Title:

Recent Trends in Artificial Intelligence for Subsurface Geothermal Energy

Authors:

Mohammad Jabs ALJUBRAN, Chinedu NWOSU, Esuru Rita OKOROAFOR, Connor Macrossie SMITH, Karen OCHIE, Halldora GUDMUNDSDOTTIR

Key Words:

artificial intelligence, machine learning, deep learning, geothermal energy, drilling, reservoir characterization, reservoir engineering, seismicity

Conference:

Stanford Geothermal Workshop

Year:

2022

Session:

Emerging Technology

Language:

English

Paper Number:

Aljubran1

File Size:

866 KB

View File:

Abstract:

This paper reviews the trends in the application of Artificial Intelligence (AI) for the drilling and subsurface aspects of the Geothermal Industry. The applications over the past two decades (from 2001 to 2021) were reviewed to understand what AI algorithms are being applied, the kind of problems being addressed with AI, and where there might be opportunities to apply AI in the geothermal industry. The study showed that there has been a steady increase in the application of AI in the geothermal industry over the past 20 years with spikes occurring every five years. Years 2020 and 2021 saw a significant rise in publications on AI for the Geothermal Industry. Several domains for subsurface geothermal energy were reviewed and it was seen that reservoir characterization had the most significant applications of AI in the geothermal industry. This study shows that there is an opportunity to improve and expand the adoption of AI in the geothermal energy industry by leveraging the successful utilization of AI algorithms across similar domains in the oil and gas industry.


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