State estimation and slug flow control of subsea production systems

Abstract

The simulation and control of the severe slugging flow in the subsea multiphase pipeline is the focus of research in the production and exploitation of oil companies. Severe slug flow results in severe fluctuations of pressure and flow rate at both the wells end and the receiving host processing facilities, causing safety and shutdown risks. To prevent the severe slugging flow regime in multiphase transport pipelines, an ODE model is established by using the mass conservation law for individual phases in the pipeline and the riser sections. Then, the proposed model is compared to the results from the OLGA simulation. A comparative study of different slugging flow control solutions is conducted. Extended Kalman Filter (EKF), Back Propagation Neural Network (BPNN) and EKF&BPNN are used for state estimation and combined with PI controller. The EKF and BPNN are good nonlinear filters. However, when the nominal choke opening is increased, they work unsatisfying. The EKF&BPNN observer shows slightly better results than EKF and BPNN when the system has high input disturbance.