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Deep learning–driven permeability estimation from 2D images

  • Mauricio Araya-PoloEmail author
  • Faruk O. Alpak
  • Sander Hunter
  • Ronny Hofmann
  • Nishank Saxena
Original Paper

Abstract

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 transmitted light microscopy delivers a much higher resolution (approximately 0.6 microns). However, reliable and efficient workflows to jointly utilize full-size 2D images, measured 3D core-plug permeabilities, and 2D direct pore-scale ow simulations on 2D images within a predictive framework for permeability estimation are lacking. In order to close this gap, we have developed a state-of-the-art deep learning (DL) algorithm for the direct prediction of permeability from 2D images. We take advantage of the computing graphics processing units (GPUs) in our implementation of this algorithm. The trained DL model predicts properties accurately within seconds, and therefore, provide a significant speeding up simulation workflow. A real-life dataset is used to demonstrate the applicability and versatility of the proposed method.

Keywords

Deep learning Machine learning Permeability Thin-section Digital rock Sandstone Reservoir properties 

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Notes

Acknowledgments

We thank Shell International Exploration and Production Inc. for allowing the publication of this material.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mauricio Araya-Polo
    • 1
    Email author
  • Faruk O. Alpak
    • 1
  • Sander Hunter
    • 1
  • Ronny Hofmann
    • 1
  • Nishank Saxena
    • 1
  1. 1.Shell International Exploration & Production Inc.HoustonUSA

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