An Intrusive Hybrid-Analytics and Modelling with Deep-Learning for Efficient and Accurate Predictions of Hole-Cleaning Process during Wellbore Drilling Simulations

The paper aims at demonstrating a novel intrusive hybrid-analytics and modelling (HAM) that combines physics-based model and machine learning (ML) for predicting key variables in the monitoring of hole cleaning during drilling, and more specifically monitoring pressure/Equivalent Circulating Density (ECD) and cuttings volume fraction. Currently, for predicting the spatial-temporal evolution of circulating mud in real-time in the annulus during drilling and potentially anticipating hole cleaning issues, a low-resolution physics-based 1D model is utilized that solves multi-phase flow equations. This model is computationally efficient but susceptible to discrepancies with actual observations. These errors could be a result of numerical issues, unmodelled physics in the model, or inaccurate input to the model. Here, machine learning is used to learn the pattern in residuals between the low-resolution model and a higher fidelity calculation, as well as measurements. The results show that the inclusion of machine learning models for correcting the low-fidelity cutting transport model has helped to improve the accuracy of low-fidelity model in predicting pressure and cutting volume fractions : which are key variables for monitoring hole cleaning. The machine learning models (ANN and LSTM models) have shown good performance in learning and correcting various errors associated with the 1D model, like (a) the numerical errors, (i.e. the error resulting from coarser and finer time-scales for the cuttings volume fraction along the well), and (b) the error due to physics (i.e. the difference in predictions between hi-fidelity model and low-fidelity model for pressure), and (c) the error between measurements and predictions of low-fidelity model. The conclusion of the work is that the intrusive HAM approach combining deep-learning with physics-based approach has the potential to provide a robust and efficient replacement of unknown parts of complex physics in mathematical models for drilling. Future work may involve using this HAM-in-drilling approach in conjunction with an anomaly detection algorithm to enable real-time decision when an anomaly occurs.