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Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Byeongcheol | - |
dc.contributor.author | Jung, Hyungsik | - |
dc.contributor.author | Jeong, Hoonyoung | - |
dc.contributor.author | Choe, Jonggeun | - |
dc.date.accessioned | 2019-11-07T04:49:18Z | - |
dc.date.available | 2019-11-07T13:50:19Z | - |
dc.date.issued | 2019-09-06 | - |
dc.identifier.citation | Petroleum Science, pp. 1-14 | ko_KR |
dc.identifier.issn | 1672-5107 | - |
dc.identifier.uri | https://hdl.handle.net/10371/162640 | - |
dc.description.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. | ko_KR |
dc.description.sponsorship | The authors thank research projects supported by The Ministry of Trade, Industry, and Energy (20172510102090, 20142520100440, 20162010201980), and Global PhD Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015H1A2A1030756).
Hoonyoung Jeong has been supported by the National Research Foundation of Korea (NRF) Grant (No. 2018R1C1B5045260). The orresponding author is also thankful to The Research Institute of Engineering at Seoul National University, Korea | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | Springer Open | ko_KR |
dc.subject | Channel reservoir characterization | ko_KR |
dc.subject | Model selection scheme | ko_KR |
dc.subject | Egg model | ko_KR |
dc.subject | Principal component analysis | ko_KR |
dc.subject | PCA | ko_KR |
dc.subject | Ensemble Kalman filter | ko_KR |
dc.subject | EnKF | ko_KR |
dc.subject | History matching | ko_KR |
dc.title | Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 강병철 | - |
dc.contributor.AlternativeAuthor | 정형식 | - |
dc.contributor.AlternativeAuthor | 정훈영 | - |
dc.contributor.AlternativeAuthor | 최종근 | - |
dc.identifier.doi | 10.1007/s12182-019-00362-8 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2019-09-08T03:49:42Z | - |
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