An efficient algorithm for using stochastic conditional simulation in which we directly incorporate dynamic information is presented. Modeling the spatial description on a fine-scale and the flow behavior on an upscaled grid reduces the flow-simulation execution time and allows for a faster conditional-simulation algorithm. Upscaling approaches are described that result in flow-performance matching between the fine or ＇＇true＇＇ scale and the upscaled grid. The simulated-annealing method is used with a two-part objective function consisting of a variogram constraint and a flow-simulation constraint with each part being appropriately weighted. Improvements in the reservoir description when the dynamic constraint is included are demonstrated. The procedure simultaneously generates a fine-scale description that can be used for future evaluation and a coarse-scale description that honors the production data. Using this procedure, up to a 10,000-gridblock description has been simulated.