Joint optimization of well placement and control for nonconventional well types

In this paper we combine a stochastic global algorithm (particle swarm optimization) and a local search (mesh adaptive direct search) to compare several simultaneous and sequential approaches to the joint placement and control problem. In particular, we study how increasing the complexity of well models (requiring more variables to describe the well׳s location and path) affects the respective performances of the two approaches. The results of several experiments with synthetic reservoir models suggest that the sequential approaches are better able to deal with increasingly complex well parameterizations than the simultaneous approaches.