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

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...

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Vydáno v:	Mathematical geosciences Ročník 49; číslo 1; s. 85 - 120
Hlavní autor:	Emerick, Alexandre A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2017 Springer Nature B.V
Témata:	Analysis Channels Chemistry and Earth Sciences Computer based modeling Computer Science Data assimilation Data collection Earth and Environmental Science Earth Sciences Geology Geostatistics Geotechnical Engineering & Applied Earth Sciences Hydrogeology Matching Mathematical models Model matching Parametrization Physics Principal components analysis Statistics Statistics for Engineering Ensemble smoother with multiple data assimilation Non-Gaussian priors Reservoir history matching Facies Data assimilation
ISSN:	1874-8961, 1874-8953
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Abstract	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.
AbstractList	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.
Author	Emerick, Alexandre A.
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Keywords	Ensemble smoother with multiple data assimilation Non-Gaussian priors Reservoir history matching Facies Data assimilation
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Snippet	Preserving plausible geological features when updating facies models is still one of the main challenges with ensemble-based history matching. This is...
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SubjectTerms	Analysis Channels Chemistry and Earth Sciences Computer based modeling Computer Science Data assimilation Data collection Earth and Environmental Science Earth Sciences Geology Geostatistics Geotechnical Engineering & Applied Earth Sciences Hydrogeology Matching Mathematical models Model matching Parametrization Physics Principal components analysis Statistics Statistics for Engineering
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Title	Investigation on Principal Component Analysis Parameterizations for History Matching Channelized Facies Models with Ensemble-Based Data Assimilation
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