  Optimal well placement and optimal well control are two important areas of
study in oilfield development. Although the two problems differ in several
respects, both are important considerations in optimizing total oilfield
production, and so recent work in the field has considered the problem of
addressing both problems jointly. Two general approaches to addressing the
joint problem are a simultaneous approach, where all parameters are optimized
at the same time, or a sequential approach, where a distinction between
placement and control parameters is maintained by separating the optimization
problem into two (or more) stages, some of which consider only a subset of the
total number of variables. This latter approach divides the problem into
smaller ones which are easier to solve, but may not explore search space as
fully as a simultaneous approach.
  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.
