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. Unstable mode often decreases sharply the lift gas efficiency for gas-lifted wells, resulting in oilfield production reduction and economic loss and even might damage the production equipment in serious cases.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 to. Then, the proposed model is compared to the results from the OLGA simulation. The new model maintains a good fit for steady-state and dynamics; therefore it is used in our controller and observer design. By doing controllability analysis, the best way to control the severe slugging flow is using the subsea pressure measurement combined with topside flow measurement. However, the subsea pressure might be difficult to measure correctly because the pipeline is placed under tough conditions. One possibility is to combine topside pressure with topside flow measurement and use for estimation of state or other subsea measurements which are generally not available.A comparative study of different slugging flow control solutions is conducted. Kalman filter combined with wavelet neural networks (WNN) and unscented Kalman filter (UKF) is used for state estimation and combined with controllers such as PI, LQR, and MPC. The UKF is a good nonlinear filter when the system has high input disturbance. However, when the nominal choke opening is increased, the UKF combined with the controller fails. The Kalman filter combined with WNN shows slightly better results.&nbsp;

State Estimation and Slug Control of the Subsea Multiphase Pipeline

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

OMAE 2021

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