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

• Published in: Petroleum science Vol. 17; no. 1; pp. 182 - 195
• Main Authors: Kang, Byeongcheol, Jung, Hyungsik, Jeong, Hoonyoung, Choe, Jonggeun
• Format: Journal Article
• Language: English
• Published: Beijing China University of Petroleum (Beijing) 01.02.2020; KeAi Publishing Communications Ltd; KeAi Communications Co., Ltd
• Subjects: Abstracts; Analysis; Channel reservoir characterization; Cluster analysis; Clustering; Computation; Computer simulation; Earth and Environmental Science; Earth Sciences; Economics and Management; Egg model; Energy Policy; Ensemble Kalman filter (EnKF); History matching; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Kalman filters; Mineral Resources; Model selection scheme; Original Paper; Principal component analysis (PCA); Principal components analysis; Reservoirs; Three dimensional models; Ensemble Kalman filter (EnKF); Channel reservoir characterization; History matching; Egg model; Model selection scheme; Principal component analysis (PCA)
• ISSN: 1672-5107; 1995-8226
• DOI: 10.1007/s12182-019-00362-8

s 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.