It is demonstrated that a method for multiscale history-matching can be used to improve efficiency and/or quality of the solution when achieving a fine-scale match as compared to history-matching directly on the fine scale. Starting from a given fine-scale realization, coarser models are generated using a global upscaling technique where the coarse models are "history-matched" with respect to the solution at the fine scale. Conditioning to dynamic data is done by history-matching a coarse model, and this model is then successively refined using a combination of downscaling and history matching until a model matching dynamic data is obtained at the finest scale. The advantage of this procedure is that the large-scale corrections are obtained using fast models, which combined with proper downscaling procedures provide a better initial model for the final adjustment on the fine scale. The coarse-scale history-matching also provides a regularization of the fine-scale match making the process less dependent on a correct prior model. With the proposed methodology, a series of models of different degree of complexity, all being consistent with both static and dynamic data, may be generated without additional cost. Effects of using á priori information and different initial downscaling techniques, such as sampling or block kriging with sequential Gaussian simulation are investigated using 2 synthetic reservoir models.