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Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach

Abstract

In reservoir engineering, it is attractive to characterize the difference between reservoir models in metrics that relate to the economic performance of the reservoir as well as to the underlying geological structure. In this paper, we develop a dissimilarity measure that is based on reservoir flow patterns under a particular operational strategy. To this end, a spatial-temporal tensor representation of the reservoir flow patterns is used, while retaining the spatial structure of the flow variables. This allows reduced-order tensor representations of the dominating patterns and simple computation of a flow-induced dissimilarity measure between models. The developed tensor techniques are applied to cluster model realizations in an ensemble, based on similarity of flow characteristics.

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Acknowledgements

We acknowledge the discussions with Dr.Tzu-hao Yeh from the Quantitative Reservoir Management group at Shell for his views on the potential application of the techniques presented in this paper on field cases. The authors acknowledge financial support from the Recovery Factory program sponsored by Shell Global Solutions International.

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Correspondence to Paul M. J. Van den Hof.

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Insuasty, E., Van den Hof, P.M.J., Weiland, S. et al. Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach. Comput Geosci 21, 645–663 (2017). https://doi.org/10.1007/s10596-017-9641-4

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Keywords

  • Reduced-order modeling
  • Tensor decompositions
  • Tensor algebra
  • Flow characterization