首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization
Authors:Satomi Suzuki  Guillaume Caumon  Jef Caers
Institution:(1) Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA;(2) School of Geology, CRPG-CNRS, Nancy Université, 54501, Vandoeuvre, Les Nancy, France;(3) 3720 West Alabama, #5313, Houston, TX 77027, USA
Abstract:This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.
Keywords:History matching  Data assimilation  Structural uncertainty  Discrete-space optimization  Distance-based model parameterization  Distance function  Stochastic search
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号