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Traditional approaches for storage devices simulation have been based on detailed and analytic models. However, analytic models are difficult to obtain and detailed models require a high computational cost which may be not affordable for large scale simulations (e.g. detailed data center simulations). In current systems like large clusters, grids, or clouds, performance and energy studies are critical, and fast simulations take an important role on them. A different approach is the black-box statistical modeling, where the storage device, its interface, and the interconnection mechanisms are modeled as a single stochastic process, defining the request response time as a random variable with an unknown distribution. A random variate generator can be built and integrated into a bigger simulation model. This approach allows to generate a simulation model for both real and synthetic complex workloads. This article describes a novel methodology that aims to build fast simulation models for storage devices. Our method uses as starting point a workload and produces a random variate generator which can be easily integrated into large scale simulation models. A comparison between our variate generator and the widely known simulation tool DiskSim, shows that our variate generator is faster, and can be as accurate as DiskSim for both performance and energy consumption predictions.