Dynamic Optimization-Based Intelligent Control System for Drilling Rate of Penetration (ROP): Simulation and Industrial Application

Optimization control of the rate of penetration (ROP) is crucial due to its vital role in maximizing the drilling efficiency. In this article, a dynamic optimization-based intelligent control system for drilling ROP is proposed considering the drilling characteristics. First of all, the framework of the proposed system is described, which has two layers (intelligent optimization layer and basic automation layer). In the former layer, two stages (ROP modeling stage and ROP optimization/implementation stage) are executed alternatively by using the moving window strategy. Moving window-Extreme learning machine and tenfold cross validation are used to establish the dynamic model between the rotation speed (RPM), weight on bit (WOB), depth, and ROP. In addition, hybrid bat algorithm is introduced to search the controllable parameters while the inputs for ROP optimization are RPM, WOB, depth, constraints, and ROP model, and the outputs are the optimized RPM and optimized WOB. After that, the outputs are recommended to the driller to be used as the setpoints of the basic automation layer. Comparison results of simulation with seven well-known methods demonstrate the effectiveness of the proposed system. In addition, in an industrial application to a real-world drilling process in the Xiangyang area, Central China, the ROP was improved by 15.9%.

Automated summary

In this article, a dynamic optimization-based intelligent control system for maximizing drilling efficiency by optimizing rate of penetration (ROP) is proposed. The system utilizes a moving window strategy to establish a model between rotation speed, weight on bit, depth, and ROP, and employs a hybrid bat algorithm for parameter search. Comparison results with other methods demonstrate the effectiveness of the proposed system.