Performance of the ensemble Kalman filter outside of existing wells for a channelized reservoir
article
The ensemble Kalman filter (EnKF) appears to give good results for matching production data at existing wells. However, the predictive power of these models outside of the existing wells is much more uncertain. In this paper, for a channelized reservoir for five different cases with different levels of information the production history is matched using the EnKF. The predictive power of the resulting model is tested for the existing wells and for new wells. The results show a consistent improvement for the predictions at the existing wells after assimilation of the production data, but not for prediction of production at new well locations. The latter depended on the settings of the problem and prior information used. The results also showed that the fit during the history match was not always a good predictor for predictive capabilities of the history match model. This suggests that some form of validation outside of observed wells is essential. © 2010 Springer Science+Business Media B.V.
Topics
TNO Identifier
425493
ISSN
14200597
Source
Computational Geosciences, pp. 1-14.
Pages
1-14
Files
To receive the publication files, please send an e-mail request to TNO Repository.