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 search and local search. Both synthetic and real oil fields are considered, and the performance of MCS is compared to the generalized pattern search (GPS), the particle swarm optimization (PSO), and the 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 are compared for the test examples. For the joint optimization problem we compare the performance of the simultaneous and sequential procedures.
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