The prediction of fluid flows within oil reservoirs or gas storage sites or aquifers requires the characterization of its petro-physical properties, i.e., facies, porosity, permeability, etc. This issue can be addressed through history-matching which calls for the determination of a three-dimensional model representing the studied reservoir. In a nutshell, a model is a grid populated by petrophysical properties. These ones have to be sequentially adjusted until the flow responses simulated for the resulting reservoir model reproduce the available dynamic data: pressures, flow rates, water cuts, 4D-seismic... A difficulty usually disregarded is that these data provide information about petrophysical properties at different scales. Referring to sequential simulation, we propose a method for generating multiscale realizations of both continuous or discrete random fields. These ones are then used to populate reservoir models with the required petrophysical properties. The integration of multiscale simulation within history-matching provides new facilities and makes it possible to incorporate dynamic data at different scales of resolution. When combined with geostatistical parameterization techniques as the gradual deformation method, it gives the essential ability to adjust the reservoir model at various scales. In addition, the overall history-matching process becomes more efficient as targeting the appropriate scale entails an economical parameterization of the model, i.e., the coarser the scale, the smaller the number of unknown parameters. Last, we present a numerical application case to highlight the advantages of the method for conditioning permeability models to dynamic data. For simplicity, we focus on two-scale processes. The coarse scale describes the variations in the mean while the fine scale characterizes local variations around the mean. We investigate the relationships between data resolution and parameterization.


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