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- W1990688623 abstract "Distance-based stochastic modeling techniques have recently emerged in the context of ensemble-level reservoir modeling, in particular for history matching, model selection and uncertainty quantification. Starting with an initial ensemble of model realizations, a distance between any two realizations is defined. This distance is introduced to incorporate specific modeling purposes into geological modeling, thereby potentially enhancing the efficiency of some modeling techniques such as history matching. If one wants to create new models that are constrained to dynamic data (i.e., history matching), the calculation of distances requires flow simulation for each model in the initial ensemble. This can be very time consuming, especially for high-resolution reservoir models. In this paper, we present a multi-scale framework for ensemble-level reservoir modeling. We employ a distance-based procedure, with emphasis on a rapid construction of multiple models that have improved dynamic data conditioning. We propose to construct new fine-scale models constrained to dynamic data, while performing flow simulations on the associated, coarse-scale models with appropriate upscaling. The availability of the multiple, high-resolution models is crucial for proper uncertainty quantification, compared to retaining only a few models that match perfectly the data but do not necessarily capture the uncertainty in some desired predictions. An error modeling procedure is also introduced in the distance calculations to account for potential errors in upscaling. Based on a few fine-scale flow simulations, the upscaling error is estimated for each model using a clustering technique. We demonstrate the efficacy of the method on an example with significant upscaling errors. Results show that the error modeling procedure can reproduce the fine-scale flow behavior from coarse-scale simulations with sufficient accuracy (in terms of uncertainty predictions). As a consequence, an ensemble of high-resolution models, which are constrained to dynamic data, can be obtained, but with a minimum of flow simulations at the fine scale." @default.
- W1990688623 created "2016-06-24" @default.
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- W1990688623 date "2010-09-06" @default.
- W1990688623 modified "2023-09-23" @default.
- W1990688623 title "Rapid Construction of Ensembles of High-resolution Reservoir Models Constrained to Production Data" @default.
- W1990688623 doi "https://doi.org/10.3997/2214-4609.20144989" @default.
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