Production forecasts are essential for sound reservoir management. The foundation for such forecasts is a characterization of relevant reservoir properties. These properties are usually determined through history matching, using production data, static well data (hard data), and the up-scaled geological model (prior model), simultaneously. This process if often very complex and costly, both in terms of CPU-time and man-hours.

To reduce cost and complexity of reservoir characterization, we propose an alternative methodology; scale splitting. This approach utilizes the fact that production data often contain information about variability of reservoir properties on a much coarser length scale than the other data do. Hence, production data alone can be used to determine the large-scale variation in the estimated properties. The selection of parameters that can be estimated from the production data are determined through a data driven, top-down search, starting with a single parameter representing the average property for the reservoir structure in question. One of the primary objectives of this search is to keep the number of parameters as low as possible, without sacrificing the match quality.

The prior model and the hard data need to be integrated to allow also for finer-scale property variation, and to match hard data at well locations. With scale splitting this is done at low cost after the history match of production data.

We present the scale splitting approach for absolute permeability estimation and give examples where it is compared to other approaches in terms of match quality and work load.

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