The economic value of real-time optimization of the drilling process has attracted extensive attention in the period of crude oil price fluctuation. Because of the complicated bottomhole conditions and coupling relationship among subsystems, it is difficult for the driller to continuously adjust the primary manipulation variables—weight on bit (WOB), revolutions per minute (RPM), and managed pressure drilling (MPD) equipment—which leads to unsatisfactory drilling effect and increased nonproductive time. Here, we propose an integrated control strategy based on economic model predictive control with zone tracking (ZoneEMPC), nonlinear model predictive control (NMPC), and Lyapunov-based model predictive control (LMPC) to optimize hydraulic mechanical specific energy (HMSE) while stabilizing bottomhole pressure (BHP).

During normal drilling, ZoneEMPC is used to minimize HMSE through operating RPM, WOB, mud pump flow, and choke-valve opening as well as its tracking performance, which ensures that the BHP is maintained within the pressure window. Moreover, the controller is switched to NMPC, which adjusts the BHP to quickly reach the new setting zone. The reason for controller switching is that the tracking accuracy of ZoneEMPC could decline when encountering high-pressure formation with gas invasion. As the BHP reaches the new pressure range, the controller is switched to ZoneEMPC again and the HMSE is reoptimized. In addition, the transmission rate of mud pulse telemetry is not enough to meet the real-time control in practical engineering. Therefore, the LMPC controller is used to ensure the closed-loop stability of the system when the downhole measurement data (e.g., BHP) delayed.

This study uses the drilling data of a vertical well located in Tarim, China, to verify the control strategy. The results indicated that the proposed integrated control strategy could improve the rate of penetration (ROP), especially in a formation with high rock strength. Meanwhile, the BHP could be adjusted efficiently and stably under different conditions. This work provides a unified framework integrating multiple system models for practical engineering, which has certain theoretical guiding significance for the automation and intelligent development of the oil and gas industry.

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