An Intelligent Control Approach for Oil Drilling Processes

Abstract

The paper presents an intelligent control and optimization framework for managed pressure drilling systems. The nonlinear drilling process model was configured in a closed-loop feedback control framework to optimize the oil drilling process performance. Two main process components, namely, the mud pump flow rate and the differential flow rate of the backpressure pump and the choke, are assumed to be the control inputs while the process down hole pressure rate is treated as the process output. The control, optimization and automation of the drilling process are investigated by designing an intelligent fuzzy logic controller in a tracking problem for real-time implementation, by utilizing the closed loop system tracking error and the error rate as the controller inputs and by generating incremental changes for the two process inputs. Although the proposed control system framework is inherently nonlinear due to the nonlinear process model and the nonlinear intelligent control, the process control input and output parameters have been successfully achieved. The proposed control framework simulation results clearly illustrate that the managed pressure drilling process can be optimized as a closed loop control tracking problem, effectively removing the need for complex controller design and allowing real-time implementation in manufacturing operations in operator support systems.