Global optimization of multiphase flow networks using spline surrogate models

•A framework for modelling and optimization of multiphase flow networks is presented.•A trade-off between optimization time and accuracy is sought.•A brief introduction to function approximation using B-splines is given.•The spline-compatible global optimization solver CENSO is described.•Three realistic production optimization cases from BP are solved.•The paper ends by discussing the encouraging results.

A general modelling framework for optimization of multiphase flow networks with discrete decision variables is presented. The framework is expressed with the graph and special attention is given to the convexity properties of the mathematical programming formulation that follows. Nonlinear pressure and temperature relations are modelled using multivariate splines, resulting in a mixed-integer nonlinear programming (MINLP) formulation with spline constraints. A global solution method is devised by combining the framework with a spline-compatible MINLP solver, recently presented in the literature. The solver is able to globally solve the nonconvex optimization problems. The new solution method is benchmarked with several local optimization methods on a set of three realistic subsea production optimization cases provided by the oil company BP.