How AI is Transforming Oil and Gas Exploration

By Samudrapom DamReviewed by Laura ThomsonJul 3 2025

Despite growing interest in renewable sources, oil and gas exploration remains vital to meeting global energy demands. As reserves become harder to locate, the industry increasingly relies on digital technologies to enhance efficiency and accuracy.

Artificial intelligence (AI) is at the forefront of this transformation, reshaping how geological data is analyzed and decisions are made. This article explores the growing role of AI in oil and gas exploration, highlighting current applications, recent innovations, real-world implementations, and the broader impact on the industry.^1-7

Image Credit: m.afiqsyahmi/Shutterstock.com

The Role of AI in Oil and Gas Exploration

AI is revolutionizing oil and gas exploration by enhancing the accuracy, speed, and cost efficiency of identifying new reserves. It rapidly interprets large, complex datasets such as seismic surveys, well logs, and satellite imagery. Machine learning algorithms detect patterns and anomalies, enabling geologists to locate high-potential exploration zones with greater precision.¹

This data-driven approach improves decision-making and reduces the environmental impact and risks associated with traditional methods. A key application is in offshore drilling, where AI integrates diverse data sources to identify promising sites, minimizing unnecessary drilling. Moreover, AI enhances seismic data interpretation, supports optimal drilling location selection, and improves operational control. It also leverages historical data to forecast pricing trends and determine the most strategic timing for drilling and production activities.¹

Recent Studies and Technological Advancements

Recent studies and technological breakthroughs are rapidly advancing the use of AI in oil and gas exploration. These innovations enhance data analysis, improve accuracy, and streamline exploration workflows across the industry.^2-5 For instance, AI is transforming seismic imaging and data analysis in oil and gas exploration by addressing limitations of traditional methods, such as low resolution and challenges in complex environments like salt domes/fault zones.

Latest studies have shown that machine learning and deep learning techniques could process vast seismic datasets to reduce noise, enhance image clarity, and automate the identification of geological features, leading to improved accuracy and faster decision-making. These technologies improve operational efficiency and exploration success rates by minimizing the need for manual interpretation.²

A recent paper published in the Proceedings of the International Field Exploration and Development Conference 2024 introduced a cognitive computing platform developed to accelerate digital transformation in oil and gas exploration and development. The platform was built around five core elements— knowledge, data, computing power, algorithms, and scenarios—and was designed to support three user groups: AI model developers, business personnel, and advanced software engineers. It featured four main functions: knowledge graph, AI computing, data preprocessing, and an AI service marketplace.³

The system offered an end-to-end environment for AI development, including data handling, machine learning, model creation, deployment, and application. After more than two years of operational use, the platform demonstrated successful implementation across several major oilfields, including Daqing, Changqing, Xinjiang, Southwest, and Dagang. It effectively empowered exploration teams with intelligent tools and significantly reduced barriers to AI adoption within the industry.³

A paper published in Natural Resources Research proposed a novel method using a self-attention convolutional neural network (SACNN) combined with transfer learning and multi-component seismic attribute data (MSAD) to predict gas probability distribution (GPD) in tight sandstone gas reservoirs with high precision.

The researchers developed a framework using synthetic oil and gas reservoir data to train the SACNN model, then applied transfer learning to adapt it to real seismic data. This approach expanded the data samples and optimized model performance.⁴

The SACNN model outperformed conventional machine learning models, showing lower prediction errors and superior accuracy on testing datasets. Uncertainty analysis confirmed the model’s robustness and efficiency. The study demonstrated the effectiveness of combining self-attention mechanisms, seismic attribute data, and transfer learning in building data-driven models for unconventional reservoir prediction and energy resource utilization.⁴

Another paper recently published in Natural Resources Research introduced a Spatiotemporal Network (STNet), a dual-branch deep learning framework that integrated spatial feature graph methods with time-sequential prediction techniques to improve lithology classification.

STNet captured complex spatial relationships in well-logging data through a graph-based structure while leveraging temporal modeling to analyze dynamic time series properties. This comprehensive approach enhanced the understanding of subsurface fluid attributes. Tested on wells from the Tarim and Daqing oilfields, STNet achieved a high classification accuracy of up to 96.83% and outperformed seven other advanced models. The framework also demonstrated strong generalizability and significantly improved lithology identification.⁵

Commercial Applications and Industry Adoption of AI in Oil and Gas

Commercial applications and industry adoption of AI in oil and gas are rapidly expanding, driven by the need for efficiency, accuracy, and cost reduction. For instance, ExxonMobil’s AI-equipped robots can detect oil leaks effectively, reducing the risk of environmental damage and aiding marine life protection.

The company uses AI to enhance decision-making in resource exploitation, lower environmental risks, and optimize production through machine learning. In partnership with Microsoft, ExxonMobil is also streamlining operations in the Permian Basin.¹

Similarly, Shell employs deep learning neural networks to identify salt boundaries, leading to improved interpretation of hydrocarbon flow and the creation of more accurate subsurface models, freeing experts to focus on complex tasks.

BP uses AI and machine learning to refine exploration and production, reduce costs, and manage resources efficiently through big data analysis and advanced simulations.

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Chevron relies on big data and AI to enhance decision-making in natural resource management, improve operational efficiency, and boost safety through simulation systems.¹

Halliburton, a leading patent holder in oil exploration AI, offers the DS365.ai software to improve drilling and production workflows. In 2022, Halliburton partnered with the Saudi Data and Artificial Intelligence Authority to expand its AI solutions. It also provides the LOGIX Autonomous Drilling Platform, which uses AI to redevelop aging oil fields. Other notable AI patent holders in this space include Schlumberger and Saudi Arabian Oil.⁶

Challenges of AI Implementation in Oil and Gas Exploration

Despite AI’s significant potential in oil and gas exploration, its implementation faces several challenges.

Key issues include poor data quality due to limited resolution, missing values, and lack of standardization, which undermine model accuracy.

AI models struggle to generalize across diverse geological settings, with deep learning prone to overfitting.

Limited interpretability and the absence of physical constraints reduce trust in predictions. Furthermore, high computational demands conflict with the real-time decision-making needs of exploration.

Practical integration barriers, including software incompatibility and a lack of standardized tools, hinder deployment. These challenges limit the scalability and real-world impact of AI in the industry.^2,7

Conclusion

AI is transforming oil and gas exploration by enhancing data analysis, improving decision-making, and increasing operational efficiency. Despite challenges like data quality and integration barriers, ongoing innovations drive broader industry adoption. As AI technologies mature, they will play an increasingly critical role in meeting global energy demands sustainably and efficiently.

References and Further Reading

1. Hanif, H. R. (2024). The Role of Artificial Intelligence in Optimizing Oil Exploration and Production. Eurasian Journal of Chemical, Medicinal and Petroleum Research, 3 (5), 176-190. DOI: /EJCMPR/2024124442, https://www.ejcmpr.com/article_210864.html
2. Daramola, G. O., Jacks, B. S., Ajala, O. A., & Akinoso, A. E. (2024). Enhancing oil and gas exploration efficiency through AI-driven seismic imaging and data analysis. Engineering Science & Technology Journal, 5(4), 1473-1486. DOI: 10.51594/estj.v5i4.1077, https://fepbl.com/index.php/estj/article/view/1077
3. Lin, X., Gong, R. B., Mi, L., Xu, C., & Hui, S. Y. (2025). Implementation of AI Platform for Oil and Gas Exploration and Development. Proceedings of the International Field Exploration and Development Conference 2024, 763-770. DOI: 10.1007/978-981-96-4759-0_58, https://link.springer.com/chapter/10.1007/978-981-96-4759-0_58
4. Yang, J., Lin, N., Zhang, K., Jia, L., & Fu, C. (2024). A Framework for Predicting the Gas-Bearing Distribution of Unconventional Reservoirs by Deep Learning. Natural Resources Research, 33(4), 1625-1655. DOI: 10.1007/s11053-024-10345-1, https://link.springer.com/article/10.1007/s11053-024-10345-1
5. Pang, Q., Chen, C., Sun, Y., & Pang, S. (2025). STNet: Advancing Lithology Identification with a Spatiotemporal Deep Learning Framework for Well Logging Data. Natural Resources Research, 34(1), 327-350. DOI: 10.1007/s11053-024-10413-6, https://link.springer.com/article/10.1007/s11053-024-10413-6
6. Artificial intelligence: who are the leaders in oil exploration AI for the oil & gas industry? [Online] Available at https://www.offshore-technology.com/data-insights/innovators-ai-oil-exploration-ai-oil-gas/?cf-view (Accessed on 25 June 2025)
7. Chen, F. et al.. (2025). A Review of AI Applications in Unconventional Oil and Gas Exploration and Development. Energies, 18(2), 391. DOI: 10.3390/en18020391, https://www.mdpi.com/1996-1073/18/2/391
   

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