Scale-corrected ensemble Kalman filtering applied to prod uction-history conditioning in reservoir evaluation

In reservoir evaluation problems, the reservoir properties are largely unknown. To infer these properties from observations of the reservoir production is referred to as history matching or production history conditioning. Traditionally, this is done by repeated fluid-flow simulations, where all the available production data are used simultaneously to arrive at a set of history-matched reservoir models. In recent years, the amount of data continuously collected from a reservoir under production has been on the increase. Hence, the need for automatic, continuous model updating is apparent. The ensemble Kalman filter has been shown to be suitable for this purpose. However, large reservoir evaluation problems require upscaling reservoir properties to perform the necessary number of fluid-flow simulations. Traditional ensemble Kalman filtering is shown to give bias in the production history conditioned reservoir representations. The loss in accuracy and precision introduced by performing fluid-flow simulations on a coarser scale should be accounted for, but this is rarely or never done. We introduce the scale-corrected ensemble Kalman filter approach in order to quantify the loss in accuracy and precision. A reference scale is defined and all uncertainty quantifications are made relative to this scale, although the fluid flow simulations are made on a coarser scale. The production history conditioned reservoir representation will be accurate with realistic precision measures on this reference scale. The methodology is demonstrated on a large case study inspired by the characteristics of the Troll field in the North Sea.