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Deep Learning-Driven Pore-Scale Simulation For Permeability EstimationNormal access

Authors: M. Araya-Polo, F.O. Alpak, S. Hunter, R. Hofmann and N. Saxena
Event name: ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery
Session: ECMOR XVI Poster Session 2
Publication date: 03 September 2018
DOI: 10.3997/2214-4609.201802173
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 2.89Mb )
Price: € 20

Summary:
Current micro-CT image resolution is limited to ~1-2 microns. A recent study has identified that at least 10 image voxels are needed to resolve pore throats, which limits the applicability of direct simulations using the Digital Rock (DR) technology to medium-to-coarse grained rocks (i.e., rocks with permeability > 100 mD). On the other hand, 2D high-resolution colored images such as the ones obtained from Scanning Electron Microscopy (SEM) deliver a much higher resolution (~0.5 microns). However, reliable and efficient workflows to jointly utilize full-size SEM images, measured 3D core-plug permeabilities, and 2D direct pore-scale flow simulations on SEM images within a predictive framework for permeability estimation are lacking. In order to close this gap, we introduce a Deep Learning (DL) algorithm for the direct prediction of permeability from SEM images. The trained DL model predicts properties accurately within seconds, and therefore, provide a significant speeding up simulation workflows. Preliminary results will be presented and discussed.


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