Iterative geostatistical history matching uses stochastic sequential simulation to generate and perturb subsurface Earth models to match historical production data. The areas of influence around each well are one of the key factors in assimilating model perturbation at each iteration. The resulting petrophysical model properties are conditioned to well data with respect to large-scale geological parameters such as spatial continuity patterns and their probability distribution functions. The objective of this work is twofold: (i) to identify geological and fluid flow consistent areas of influence for geostatistical assimilation; and (ii) to infer large-scale geological uncertainty along with the uncertainty in the reservoir engineering parameters through history matching. The proposed method is applied to the semi-synthetic Watt field. The results show better match of the historical production data using the proposed regionalization approach when compared against a standard geometric regionalization approach. Tuning large-scale geological and engineering parameters, as represented by variogram ranges, property distributions and fault transmissibilities, improves the production match and provides an assessment over the uncertainty and impact of each parameter in the production of the field.
|Published - 11 Oct 2018