Short-term oil production global optimization with operational constraints: A comparative study of nonlinear and piecewise linear formulations - Carpio, Roymel R.; dAvila, Thiago C.; Taira, Daniel P.; Ribeiro, Leonardo D.; Viera, Bruno F.; Teixeira, Alex F.; Campos, Mario M.; Secchi, Argimiro R. (Journal of petroleum science & engineering, 2021)
The short-term oil production optimization of offshore platforms which use continuous gas lift and consider several operational constraints is a challenging task, specially when decision variables include both continuous and integer ones. The intrinsic non-linearity of the Gas Lift Performance Curves (GLPC) naturally yields to Mixer-Integer Nonlinear Program formulation. Picewise linear approximations of the GLPC are usually applied in order to reduce the complexity of the optimization problem and to ensure an approximate solution, leading to Mixer-Integer Linear Program formulation. However, a comprehensive and comparative study of these two formulations for the oil production optimization problem is scarce in the literature. This paper provides a deep performance evaluation of these two approaches, showing the limitations of both strategies, and proposes a hybrid two-stage optimization strategy to achieve the global optimal operating condition which maximizes the oil production subject to safety, environmental and processing capacity constraints. •The solution of the MINLP formulation is highly dependent on the initial guess.•The MILP formulation always achieves a solution near to the global optimum.•The solution of the MILP formulation may be unfeasible regarding operational constraints.•The computational cost of the MILP formulation could be up to 30 times higher than the MINLP formulation.•The hybrid MILP-MINLP optimization strategy showed the best cost-benefit ratio considering time-consumption and accuracy.