Gas Lift Optimization under Uncertainty

Abstract In this paper, we consider the problem of production optimization under uncertainty applied to gas lifted well networks. Worst-case and scenario optimization methods are presented to explicitly handle the uncertainty. We also compare the performance and computation time of the presented methods with nominal and ideal cases using Monte Carlo simulations. We show that the scenario optimization method is able to reduce the conservativeness, however at the cost of computation time. We also show that the performance can be improved by parameter adaptation using an extended Kalman filter for combined state and parameter estimation.