Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post-2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field. Our research explores the growing significance of artificial intelligence (AI), particularly machine learning and deep learning, in the geothermal energy sector. We have identified an upward trend in AI applications from 2019 to 2022 and highlight its critical role in reservoir engineering, exploration, drilling, and production optimization. This paper offers insights into AI's potential to enhance efficiency and reduce costs in the geothermal industry. image Recent machine learning/deep learning advancements in geothermal development up to 2024. Case studies are presented on AI's practical impact in seismic detection and reservoir engineering. AI application trends in geothermal energy from 2000 to 2024 are analyzed. Future AI integration into geothermal drilling and production is explored.