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Currently a multitude of techniques exist for (computer-)assisted history matching (AHM) of simulation models, each with their merits and limitations. In this paper, it is demonstrated how different AHM techniques can be combined to quickly reveal diagnostics of a subsurface model and to obtain a better model in less time, optimally using the strengths of each method. A completed field application of AHM will be presented, in which several AHM techniques are sequentially used to arrive at a history match on pressures and fluid rates and, equally important, an improved understanding of both the static and dynamic model. The water flooded field, located in the Middle East, has decades of historical production data from twenty wells and is notoriously difficult to match. The first technique that has been applied involves Design of Experiments to generate proxies followed by Monte Carlo Markov Chain to find the ensemble of scalar parameters that give an improved match. Subsequently, adjoint-based history matching has been used to find the areas in the model that were under-modeled and needed additional attention of the subsurface team members. Based on the results in this step of the workflow the static model has been improved such that it is consistent with the information in the production measurements. For this field, the AHM workflow has achieved a considerable reduction of history matching time. For general simulation studies this workflow is estimated to result in a time saving of 40% with respect to manual history matching. In addition, it results in an improved match and a better understanding of the static and dynamic subsurface uncertainties.
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Doren et al. (2012) studied this question.