Physics-Informed Machine Learning Model for Real-Time Downhole Temperature Monitoring in HPHT and Geothermal Drilling

Abstract Predicting downhole temperature (DHT) in real time is crucial for safe and efficient operations in geothermal and high-pressure/high-temperature (HPHT) drilling. Although numerical models accurately capture DHT transients, their high computational cost limits their real-time use. Conversely, machine learning (ML) models offer speed but often lack embedded physical constraints, impairing generalization and interpretation. To address these limitations, this study proposes a hybrid, physics-informed machine learning approach that integrates mechanistic accuracy with ML efficiency for practical, interpretable DHT monitoring and forecasting. Physics-informed neural networks (PINNs) are used to integrate embedded equations derived from the one-dimensional conservation equations for mass, momentum, and energy, directly into the network's learning frame through an augmented loss function that supplements the standard data-driven loss function. This physics-guided approach leverages physical principles to enhance learning from limited data while simultaneously preserving model interpretability. The hybrid model is evaluated by comparing its outputs to numerical simulation results from a thermo-hydraulic model previously validated using field data from the Utah FORGE geothermal site, as reported in prior literature. Additionally, a parametric analysis was conducted to assess the model's predictive capability under various temperature management strategies reported in the literature. Modeling results show exceptional precision in capturing the transient behavior of DHT compared to the reference numerical models. The developed PINNs model leverages differentiable, physics-based constraints to achieve rapid convergence during the training and validation process, significantly reducing computational time relative to conventional numerical methods without compromising accuracy. In all scenarios, the model captured the complete profile of temperature over depth and time as well as the DHT with a mean absolute error (MAE) ~ 1℃ in training and testing, compared to the thermo-hydraulic results. In comparisons across multiple drilling case scenarios, PINNs achieved near-instantaneous simulation times and superior computational efficiency relative to the numerical model. Additionally, the model demonstrated reliable mud temperature profile predictions under varying conditions, including different pump rates, mud inlet temperatures, demonstrating strong generalization and potential for real-time geothermal well simulation. The developed hybrid model uniquely integrates mechanistic thermo-hydraulic modeling with a rapid ML approach to achieve real-time, precise downhole temperature estimations and forecasting. Consequently, it enables proactive temperature control by optimizing cooling strategies and mitigating downhole tool failures caused by elevated temperatures in geothermal and HPHT wells.