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Training image-based scenario modeling of fractured reservoirs for flow uncertainty quantification

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Abstract

Geological characterization of naturally fractured reservoirs is potentially associated with large uncertainty. However, the geological modeling of discrete fracture networks (DFN) is considerably disconnected from uncertainty modeling based on conventional flow simulators in practice. DFN models provide a geologically consistent way of modeling fractures in reservoirs. However, flow simulation of DFN models is currently infeasible at the field scale. To translate DFN models to dual media descriptions efficiently and rapidly, we propose a geostatistical approach based on patterns. We will use experimental design to capture the uncertainties in the fracture description and generate DFN models. The DFN models are then upscaled to equivalent continuum models. Patterns obtained from the upscaled DFN models are reduced to a manageable set and used as training images for multiple-point statistics (MPS). Once the training images are obtained, they allow for fast realization of dual-porosity descriptions with MPS directly, while circumventing the time-consuming process of DFN modeling and upscaling. We demonstrate our ideas on a realistic Middle East-type fractured reservoir system.

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Authors and Affiliations

  1. Department of Energy Resources Engineering, Stanford University, Stanford, CA, 94305, USA

    Andre Jung & Jef Caers

  2. Streamsim Technologies, San Francisco, CA, 94121, USA

    Darryl H. Fenwick

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  1. Andre Jung
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  2. Darryl H. Fenwick
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  3. Jef Caers
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Correspondence to Andre Jung.

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Jung, A., Fenwick, D.H. & Caers, J. Training image-based scenario modeling of fractured reservoirs for flow uncertainty quantification. Comput Geosci 17, 1015–1031 (2013). https://doi.org/10.1007/s10596-013-9372-0

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