Improving Efficiency Under Safety Constraints for Drilling Process Optimization Using Economic Model Predictive Control With Zone Tracking

During drilling, the driller cannot continuously and reliably regulate manipulated variables such as surface weight on bit (WOBs), surface revolutions per minute (RPMs), and mud pump flow rate. This results in optimal drilling parameters that cannot be obtained in real time, which will affect drilling safety and efficiency. Therefore, we consider an integrated coupling model to simultaneously describe the response of rate of penetration (ROP) and bottom hole pressure (BHP). With this model, we design an economic model predictive control with zone tracking (ZEMPC) algorithm combining the optimization and control layers to reduce the hydraulic mechanical specific energy (HMSE) while accurately regulating the BHP and ensuring efficient hole cleaning during operation. This algorithm is compared with another hierarchical control structure. The upper layer of this structure implements steady-state optimization for HMSE, and the optimal set value of BHP and ROP is dynamically tracked in the lower layer using nonlinear model predictive control (NMPC). A performance comparison reveals that both control algorithms can track formation pressure with acceptable accuracy, and the concentration of bottom hole cuttings is less than 5% during improving ROP. ZEMPC based on $l_{1}$ norm constraint can better improve the economic performance of key variables by increasing control redundancy and is not affected by the decision cycle. The robustness of the two algorithms is studied in case of errors in mud density and friction parameters. It is found that the ZEMPC based on the $l_{1}$ norm constraint is less sensitive to error when the model is mismatched.