Investigation on Principal Component Analysis Parameterizations for History Matching Channelized Facies Models with Ensemble-Based Data Assimilation

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• 作者：Alexandre A. Emerick
• 关键词：Data assimilation ; Reservoir history matching ; Non ; Gaussian priors ; Facies ; Ensemble smoother with multiple data assimilation
• 刊名：Mathematical Geosciences
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：49
• 期：1
• 页码：85-120
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth Sciences, general; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology;
• 出版者：Springer Berlin Heidelberg
• ISSN：1874-8953
• 卷排序：49

文摘

Preserving plausible geological features when updating facies models is still one of the main challenges with ensemble-based history matching. This is particularly evident for fields with complex geological description (e.g., fluvial channels). There is an impressive amount of research published in the last few years about this subject. However, it appears that there is no definitive solution and both, academia and industry, are looking for practical and robust methods. Among the parameterizations traditionally investigated for history matching, the principal component analysis (PCA) of the prior covariance matrix is an efficient alternative to represent models described by two-point statistics. However, there are some recent developments extending PCA-based parameterizations for models described by multiple-point statistics. The first part of this paper presents an investigation on PCA-based schemes for parameterizing channelized facies models for history matching with ensemble-based methods. The following parameterizations are tested: standard PCA, two alternative implementations of kernel PCA and optimization-based PCA. In the second part of the paper, the optimization-based PCA is modified to allow the use of covariance localization and adapted for simultaneously adjusting the facies type and the permeability values within each facies when history matching production data with an ensemble-based method.