkarian Scientific Center of Russian Academy of Sciences, 2023, no. 6, pp. 152–159. (In Russ.) URL: Link
Pakhomova A.A., Li A.D. [Applying the EM algorithm for a Gaussian mixture]. Nauchnyi vzglyad v budushchee = Scientific Look into the Future, 2020, vol. 1, no. 18, pp. 29–34. (In Russ.) URL: Link
Oreshkov V.I. [Selection of the number of clusters in k-mean algorithm using cluster solution entropy]. Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta = Vestnik of Ryazan State Radioengineering University, 2021, no. 77, pp. 81–92. (In Russ.) URL: Link
Kuznetsov D.A., Plotnikova N.P., Fedosin S.A. [Agglomerative clusterization with DBSCAN algorithm and iterative method]. Nelineinyi mir = Nonlinear World, 2021, vol. 19, no. 3, pp. 29–36. (In Russ.) URL: Link
Skvortsov D.S. [Specific features of artifical intelligence implementation at the enterprises of the oil and gas sector]. Vestnik Moskovskogo gumanitarno-ekonomicheskogo instituta = Herald of Moscow Humanitarian Economic Institute, 2024, no. 3, pp. 39–44. (In Russ.)
Dementiev K.I. [Optimization of business processes of oil and gas enterprises in Russia using artificial intelligence]. Nauchnye trudy Severo-Zapadnogo instituta upravleniya RANKhiGS, 2022, vol. 13, no. 2, pp. 39–48. (In Russ.)
