Real-time incident detection in geothermal drilling through machine learning

Aira H. Aspiras, Sadiq J. Zarrouk, Ralph Winmill and Andreas W. Kempa-Liehr

Renewable Energy, 2025, vol. 250, issue C

Abstract: Geothermal energy, while a reliable baseload low-carbon resource, only comprise a small fraction of global renewable capacity due to high upfront costs and resource risks. Drilling wells accounts for ∼60 % of capital investment costs, thus finishing wells on-time and within budget has always been a crucial challenge for operators and challenges like fault structures, severe lost circulation, and high temperatures inherent to geothermal systems make this difficult. Early detection is crucial in taking corrective actions before problems escalate and leveraging machine learning (ML) technologies offers the potential to identify patterns that precede hole-related non-productive time incidents, such as stuckpipes or borehole instability.

Keywords: Geothermal drilling; Incident detection; Risk reduction; Machine learning; Stuckpipe (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:250:y:2025:i:c:s096014812500922x

DOI: 10.1016/j.renene.2025.123260

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