A mixed-integer convex formulation for production optimization of gaslifted oil fields with routing and pressure constraints

Abstract

Production optimization of gas-lifted oil fields under facility, routing, and pressure constraints has attracted the attention of researchers and practitioners for its scientific challenges and economic impact. The available methods fall into one of two categories: nonlinear or piecewise-linear approaches. The nonlinear methods optimize simulation models directly or use surrogates obtained by curve fitting. The piecewise-linear methods represent the nonlinear functions using a convex combination of sample points, thereby generating a Mixed-Integer Linear Programming (MILP) problem. The nonlinear methods rely on compact models, but can get stuck in local minima, whereas the piecewise-linear methods can reach globally optimal solutions, but their models tend to get very large. This work combines these methods, whereby piecewise-linear models are used to approximate production functions, which are then composed with convex-quadratic models that approximate pressure drops. The end result is a Mixed-Integer Convex Programming (MICP) problem which is more compact than the MILP model and for which globally optimal solutions can be reached.