Digital Well-Control Twin for Pressure-Window Management, Kick and Loss Risk Assessment, and Hybrid Bottom-Hole Pressure Prediction

Well control during drilling requires continuous assessment of bottom-hole pressure (BHP) relative to the pressure window bounded by formation and fracture pressures. This study presents a reduced-order, physics-guided digital-twin framework for well-control decision support, kick and loss risk assessment, and hybrid BHP prediction. The framework is intended as a computational decision-support prototype rather than a fully deployed, real-time, field-validated digital twin. It combines pressure-window calculations, dimensionless risk indices, bounded machine-learning correction, scenario-based event simulation, an interactive engineering dashboard, and 3D safety-envelope visualization. The machine-learning layer was trained on a predominantly augmented drilling dataset containing 909 cases, including nine field-related baseline records and 900 synthetically generated cases, and was used as a constrained correction mechanism rather than a replacement for the physics-based model. On the held-out test set, the BHP regression model achieved R2 = 0.987, MAE = 108.6 psi, and RMSE = 215.7 psi, while the well-control status classifier achieved an accuracy of 98.35%. Scenario simulations reproduced representative kick-prone and loss-prone conditions and tracked the evolution of BHP, the Pressure Safety Index, the Kick Risk Index, and the Loss Risk Index. The results show that the proposed workflow can identify underbalanced states, quantify pressure margins, evaluate mud-weight sensitivity, and support visual interpretation of well-control risk. Further field validation, real-time data integration, uncertainty quantification, and robustness testing are required before operational deployment.