A Digital Twin for Real-Time Drilling Hydraulics Simulation Using a Hybrid Approach of Physics and Machine Learning

Abnormal hydraulic event detection is essential for offshore well construction operations. These operations require model comparisons and real-time measurements. For this task, physics-based models, which need frequent manual calibration do not accurately capture all the hydraulic trends. The paper presents a method to overcome existing limitations by combining physics-based models with machine learning techniques, which are suited for time series forecasting. This method ensures accurate and reliable predictions during the forecasting period and helps remove the need for frequent manual calibration of the hydraulic input parameters.