期刊
APPLIED SOFT COMPUTING
卷 34, 期 -, 页码 463-484出版社
ELSEVIER
DOI: 10.1016/j.asoc.2015.05.032
关键词
Adaptive constraint handling; Global search; Particle swarm; Reservoir simulation; Surrogate-based optimization; Waterflooding management
资金
- CNPq (National Research Council, Brazil)
- PETROBRAS
- Foundation CMG
- Convergence European Regional Development Fund through the Welsh Government via the ASTUTE project
This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust particle swarm algorithm coupled with a novel adaptive constraint-handling technique to search for the global optimum of these formulations. However, this is a population-based method, which therefore requires a high number of evaluations of an objective function. Since the performance evaluation of a given management scheme requires a computationally expensive high-fidelity simulation, it is not practicable to use it directly to guide the search. In order to overcome this drawback, a Kriging surrogate model is used, which is trained offline via evaluations of a High-Fidelity simulator on a number of sample points. The optimizer then seeks the optimum of the surrogate model. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据