Well Placement and Control Optimization of Horizontal Steamflooding Wells Using Derivative-Free Algorithms

Abstract

Summary The determination of optimal well locations and well controls in the horizontal-well steamflooding of heavy-oil reservoirs is a meaningful but also challenging task for the complex well types and complicated mechanisms. In this paper, a framework that combines optimization algorithms and the reservoir simulator together is proposed to solve this problem. Two typical algorithms, particle-swarm optimization (PSO) and mesh adaptive direct search (MADS), are both used to study their performance on well-placement optimization, well-control optimization, and the joint optimization of these two aspects. For the joint-optimization problem, both the simultaneous approach and sequential approach are considered. A net-present-value (NPV) formula for evaluation of the steamflooding project is proposed, and optimization runs are conducted for an offshore heavy-oil reservoir by maximizing the NPV of the horizontal steamflooding pilot. The results show that both the PSO and MADS were effective for well-placement/control optimization of the horizontal steamflooding wells. The NPVs were greatly improved throughout the optimization process. The control frequency has great influence on the optimal NPV. Intermittent steamflooding might be a better choice than continuous steamflooding. The steam-injection rate and oil-production rates need to be controlled and decreased at the latter stage for mitigating steam channeling and an ineffective steam cycle between injection and production wells. For the joint-optimization problem, the simultaneous procedure finds the best solution for a case with smaller variable numbers and the sequential procedure performs better for a case with larger variable numbers. The PSO algorithm performs better than the MADS algorithm for more complex problems with larger variable numbers in both the simultaneous and sequential procedures. The sequential procedure is worth considering in practice for problems with large scale and a limited computational budget.