人工蜂群ABC:优化井位的潜力综述

E. Okoro, O. Agwu, D. I. Olatunji, O. Orodu
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引用次数: 2

摘要

为了最大限度地提高石油采收率,目前存在许多优化工具。传统的工具如模拟退火、响应面技术、基于梯度的优化、混合整数规划等比比皆是。然而,人工智能优化工具已经出现了多年,并正在取得进展。人工蜂群(Artificial bee colony, ABC)自上世纪90年代初提出以来,已成为人工智能领域最常用的优化方法之一。因此,在井位优化方面有大量的研究成果。因此,本文重点介绍了传统的井位优化工具,并对基于人工智能的优化工具,特别是ABC算法和ABC算法的混合算法进行了综述,并使用四个基本标准对它们进行了比较。回顾表明,ABC算法在井规划过程中处理储层井的布置是非常有效的。因此,这项工作在井位优化领域开辟了一个新的前景,因此推荐给任何寻找井位优化讨论枢纽的人。
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Artificial Bee Colony ABC a Potential for Optimizing Well Placement – A Review
Many optimization tools exist for well placement into reservoirs for maximum oil recovery. Conventional tools such as simulated annealing, response surface technology, gradient-based optimization, mixed integer programming etc. abound. However, artificial intelligence optimization tools have emerged over the years and are gaining ground. Artificial bee colony (ABC) has become one of the most common optimization methods in the domain of Artificial Intelligence since it was first conceived in the early nineties. As a result, avalanches of researches to its credit in well placement optimization exist. This paper therefore, highlighted conventional well placement optimization tools and also reviewed the artificial intelligence based optimization tools especially ABC and hybrids of ABC Algorithms formulated for well placement and compared them with each other using four basic criteria. The review has shown that ABC algorithms are very efficient in handling the placement of wells in reservoirs during well planning. This work therefore opens up a new vista in the area of well placement optimization and is therefore recommended to anyone looking for a pivot on the well placement optimization discussion.
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