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

Cited 3 time in Web of Science Cited 4 time in Scopus
Authors
Kang, Byeongcheol; Jung, Hyungsik; Jeong, Hoonyoung; Choe, Jonggeun
Issue Date
2019-09-06
Publisher
Springer Open
Citation
Petroleum Science, pp. 1-14
Keywords
Channel reservoir characterizationModel selection schemeEgg modelPrincipal component analysisPCAEnsemble Kalman filterEnKFHistory matching
Abstract
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.
ISSN
1672-5107
Language
English
URI
http://hdl.handle.net/10371/162640
DOI
https://doi.org/10.1007/s12182-019-00362-8
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Energy Systems Engineering (에너지시스템공학부)Journal Papers (저널논문_에너지시스템공학부)
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