Steady-state real-time optimization using transient measurements on an experimental rig

Abstract

Real-time optimization with persistent parameter adaptation (ropa) is an rto approach, where the steady-state model parameters are updated dynamically using transient measurements. Consequently, we avoid waiting for a steady-state before triggering the optimization cycle, and the steady-state economic optimization can be scheduled at any desired rate. The steady-state wait has been recognized as a fundamental limitation of the traditional rto approach. In this paper, we implement ropa on an experimental rig that emulates a subsea oil well network. For comparison, we also implement traditional and dynamic rto. The experimental results confirm the in-silico findings that ropa’s performance is similar to dynamic rto’s performance with a much lower computational cost. Additionally, we present some guidelines for ropa’s practical implementation and a theoretical analysis of ropa’s convergence properties.