Enhanced gas-lift system operation using LPV nonlinear model predictive control

Abstract

In this work, we propose a novel Linear Parameter Varying (LPV) Model Predictive Control (MPC) scheme for oil production maximisation in wells operated with gas-lift systems. The control of a gas-lift system poses several engineering challenges, including the nonlinear nature of its behaviour and different kinds of process constraints. Recent studies indicate that MPC strategies stand out as promising solutions to address many of these issues – with results already published for gas-lift applications. However, when nonlinear models are used, the corresponding nonlinear MPC (NMPC) algorithms entail substantial computational costs, potentially complicating the feasibility of (real-time) embedded implementation. Furthermore, certain complexities in standard gas-lift system representations hinder the use of typical NMPC solvers (e.g. CasADi), thus making approximation-based schemes unavoidable. Accordingly, we propose an LPV formulation to describe the gas-lift system that ensures closed-loop stability and recursive feasibility when implemented as a model for MPC. In this regard, we synthesise an LPV MPC scheme and compare it to an NMPC through CasADi. Several realistic nonlinear numerical simulations are presented and our results indicate that the proposed LPV MPC scheme, in addition to being three times faster (in average), achieved an increase in the total amount of produced oil when compared to an NMPC via CasADi. Then, aiming to maximise oil production rate, we introduce a modified version of the LPV MPC approach, resulting in an increase in the total amount of produced oil of approximately 3% in the studied scenario, when compared to the unmodified LPV MPC scheme.