A Multilevel Coordinate Search Algorithm for Well Placement, Control and Joint Optimization
This addresses optimization challenges in oil field development, offering incremental improvements for domain-specific applications.
The authors tackled the computationally expensive and nonconvex optimization of well placements and controls in oil field development by applying the multilevel coordinate search (MCS) algorithm, showing it is strongly competitive and outperforms other methods for joint optimization with limited computational budgets.
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.