Proactive Hybrid Risk Assessment Framework for Geothermal Drilling: Insights from the Utah Frontier Observatory for Research in Geothermal Energy Field

Summary

Persistent inefficiencies in geothermal drilling, including frequent tool failures, poor directional control, and prolonged nonproductive time (NPT), increase project costs and delay well delivery. To address these challenges, we present a probabilistic risk assessment framework for unconventional geothermal wells using data from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) field. The study integrates more than 550 validated NPT events from seven wells using more than 330 daily drilling reports (DDRs), directional survey data, mud logs, and end-of-well reports (EOWRs). Events were standardized, categorized, and analyzed to identify dominant failure modes, root causes, and mitigation opportunities. A Monte Carlo (MC)-based probabilistic workflow, implemented through Markov Chain MC (MCMC), was developed to quantify uncertainty in event frequency and duration, and to estimate future NPT distributions. The results show that five dominant categories—rig equipment failures, directional tool malfunctions, logging tool issues, hole cleaning/wellbore instability, and operational/personnel errors—account for more than 60% of total NPT at FORGE. Well 16A (78)−32 recorded the highest cumulative NPT (420 hours), primarily due to repeated rig equipment failures, tool malfunctions, and operational inefficiencies. The proposed framework achieved approximately 90% posterior predictive coverage of aggregated NPT values, with observed data falling within the model’s P10–P90 credible intervals. A depth-indexed risk heat map was also generated to identify the expected contribution of each risk category by interval, supporting improved well planning, tool selection, and resource allocation. This study represents the first technical application of an MCMC-based probabilistic risk model for NPT analysis in the Utah FORGE field. The framework provides uncertainty-aware, data-driven risk prioritization and offers a scalable approach for reducing NPT and improving decision-making in geothermal drilling operations.