A comparative evaluation of global search algorithms in black box optimization of oil production: A case study on Brugge field

The selected algorithms have been applied to optimization of oil production in Brugge field. This problem is a bounded NPV optimization with 640 decision variables consist of injection and production rates over 10 years of operation and 200 linear inequality constraints. The results here show that algorithms that use approximated gradients (SPSA and En-Opt) and take advantage of physical properties of the underlying problem (GPS) are superior. Algorithms with self adaptation ability such as SADE and CMAES are the second best performers on this application. In fact, SADE which is the self adapted version of DE could achieve 7.5% more NPV than ordinary DE algorithm. Finally, in this study, GPS has been overall the most efficient algorithm with lowest number of function evaluations and the second highest NPV compared to other algorithms.