An Improved Dynamic Optimization Control System for the Drilling Rate of Penetration (ROP) and Its Industrial Application

Abstract

Optimization control of the rate of penetration (ROP) is crucial in the drilling process due to its vital role in improving the drilling efficiency and safety. In this paper, an improved dynamic optimization control system for ROP is proposed and successfully applied to a drilling site. First, a three layers (intelligent optimization layer, basic automation layer, and process monitoring layer) system framework is proposed according to the drilling characteristics. After that, “If-Then” strategy is used as the method to identify the rotary drilling condition from four different working conditions. Moving-window strategy is selected to establish the dynamic ROP model that can better adapt to environmental changes. Moreover, Jaya algorithm, a new meta-heuristic optimization method is introduced to solve the dynamic ROP optimization issue. In the simulations, the optimization performance of the proposed system is better than moving window-extreme learning machine-hybrid bat algorithm with more stable (fewer switch times) drilling operational parameters. Finally, in an industrial application to a drilling process in Dandong area, Northeast China, the ROP was improved by 20.79% compared with manual operation. Both simulation and industrial application results indicated that the proposed system has significantly improved the drilling efficiency and safety.