Real-Time Model–Data Fusion for Accurate Wellbore Pressure Prediction in HTHP Wells

Accurate wellbore pressure management is critical for safety and efficiency in deep drilling, where narrow pressure windows and extreme high-temperature, high-pressure (HTHP) conditions exist. Current methods, including direct measurement and model-based prediction, face limitations such as sensor reliability issues and inaccurate lab-derived parameters. Data-driven AI methods lack interpretability and generalize poorly. This study proposes a model-data fusion approach to address these issues. It integrates mechanistic models with real-time data using the Unscented Kalman Filter (UKF) for real-time parameter correction. Friction correction factors are introduced to continuously update and optimize the estimation of frictional pressure drop. Validated with field data, the model demonstrates high accuracy, with absolute percentage errors below 5% and mean absolute errors (MAPE) below 1%. It also shows strong robustness, maintaining low MAPE (1.10–2.15%) despite significant variations in frictional pressure drop distribution. This method significantly enhances prediction reliability for safer ultra-deep drilling operations.