Fast Economic Model Predictive Control for a Gas Lifted Well Network

This paper considers the optimal operation of an oil and gas production network by formulating it as an economic nonlinear model predictive control (NMPC) problem. Solving the associated nonlinear program (NLP) can be computationally expensive and time consuming. To avoid a long delay between obtaining updated measurement information and injecting the new inputs in the plant, we apply a sensitivity-based predictor-corrector path-following algorithm in an advanced-step NMPC framework. We demonstrate the proposed method on a gas-lift optimization case study and compare the performance of the path-following economic NMPC to a standard economic NMPC formulation.