Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (9): 196-199.DOI: 10.3778/j.issn.1002-8331.2009.09.057

• 工程与应用 • Previous Articles     Next Articles

Application of hybrid method in parameters optimization of well test

XU Jie,LU De-tang,HAN Wei   

  1. The Institute of Engineering and Science Software of USTC,Hefei 230027,China
  • Received:2008-09-09 Revised:2008-11-21 Online:2009-03-21 Published:2009-03-21
  • Contact: XU Jie


徐 杰,卢德唐,韩 伟   

  1. 中国科学技术大学 工程科学软件研究所,合肥 230027
  • 通讯作者: 徐 杰

Abstract: Parameters optimization of well test optimizes the reservoir parameters that are inversed by the information,which is based on the varieties of measured bottom pressure or flow with the change of time.The complex equations and boundary conditions make the problem of well test parameters optimization be high nonlinearity and existence of many local extremums in modern well test.This paper presents a kind of hybrid method which is based on L-M algorithm and differential evolution algorithm.Hybrid method uses differential evolution algorithm to get characteristics of population clustering after certain evolutionary generations and identifies the populations as different clustering regions.And then by making the center of each clustering as the starting point,it re-uses L-M algorithm that is based on the gradient of local search with strong ability of local search to quickly find the least value in this clustering region.Hybrid method combines the advantages of both differential evolution algorithm with strong global search ability and L-M algorithm with strong local search ability as well as high speed of convergence.The paper applies this hybrid method into well test parameters optimization and demonstrates that it has higher optimizing speed and exacter convergence precision than other any single algorithm through providing the results of two different kinds of oil reservoir model.Besides,the hybrid method has wide practicability and can efficiently solve the complex issue about the existence of many local extremums.

Key words: L-M algorithm, differential evolution algorithm, density clustering, hybrid method, parameters optimization of well test

摘要: 试井参数优化就是对利用测得的油气井底压力或流量随时间变化的资料所反演出的油藏参数进行优化处理。现代试井中遇到的复杂方程和定解条件使得试井参数优化问题高度非线性,存在多局部极值。所提出的基于L-M和差分进化的混合方法是利用差分进化算法在一定进化代数后出现的种群聚类特性,将种群识别为不同的聚类区域,然后以每个聚类的中心为起始点,再利用基于梯度具有局部搜索能力强的L-M算法快速找到该聚类区域的最小极值。混合方法兼顾了差分进化全局搜索能力强和L-M局部搜索能力强收敛速度快的优点。将该混合方法应用于试井参数优化中,并通过两种不同油藏模型的实例结果表明该混合方法比单一的算法优化速度更快,收敛精度更高。此外该混合方法实用性广,能有效地解决存在多局部极值的试井参数优化复杂问题。

关键词: L-M算法, 差分进化算法, 密度聚类, 混合方法, 试井参数反演