Вестник нефтегазовой отрасли КазахстанаВестник нефтегазовой отрасли Казахстана2707-42262957-806XKMG Engineering10881910.54859/kjogi108819Review ArticleOil and Algorithms: How Artificial Intelligence turns Data into EnergySeitimbetovaAigerim B.sab.buketov.2022@gmail.comhttps://orcid.org/0009-0000-8755-7992Shulgina-TarachshukAlevtina S.alevtinash79@mail.ruhttps://orcid.org/0009-0000-4759-9389SmailovaAizhan S.smailova.buketov@gmail.comhttps://orcid.org/0000-0003-2936-0336Karaganda Buketov University260920257343500802202519062025Copyright © 2025, Seitimbetova A.B., Shulgina-Tarachshuk A.S., Smailova A.S.2025<p><span style="font-weight: 400;">The article explores the application of Artificial intelligence in the oil industry, focusing on the transformation of data into new energy sources. Artificial intelligence is used to optimize oil extraction and refining processes, contributing to increased productivity, reduced costs, and enhanced safety. The implementation of innovative algorithms, such as machine learning and the Internet of Things, significantly improves forecasting accuracy, the identification of hidden patterns, and process automation. These technologies help effectively manage risks, minimize costs, and accelerate operations, while also enhancing environmental sustainability. Artificial intelligence promotes the rational use of natural resources and reduces environmental impact, improving both economic and environmental performance of oil companies. Overall, the use of Artificial intelligence in the oil industry opens up new opportunities for more efficient and environmentally friendly production, making processes more sustainable in the long term.</span></p>artificial intelligenceoil industryforecastingautomationжасанды интеллектмұнай өнеркәсібіболжауавтоматтандыруискусственный интеллектнефтяная промышленностьпрогнозированиеавтоматизация1.Smith J. Modern Technologies in Oil and Gas Industry. New York : Science Publishing, 2021. 350 p.2.Brown T.L. Artificial Intelligence: Challenges and Future Prospects. London : Academic Press, 2021. 220 p.3.Колбикова Е.С. Литофациальный анализ и возможности прогнозирования свойств по данным геофизических исследований и сейсморазведки методами машинного обучения // Вестник нефтегазовой отрасли Казахстана. 2021.Т. 3, №4. C. 34–39. doi: 10.54859/kjogi99690.4.Жетруов Ж.Т., Шаяхмет К.Н., Карсыбаев К.К., и др. Применение прокси-моделей при прогнозировании параметров разработки нефтяных залежей // Вестник нефтегазовой отрасли Кказахстана. 2022. Т. 4, №2. С. 48–57. doi: 10.54859/kjogi108021.5.Williams R.G. Energy and Environment: The New Paradigms. Los Angeles : Energy Books, 2022. 280 p.6.Johnson P.D. The Future of Oil and Gas: Sustainable Solutions. Chicago: Global Energy Publishers, 2022. 310 p.7.Miller A.J. Digitalization in Energy: Technologies and Strategies. San Francisco : Energy Solutions, 2023. 260 p.8.Taylor M.C. Artificial Intelligence in the Energy Sector. Boston : Tech Innovations, 2023. 230 p.9.Davis B.P. Innovative Methods in Oil Exploration and Extraction. Houston : Oil & Gas Press, 2022. 375 p.10.Wilson C.A. Smart Energy Systems: Artificial intelligence and Beyond. Oxford : Future Energy Publications, 2022. 300 p.11.Evans R.J. Energy Markets and Artificial Intelligence: A New Era. Cambridge : Energy Insights, 2021. 320 p.12.Dutta D., Upreti S.R. Artificial intelligence-based process control in chemical, biochemical, and biomedical engineering // Canadian Journal of Chemical Engineering. 2021. Vol. 99, Issue 11. P. 2467–2504. doi: 10.1002/cjce.24246.13.Terkina A. Use of information technology by engineers in the oil and gas industry // Recent Achievements and Prospects of Innovations and Technologies. 2022. Vol. 1. P. 122–128.14.Grimberg H., Tiwari V.S., Tam B., et al. Machine learning approaches to optimize small-molecule inhibitors for RNA targeting // Journal of Cheminformatics. 2022. Vol. 14, N 1. P. 1–15. doi: 10.1186/s13321-022-00583-x.15.Gallegos M., Vassilev-Galindo V., Poltavsky I., et al. Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors // Nature Communications. 2024. Vol. 155. doi: 10.1038/s41467-024-48567-9.16.Parker D.L. Artificial Intelligence and the Future of Energy. Toronto : GlobalTech, 2023. 210 p.17.Abisha J.J., Janaki M. Cyber security for chemical plant using artificial intelligence // International Journal of Computer Science and Mobile Computing. 2024. Vol. 13, Issue 5. P. 116–129. doi: 10.47760/ijcsmc.2024.v13i05.012.