Applications of artificial intelligence in geothermal resource exploration: a review.

AlGaiar, Mahmoud; Hossain, Mamdud; Petrovski, Andrei; Lashin, Aref; Faisal, Nadimul

Abstract

Artificial intelligence (AI) has become increasingly important in geothermal exploration, significantly improving the efficiency of resource identification. This review examines current AI applications, focusing on the algorithms used, the challenges addressed, and the opportunities created. In addition, the review highlights the growth of machine learning applications in geothermal exploration over the past decade, demonstrating how AI has improved the analysis of subsurface data to identify potential resources. AI techniques such as neural networks, support vector machines, and decision trees are used to estimate subsurface temperatures, predict rock and fluid properties, and identify optimal drilling locations. In particular, neural networks are the most widely used technique, further contributing to improved exploration efficiency. However, the widespread adoption of AI in geothermal exploration is hindered by challenges such as data accessibility, data quality, and the need for tailored data science training for industry professionals. Furthermore, the review emphasizes the importance of data engineering methodologies, data scaling, and standardization to enable the development of accurate and generalizable AI models for geothermal exploration. It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources. By effectively addressing key challenges and leveraging AI technologies, the geothermal industry can unlock opportunities for cost-effective and sustainable power generation.

Citation

ALGAIAR, M., HOSSAIN, M., PETROVSKI, A., LASHIN, A. and FAISAL, N. 2024. Applications of artificial intelligence in geothermal resource exploration: a review. Deep underground science and engineering [online], 3(3): geothermal energy, pages 269-285. Available from: https://doi.org/10.1002/dug2.12122

• Journal Article Type: Article
• Acceptance Date: Aug 13, 2024
• Online Publication Date: Sep 4, 2024
• Publication Date: Sep 30, 2024
• Deposit Date: Aug 13, 2024
• Publicly Available Date: Aug 13, 2024
• Journal: Deep underground science and engineering
• Print ISSN: 2097-0668
• Electronic ISSN: 2770-1328
• Publisher: Wiley Open Access
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 3
• Pages: 269-285
• DOI: https://doi.org/10.1002/dug2.12122
• Keywords: Geothermal energy; Geothermal exploration; Hidden/blind geothermal resources; Geothermometry; Artificial intelligence; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2433945