Model complexity reduction and controller design for managed pressure drilling automation. (February 2023) - Naderi Lordejani, S.; Besselink, B.; Schilders, W.H.A.; van de Wouw, N.
Abstract: Automation of Managed Pressure Drilling (MPD) allows for fast and accurate pressure control in drilling operations. The achievable performance in automated MPD with model-based controllers is determined by the controller and, indirectly, also by the hydraulics model used for controller synthesis. On the one hand, such a hydraulics model should accurately capture essential flow dynamics of the system such as, e.g., wave propagation effects, for which typically complex models are needed. On the other hand, a suitable model should be simple enough to facilitate high-performance controller design as well as to support fast simulation studies supporting well scenario analysis. This paper shows that low-order models in terms of delay differential equations can effectively meet these requirements. Moreover, we propose a data-based model reduction technique to construct these low-order delay models. Next, based on this reduced-complexity model, a novel controller is designed to regulate the downhole pressure. Simulation results confirm that this controller outperforms existing pressure controllers in realistic drilling scenarios related to the mitigation of liquid kicks and mud losses encountered when drilling into high- or low-pressure zones. Highlights: A compact PDE model is presented for single phase flow managed pressure drilling processes. The PDE model is approximated by a reduced complexity model in terms of delay differential equations. The reduced complexity model captures the wave propagation effect with a high accuracy. Using the reduced model, an observer-based state-feedback controller is designed for downhole pressure control. The high performance of the controller and the high accuracy of the reduced complexity model are demonstrated via simulations.