Analyzing AI Applications in Oil and Gas Industry: Breakthrough Tools, Methods and Platforms
Keywords:
Computing methodologies, Artificial intelligence, Machine learning, Oil and gas industry
Abstract
The article explores the transformative impact of artificial intelligence (AI) on the oil and gas industry, highlighting how AI technologies are being leveraged to optimize operations, improve safety, and reduce costs. It examines a range of AI tools, methods, and platforms that are revolutionizing various stages of the oil and gas value chain, including exploration, drilling, production, and maintenance. Key applications discussed include predictive maintenance, reservoir modeling, automated drilling systems, and real-time data analytics. The paper also addresses the challenges of AI integration in legacy systems and emphasizes the need for industry-wide collaboration and regulatory support to fully realize AI's potential. Overall, the study underscores AI as a breakthrough enabler for digital transformation in the energy sector.
References
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Alyaev, S., Fossum, K., Djecta, H. E., Tveranger, J., & Elsheikh, A. (2024, September). DISTINGUISH Workflow: A New Paradigm of Dynamic Well Placement Using Generative Machine Learning. In ECMOR 2024 (Vol. 2024, No. 1, pp. 1-16). European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214-4609.202437018.
Amadi, K., Iyalla, I., & Prabhu, R. (2021, August). Modeling and predicting performance of autonomous rotary drilling system using machine learning techniques. In SPE Nigeria Annual International Conference and Exhibition (p. D021S002R008). SPE. https://onepetro.org/SPENAIC/proceedings abstract/21NAIC/2 21NAIC/D021S002R008/465724.
Dauvergne, P. (2020). AI in the Wild: Sustainability in the Age of Artificial Intelligence. MIT Press. https://books.google.com/books?hl=vi&lr=&id=M1X6DwAAQBAJ&oi=fnd&pg=PR9&dq=AI+leading+to+faster,+cheaper+oil+production,+executives+say&ots=gMz3YbRHFx&sig=XdjYudFFsra0DyeGgpslLMswsY8.
Deif, A., & Vivek, T. (2022). Understanding AI application dynamics in oil and gas supply chain management and development: A location perspective. HighTech and Innovation Journal, 3, 1-14.
Ehret, T., De Truchis, A., Mazzolini, M., Morel, J. M., D’aspremont, A., Lauvaux, T., ... & Facciolo, G. (2022). Global tracking and quantification of oil and gas methane emissions from recurrent sentinel-2 imagery. Environmental Science & Technology, 56(14), 10517-10529. https://doi.org/10.1021/acs.est.1c08575.
Fossum, K., Alyaev, S., Tveranger, J., & Elsheikh, A. (2021). Deep learning for prediction of complex geology ahead of drilling. In Computational Science–ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part II 21 (pp. 466-479). Springer International Publishing. https://doi.org/10.1007/978-3-030-77964-1_36.
Gowekar, G. S. (2024). Artificial intelligence for predictive maintenance in oil and gas operations. World J Adv Res Rev, 23(3), 1228-33.
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., ... & Frankenberg, C. (2016). Satellite observations of atmospheric methane and their value for quantifying methane emissions. Atmospheric Chemistry and Physics, 16(22), 14371-14396.
Lai, N. S., Tew, Y. S., Zhong, X., Yin, J., Li, J., Yan, B., & Wang, X. (2023). Artificial intelligence (AI) workflow for catalyst design and optimization. Industrial & Engineering Chemistry Research, 62(43), 17835-17848. https://doi.org/10.1021/acs.iecr.3c02520.
Latrach, A. (2020). Application of Deep Learning for Predictive Maintenance of Oilfield Equipment. https://doi.org/10.13140/RG.2.2.12595.09762.
Ma, L., Han, L., & Feng, Q. (2024). Deep learning for high-resolution seismic imaging. Scientific Reports, 14(1), 10319.
Martin, L., Whitehouse, N., Yiu, S., Catterson, L., & Perera, R. (2024). Better call gpt, comparing large language models against lawyers. arXiv preprint arXiv:2401.16212. https://doi.org/10.48550/arXiv.2401.16212.
Parmar, K. J., Jain, R., Palaniappan, D., & Premavathi, T. (2025). Use Cases of AI in the Oil and Gas Industry. In Revolutionizing AI and Robotics in the Oil and Gas Industry (pp. 397-420). IGI Global Scientific Publishing. https://www.igi-global.com/chapter/use-cases-of-ai-in-the-oil-and-gas-industry/376737.
Qamar, R., & Zardari, B. A. (2025). Revolutionizing AI and Robotics in the Oil and Gas Industry. In Revolutionizing AI and Robotics in the Oil and Gas Industry (pp. 145-164). IGI Global Scientific Publishing. https://www.igi-global.com/chapter/revolutionizing-ai-and-robotics-in-the-oil-and-gas-industry/376725.
Rebello, C. M., Jäschkea, J., & Nogueira, I. B. (2023). Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry. arXiv preprint arXiv:2311.12755. https://doi.org/10.48550/arXiv.2311.12755.
Wang, M., Su, X., Song, H., Wang, Y., & Yang, X. (2025). Enhancing predictive maintenance strategies for oil and gas equipment through ensemble learning modeling. Journal of Petroleum Exploration and Production Technology, 15(3), 46. https://doi.org/10.1007/s13202-025-01931-x.
Wang, X., Yuan, S., Magableh, S. K., Dawaghreh, O., Wang, C., & Wang, L. Y. (2025). Graph-Based Simulation Framework for Power Resilience Estimation and Enhancement. arXiv preprint arXiv:2411.16909. https://doi.org/10.48550/arXiv.2411.16909.
Zerafa, C., Galea, P., & Sebu, C. (2025). Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging. arXiv preprint arXiv:2502.17585. https://doi.org/10.48550/arXiv.2502.17585.
Zhong, Y., Chen, X., Wang, Z., & Lin, R. F. Y. (2024). The nexus among artificial intelligence, supply chain and energy sustainability: A time-varying analysis. Energy Economics, 132, 107479.
Published
2025-05-29
How to Cite
Pham Van, D., & Truong Xuan, B. (2025). Analyzing AI Applications in Oil and Gas Industry: Breakthrough Tools, Methods and Platforms. European Journal of Science, Innovation and Technology, 5(3), 56-64. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/663
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Copyright (c) 2025 Pham Van Dong, Truong Xuan Binh
This work is licensed under a Creative Commons Attribution 4.0 International License.
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