Increasingly computer assisted techniques are used for history matching reservoir models. Such methods will become indispensable in view of the increasing amount of information generated by intelligent wells, in which case manual interpretation becomes too time consuming. Also, with the increasing possibilities for controlling a reservoir, a better prediction of the reservoir behavior is very important. A technique that has received considerable attention lately is the Ensemble Kalman Filter (EnKF), which is a sequential updating technique based on the Bayesian notion of updating prior information with observations (measurements). The EnKF has already been proven to be useful for history matching of real fields. Some of the advantages of the EnKF are its sequential nature, the large number of parameters that can be estimated (10000+) and the fact that an uncertainty estimate is generated. The most important and time consuming step for anyone wanting to do a history match with the EnKF is creating the initial ensemble including defining all the proper initial uncertainties. This paper presents an integrated workflow for creating an initial ensemble for a channelized reservoir and updating the ensemble sequentially using the EnKF. The resulting ensemble of history matched models is then used to predict the expected production and associated uncertainty for a new production strategy. The quality of the prediction is compared to the predictions from a model which was history matched manually. The improved predictive capabilities of the models which were history matched with the EnKF allows for better optimization of the production strategy for the complex channel geometry. Moreover, the updated uncertainty estimate shows the risks involved with some of the newly proposed wells, which allows for better decision making.

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