<p>Dynamic Positioning (DP) systems play a crucial role in oil and gas drilling and production floaters which are used globally for deep-water operations. It is essential for drilling operations to maintain automatic positioning of the rig/floater (platform) in the horizontal-plane within the safe zone. Operating DP systems typically requires highly responsive control systems when encountering prevailing weather conditions. However, DP incidents analysis demonstrates that control and thrusters 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 by allowing the platform to recover the position faster, and improve environmental pollution protection by decreasing the risk of losing the platform position and heading which can lead to catastrophic damages. 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 DP accuracy and robustness improvement of the semi-submersible drilling platform positioning and performance using the PNN strategy.</p>

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

Paper Proceedings: https://asmedigitalcollection.asme.org/OMAE/OMAE2021/volume/85116

OMAE 2021

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