Health-aware control and model-based prognostics of a subsea oil and gas separation system

Abstract

The exploitation of fields with high CO2 content is challenging, due to the low economic value and the environmental impact associated with this component. In this context, a significant use for the generated CO2 is its reinjection into the reservoir, and subsea CO2 separation allows for more efficient processing. Prognostics is a key activity in this process, due to the necessity of minimizing intervention in equipment. The main objective of this work is to investigate the health-aware control of a subsea CO2 separation system. The considered system model, which consists of a differential-algebraic equation system, was adapted from the literature. All simulations were performed using libraries developed by the author, based on methods from the Scipy and Assimulo libraries. A reference steady state was obtained for the design conditions, and continuous-time transfer functions were identified from step response data. The identified models were used in a predictive controller, and closed-loop simulations were performed to evaluate the controller tuning. In the sense of prognostics, a stochastic model of multiphase pump degradation, sensitive to the process operating condition, was proposed, and a particle filter was implemented to perform online degradation state estimation and remaining useful lifetime prediction. At last, a health-aware controller was designed, and some difficulties in combining reference tracking and lifetime extension objectives were investigated. The obtained results indicate that dealing with the health-aware control problem through the multiobjective optimization theory and addressing the useful lifetime extension in an optimization layer may result in more satisfactory results.