In a typical reservoir simulation model, varying details of its geological and petrophysical properties need to be captured accurately. There are single-phase regions, where a considerable savings in costs may be realized if large blocks are used, while there are regions, particularly near well-bores, where fine grids are required to adequately capture high and low permeability streaks. The main goal of reservoir characterization is to get a reservoir model that accurately describes and predicts dynamic fluid flow paths and production/injection performances of the wells. This implies a correct description of extreme values of connections of petrophysical properties, mainly permeability and porosity.

The fine scale model can handle most structural and geological complexity with few compromises. Such a model will allow us to quantify uncertainties and also to run risk analysis. This ensures a realistic and consistent model that honors and maintains the latest field information. In case of updating or adjustment to the simulation model, this can be updated in the geological model as well.

Moreover, history matching, by nature, is a very complex inverse problem that can be computationally intensive and practically difficult for very large multimillion cell reservoir models. Therefore, the use of an optimal parameterization and refinement grid is crucial to get fast and valid history matching results.

This paper shows that fine models provide much more accurate results than the up scaled coarse model. A new method will be introduced to enhance history match quality by conditioning a second version of the permeability model to a relation found between errors in pressure and KH from well test data. A comparison of results including core permeability and saturation from both models are presented. The positive impact of this new method on the history match process is discussed in details.

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