Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

Abstracts Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter (EnKF), increasing...

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Autores principales:	Byeongcheol Kang, Hyungsik Jung, Hoonyoung Jeong, Jonggeun Choe
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2019
Materias:	Channel reservoir characterization Model selection scheme Egg model Principal component analysis (PCA) Ensemble Kalman filter (EnKF) History matching Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/b43a10d2f73449c4b1748a919b1b4fab
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spelling	oai:doaj.org-article:b43a10d2f73449c4b1748a919b1b4fab2021-12-02T08:24:15ZCharacterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis10.1007/s12182-019-00362-81672-51071995-8226https://doaj.org/article/b43a10d2f73449c4b1748a919b1b4fab2019-09-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-019-00362-8https://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstracts Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter (EnKF), increasing ensemble size can be one of the solutions, but it causes high computational cost in large-scale reservoir systems. In this paper, we propose a preprocessing of good initial model selection to reduce the ensemble size, and then, EnKF is utilized to predict production performances stochastically. In the model selection scheme, representative models are chosen by using principal component analysis (PCA) and clustering analysis. The dimension of initial models is reduced using PCA, and the reduced models are grouped by clustering. Then, we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data. One representative model with the minimum error is considered as the best model, and we use the ensemble members near the best model in the cluster plane for applying EnKF. We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.Byeongcheol KangHyungsik JungHoonyoung JeongJonggeun ChoeKeAi Communications Co., Ltd.articleChannel reservoir characterizationModel selection schemeEgg modelPrincipal component analysis (PCA)Ensemble Kalman filter (EnKF)History matchingScienceQPetrologyQE420-499ENPetroleum Science, Vol 17, Iss 1, Pp 182-195 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Channel reservoir characterization Model selection scheme Egg model Principal component analysis (PCA) Ensemble Kalman filter (EnKF) History matching Science Q Petrology QE420-499
spellingShingle	Channel reservoir characterization Model selection scheme Egg model Principal component analysis (PCA) Ensemble Kalman filter (EnKF) History matching Science Q Petrology QE420-499 Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
description	Abstracts Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter (EnKF), increasing ensemble size can be one of the solutions, but it causes high computational cost in large-scale reservoir systems. In this paper, we propose a preprocessing of good initial model selection to reduce the ensemble size, and then, EnKF is utilized to predict production performances stochastically. In the model selection scheme, representative models are chosen by using principal component analysis (PCA) and clustering analysis. The dimension of initial models is reduced using PCA, and the reduced models are grouped by clustering. Then, we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data. One representative model with the minimum error is considered as the best model, and we use the ensemble members near the best model in the cluster plane for applying EnKF. We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.
format	article
author	Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe
author_facet	Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe
author_sort	Byeongcheol Kang
title	Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
title_short	Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
title_full	Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
title_fullStr	Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
title_full_unstemmed	Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
title_sort	characterization of three-dimensional channel reservoirs using ensemble kalman filter assisted by principal component analysis
publisher	KeAi Communications Co., Ltd.
publishDate	2019
url	https://doaj.org/article/b43a10d2f73449c4b1748a919b1b4fab
work_keys_str_mv	AT byeongcheolkang characterizationofthreedimensionalchannelreservoirsusingensemblekalmanfilterassistedbyprincipalcomponentanalysis AT hyungsikjung characterizationofthreedimensionalchannelreservoirsusingensemblekalmanfilterassistedbyprincipalcomponentanalysis AT hoonyoungjeong characterizationofthreedimensionalchannelreservoirsusingensemblekalmanfilterassistedbyprincipalcomponentanalysis AT jonggeunchoe characterizationofthreedimensionalchannelreservoirsusingensemblekalmanfilterassistedbyprincipalcomponentanalysis
_version_	1718398561926774784

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

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