Performance of an Advanced Intelligent Control Strategy in a Dynamic Positioning (DP) System Applied to a Semisubmersible Drilling Platform

Oil drilling and extraction platforms are currently being used in many offshore areas around the world. Whilst those operating in shallow seas are secured to the seabed, for deeper water operations, Dynamic Positioning (DP) is essential for the platforms to maintain their position within a safe zone. Operating DP requires intelligent and reliable control systems. Nearly all DP accidents have been caused by a combination of technical and human failures; however, according to the International Marine Contractors Association (IMCA) DP Incidents Analysis, DP control and thruster system failures have been the leading causes of incidents over the last ten years. This paper will investigate potential operational improvements for DP system accuracy by adding a Predictive Neural Network (PNN) control algorithm in the thruster allocation along with a nonlinear Proportional Integral derivative (PID) motion control system. A DP system’s performance on a drilling platform in oil and gas deep-water fields and subject to real weather conditions is simulated with these advanced control methods. The techniques are developed for enhancing the safety and reliability of DP operations to improve the positioning accuracy, which may allow faster response to a critical situation during DP drilling operations. The semisubmersible drilling platform’s simulation results using the PNN strategy show improved control of the platform’s positioning

Advanced Intelligent Control Strategy in Dynamic Positioning (DP) System Applied to a Semi-Submersible Drilling Platform in The North Sea

Dynamic Positioning (DP) systems play a crucial role in oil and gas drilling and production floaters used globally for deep-water operations. Drilling operations need to maintain automatic positioning of the platform in the horizontal-plane within the safe zone. Operating DP systems typically require highly responsive control systems when encountering prevailing weather conditions. However, DP incident analysis demonstrates that control and thruster failures have been the leading causes of accidents for the past two decades, according to the International Marine Contractors Association (IMCA). In this paper, a Predictive Neural Network (PNN) strategy is proposed for thruster allocation on a platform; it has been developed by predicting the platform response and training the network to transform the required force commands from a nonlinear Proportional Integral Derivative (PID) motion controller for each thruster. The strategy is developed for increasing safety and zone keeping of DP-assisted-drilling operations in harsh weather. This is done by allowing the platform to recover the position more rapidly whilst decreasing the risk of losing the platform position and heading, which can lead to catastrophic damage. The operational performance of the DP system on a drilling platform subjected to the North Sea real environmental conditions of wind, currents and waves, is simulated with the model incorporating the PNN control algorithm, which deals with dynamic uncertainties, into the unstable conventional PID control system for a current drilling semi-submersible model. The simulation results demonstrate the improvement in DP accuracy and robustness for the semi-submersible drilling platform positioning and performance using the PNN strateg