A multilevel coordinate search algorithm for well placement, control and joint optimization

- We apply the multilevel coordinate search for oil field development optimization.
- We investigate the effects of the algorithmic parameters of MCS.
- MCS outperforms GPS, PSO, and CMA-ES in most cases especially for the joint problem.
- The sequential procedure outperforms the simultaneous procedure for the joint problem.

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 requires 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 is 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.