Kazakhstan journal for oil & gas industry

2707-42262957-806X

KMG Engineering

108909

10.54859/kjogi108909

Research Article

Application of artificial intelligence in the oil and gas industry: trend or necessity?

Ayaganova

Anar I.

anar.ayaganova@aogu.edu.kzhttps://orcid.org/0009-0009-9712-6737Kurmangaliyev

Darkhan Zh.

d.kurmangaliyev@kmge.kzhttps://orcid.org/0009-0000-6898-1907Abilgaziyeva

Aliya Sh.

aliya-abilgaz@mail.ruhttps://orcid.org/0009-0001-8496-6931Dukessova

Nadezhda K.

n.dukessova@kmge.kzhttps://orcid.org/0009-0009-7198-731X

Atyrau Oil and Gas UniversityKMG Engineering

30062026

825973

08082025

15052026

2026

Background: In recent decades, artificial intelligence technologies (hereinafter – AI) have been rapidly integrated into the oil and gas industry, covering key stages of geological exploration, geophysical data interpretation, reservoir modeling, and field development. Modern methods of big data analysis, machine learning, and intelligent control systems make it possible to improve the accuracy of interpreting geological, geophysical, and well logging data, reduce uncertainty in engineering decision-making, minimize operational risks, and optimize hydrocarbon exploration and production processes. Aim: This article examines contemporary areas of AI application in the oil and gas industry, with a particular focus on the tasks of automated interpretation of well logging data (hereinafter – WL), classification of lithological rock composition, reconstruction of logging curves, and digitalization of geological exploration processes. An analysis of global experience in implementing AI technologies in the field of well logging, processing and interpretation of geological and geophysical information is conducted, and integrated software solutions and digital platforms of leading international oilfield service and oil and gas companies are also reviewed. Materials and Methods: Particular attention is given to the practical experience of applying machine learning methods at KMG Engineering LLP for automated lithology classification based on well logging data. Within the framework of the study, various machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, and other machine learning algorithms, were tested using data from more than 100 wells. The study also considers the specifics of data preparation and cleaning, the formation of training and test datasets, as well as issues related to incompleteness, heterogeneity, and the low quality of historical geological and geophysical data. Results: The results of the study demonstrated that the application of ensemble methods and gradient boosting algorithms makes it possible to achieve high accuracy in lithological type classification and effectively automate the interpretation of well logging data. The best results were obtained using the Random Forest algorithm, which demonstrated high robustness and predictive performance under real production data conditions. Particular attention is also given to the integration of trained models into corporate information systems for operational lithology prediction and support of geological and technical decision-making. Conclusion: It is concluded that the implementation of artificial intelligence technologies represents one of the key directions of digital transformation in the oil and gas industry of Kazakhstan. The use of AI makes it possible to improve the efficiency of geological exploration activities, accelerate data processing and interpretation, increase hydrocarbon recovery factors, and reduce field development costs under conditions of increasing geological complexity and declining resource base quality.

artificial intelligencemachine learningoil and gas industrywell loggingwell logginglogginglithologyRandom ForestXGBoostautomated interpretationdigitalizationgeological explorationneural networksdata analysisrock classification

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искусственный интеллектмашинное обучениенефтегазовая отрасльгеофизические исследования скважингеофизические исследования скважинкаротажлитологияRandom ForestXGBoostавтоматизированная интерпретацияцифровизациягеологоразведканейронные сетианализ данныхклассификация пород

Alaudah Y, Alfarraj M, AlRegib G. Machine learning for characterization of subsurface geology from seismic images. Journal of Applied Geophysics. 2019;164:225–239. doi: 10.1016/j.jappgeo.2019.02.005.

Qi J, Zhang B, Lyu B. Seismic attribute selection for machine-learning-based facies analysis. Geophysics. 2020;85(2):017–035. doi: 10.1190/geo2019-0223.1.

Ronkin MV, Akimova EN, Misilov VE, Reshetnikov KI. Review on Application of Deep Neural Networks and Parallel Architectures for Rock Fragmentation Problems. Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering. 2023;12(4):5–54. doi: 10.14529/cmse230401. (In Russ).

slb.com [Internet]. SLB. Techlog Wellbore Software Platform [cited 2025 Feb 12]. Available from: www.slb.com/products-and-services/delivering-digital-at-scale/software/techlog

slb.com [Internet]. SLB. Remote Operations and Digital Logging Solutions [[cited 2025 Feb 12]. Available from: www.slb.com

neftegaz.ru [Internet]. Neftegaz.RU. Halliburton unveils Intelligent digital platform for drilling and logging wells [cited 2025 Feb 12]. Available from: neftegaz.ru/news/Oborudovanie/701778-halliburton-predstavila-intellektualnuyu-tsifrovuyu-platformu-dlya-bureniya-i-karotazha-skvazhin/?ysclid=m8vzcjblz7594922150. (In Russ).

Kaur H, Pham N, Fomel S, et al. A deep learning framework for seismic facies classification. Interpretation. 2022;11(4):1–47. doi: 10.1190/int-2022-0048.1.

Abdullina KM, Spivak AI. U-NET Architecture Neural Network for Localization of Digital Images Integrity Violation. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2020;20(3):425–431. doi: 10.17586/2226-1494-2020-20-3-425-431.

Volkov SS, Kurochkin II. Primeneniye glubokih neyronnykh setey, osnovannykh na LSTM, dlya resheniya zadach klassifikatsii. International Scientific Conference Proceedings “Advanced Information Technologies and Scientific Computing”; 2021 May 24–27; Samara, Russia. Available from: repo.ssau.ru/jspui/handle/123456789/2911. (In Russ).

10.

kmg.kz [Internet]. KazMunayGas. Intellektual’nyye mestorozhdeniya. Keysy KMG [cited 2025 Feb 25]. Available from: www.kmg.kz/ru/press-center/articles/intellectual-fields-cases/. (In Russ).

11.

tengizchevroil.com [Internet]. TCO. How artificial intelligence is used in Tengizchevroil [cited 2025 Feb 25]. Available from: www.tengizchevroil.com/ru/tco-news/detail/2024/04/06/how-artificial-intelligence-is-used-in-tengizchevroil. (In Russ).

12.

forbes.kz [Internet]. Forbes Kazakhstan. Caspian Oil: Digitalization of the Field and Corporate Social Responsibility [cited 2025 Feb 25]. Available from: forbes.kz/articles/kaspiy_neft_tsifrovizatsiya_mestorojdeniya_zabota_o_kadrah_i_regione. (In Russ).