A Multilevel Coordinate Search Algorithm for Well Placement, Control and Joint Optimization

TL;DR

This paper introduces a multilevel coordinate search (MCS) algorithm for optimizing well placement and control in oil fields, demonstrating its efficiency and robustness over other methods through tests on synthetic and real data.

Contribution

The paper presents a derivative-free multilevel coordinate search algorithm tailored for joint well placement and control optimization, outperforming existing methods in computational efficiency.

Findings

MCS outperforms GPS, PSO, and CMA-ES in joint optimization tasks.

MCS is effective with limited computational budgets.

Sequential procedures enhance joint optimization performance.

Abstract

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.