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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2019-09-01
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| Series: | Petroleum Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s12182-019-00362-8 |
| _version_ | 1818909921663516672 |
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| author | Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe |
| author_facet | Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe |
| author_sort | Byeongcheol Kang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T22:34:36Z |
| format | Article |
| id | doaj.art-b43a10d2f73449c4b1748a919b1b4fab |
| institution | Directory Open Access Journal |
| issn | 1672-5107 1995-8226 |
| language | English |
| last_indexed | 2024-12-19T22:34:36Z |
| publishDate | 2019-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum Science |
| spelling | doaj.art-b43a10d2f73449c4b1748a919b1b4fab2022-12-21T20:03:16ZengKeAi Communications Co., Ltd.Petroleum Science1672-51071995-82262019-09-0117118219510.1007/s12182-019-00362-8Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysisByeongcheol Kang0Hyungsik Jung1Hoonyoung Jeong2Jonggeun Choe3Department of Energy Systems Engineering, Seoul National UniversityDepartment of Energy Systems Engineering, Seoul National UniversityDepartment of Energy Systems Engineering, Seoul National UniversityDepartment of Energy Systems Engineering, Seoul National UniversityAbstracts 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.http://link.springer.com/article/10.1007/s12182-019-00362-8Channel reservoir characterizationModel selection schemeEgg modelPrincipal component analysis (PCA)Ensemble Kalman filter (EnKF)History matching |
| spellingShingle | Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis Petroleum Science Channel reservoir characterization Model selection scheme Egg model Principal component analysis (PCA) Ensemble Kalman filter (EnKF) History matching |
| title | 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_short | 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 |
| topic | Channel reservoir characterization Model selection scheme Egg model Principal component analysis (PCA) Ensemble Kalman filter (EnKF) History matching |
| url | http://link.springer.com/article/10.1007/s12182-019-00362-8 |
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