Consistent integration of measured static and dynamic data and a proper description of the model uncertainty is essential for the predictability of a reservoir model. In a traditional base-case approach, the static data conditioning (geo-modelling) is often decoupled from the dynamic data conditioning (history matching) and the model uncertainty is completely ignored – which often lead to poor model predictability. In this paper we have applied an ensemble Kalman based history matching technique to consistently integrate both static and dynamic data, while capturing the modelling uncertainty on two gas condensate fields offshore Norway. We demonstrate the effectiveness and discuss practical aspects of the method when applying it on real field data.


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