Development of Drilling Optimization Models for Autonomous Rotary Drilling Systems

The growing global energy demand and strict environmental policies, motivates the use of technology and performance improvement techniques in drilling operations. In the traditional drilling method, parameter optimization depends on the effectiveness of human-driller. Although existing work has identified the significance of upscaling from manual drilling to autonomous drilling system, but little has been done to support this transition. This work presents optimization models for an autonomous rotary drilling system, controlled by a self-tuning, multivariant controller that uses machine learning optimization strategy. The method determines the drilling medium from real-time measurement by estimating the unconfined compressive strength (UCS) from the latest data uploaded via the mud pulse telemetry (MPT) and adjust optimal setpoint based on model output. In the study, four machine learning algorithms were used to predict UCS including artificial neutral network (ANN), Category boast (CB), Support vector machine (SVR) and Randon Forest. Whilst Physics based empirical models with ANN were used to predict the drill rate. Results showed that machine learning (ML) application improves the prediction quality of drill rate and UCS with ANN and Catboast as best ML predictors. The coefficient of determination (R2) of 0.95 ROP prediction and (R2) for test dataset of 0.77 and 070 for UCS prediction using ANN and Catboast respectively. The Q-learning algorithm which uses the value function to search for optimal operating parameter at different Lithologies through dynamic programming, returns decisions for optimal drill rate at respective drilling states consequently improving the efficiency of rotary drilling process in terms of cost and time