Model predictive control, an advanced control technology that helps to achieve zero-emission industrial processes
In oil production, treating the produced water is key to ensuring the low amount of discharge (i.e., marine pollution) which are required of oil producers.
Furthermore, ensuring steady flow in the risers is important to maintaining a high production, reducing flaring, and protecting the downstream equipment, i.e., the separation and cleaning facilities which follow the risers.
In this project, we develop digitalization tools for simultaneously optimizing well flow and produced water quality in order to meet discharge goals and increase production. This requires combined modelling of the near-wellbore region, the riser, the three-phase separation of the produced liquid, and de-oiling hydrocyclones (i.e., primary treatment).
Each subsystem can be operated in a decentralized manner. However, fully coordinated and centralized control can offer significant advantages in terms of 1) profits, 2) satisfaction of emissions and discharge limits, and 3) mitigation of instabilities (e.g., slug flow).
We investigate both linear and nonlinear economic model predictive control (MPC) strategies, and we compare centralized and decentralized setups. Furthermore, the MPC strategies involve 1) system identification (e.g., parameter estimation), 2) state estimation, and 3) optimal control. The comparison will be based on both in silico simulations as well as in vivo tests on the pilot plant at AAU Esbjerg.
Main supervisor:
Professor John Bagterp Jørgensen, DTU Compute
Co- supervisor:
Steen Hørsholt, DTU Compute
Associate Professor Zhenyu Yang, Aalborg University Esbjerg