  Determining optimal well placements and controls are two important tasks in
oil field development. These problems are computationally expensive, nonconvex,
and contain multiple optima. The practical solution of these problems require
efficient and robust algorithms. In this paper, the multilevel coordinate
search (MCS) algorithm is applied for well placement and control optimization
problems. MCS is a derivative-free algorithm that combines global and local
search. Both synthetic and real oil fields are considered. The performance of
MCS is compared to generalized pattern search (GPS), particle swarm
optimization (PSO), and covariance matrix adaptive evolution strategy (CMA-ES)
algorithms. Results show that the MCS algorithm is strongly competitive, and
outperforms for the joint optimization problem and with a limited computational
budget. The effect of parameter settings for MCS are compared for the test
examples. For the joint optimization problem we compare the performance of the
simultaneous and sequential procedures and show the utility of the latter.
