Online Parameter Calibration in Drilling Hydraulics Model

Abstract Accurate prediction and real-time monitoring of downhole conditions are essential for the efficient and safe execution of drilling operations. Key parameters influencing these predictions include drilling fluid properties, different wellbore geometry, existing cuttings in the annulus, eccentricity of drill string and pipe rotation. Traditional hydraulics models typically utilize static equations, which may not adapt well to the varying conditions encountered during drilling. This can lead to inaccurate predictions, increased non-productive time (NPT), and potential safety risks. This paper presents an online calibration approach that dynamically updates the hydraulics model using sensor readings, ensuring continuous, real-time optimization. By doing so, the approach improves the accuracy of downhole predictions and enhances decision-making capabilities in drilling operations. The novelty of this research lies in its integration of physics-based governing equations with data-driven techniques for real-time calibration, providing an adaptive and responsive system. The proposed calibration method integrates drilling hydraulics model with model reference adaptive control (MRAC) architecture to monitor variations in drilling parameters. The calibration process is adaptive, meaning it can continuously respond to changing conditions, such as variations in flow rates, cuttings concentration, and wellbore geometry, that static models might fail to capture. This approach involves utilizing a hydraulic model, which is recursively updated using real-time measurements and a hybrid combination of physics-based and data-driven modeling techniques. The proposed modeling framework is simulated using drilling data from a case study. The results indicate that the online calibration method delivers accurate frictional pressure loss and bottom hole pressure predictions. By dynamically updating the coefficients in the hydraulic model, the system can account for real-time changes, reducing discrepancies between predicted and actual conditions. This leads to better management of drilling parameters, minimizes NPT, and enhances safety by providing more reliable data for decision-making. The approach demonstrates robustness and potential applicability across various drilling environments, including high-pressure, high-temperature (HPHT) conditions and complex well geometries.