Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

被引:0
|
作者
Satomi Suzuki
Guillaume Caumon
Jef Caers
机构
[1] Stanford University,Department of Energy Resources Engineering
[2] Nancy Université,School of Geology, CRPG
来源
Computational Geosciences | 2008年 / 12卷
关键词
History matching; Data assimilation; Structural uncertainty; Discrete-space optimization; Distance-based model parameterization; Distance function; Stochastic search;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:105 / 119
页数:14
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