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Towards Large-Scale DRP Simulations: Generation of Large Super-Resolution images and Extraction of Large Pore Network Models

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Abstract

Representativity and accuracy of digital rock physics (DRP) simulations depend strongly on the size of the image volume and the resolution obtained. Even with one of the fastest DRP simulation techniques like pore network modelling, simulation volumes have typically been limited to few cubic millimetres for highly resolved images. In this paper, a super-resolution technique named enhanced super-resolution generative adversarial network (ESRGAN) is used to obtain well-resolved images with large field of view and to generate micro-CT images with resolution enhancement factors of × 4 and × 8. Subsets of resulting ESRGAN images were tested against the same volume (of acquisitions resolved at high and low resolution) by comparing petrophysical properties of interest. Pore network extraction and multiphase simulation results showed that pore size distribution, porosity, permeability, drainage capillary pressure and relative permeability curves obtained using ESRGAN images were more accurate. Large images, however, pose subsequent limitations on DRP simulations as pore network extraction code needs a lot of memory to process them (usually more than 60 GB of RAM for 15003 voxels image). Thus, we present a novel stitching strategy that is developed to enable the extraction of pore networks on such large images. Several validation cases of this method are presented to test the accuracy of the results from stitched networks on single- and multiphase flow properties. Finally, our stitching tool was used to generate two large networks of 3.6 million and 9.2 million elements, respectively, from two large ESRGAN images of approximately 49003 voxels.

Article Highlights

  • Application of ESRGAN on micro-CT images with resolution improvement factors × 4 and × 8.

  • Testing the image quality enhancement by performing single- and multiphase flow simulations.

  • Generation of large super-resolved images with size up to 4688 × 5160 × 4800 voxels.

  • Development of a stitching methodology to extract pore networks from these large images.

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Data Availability

The super-resolution images could be provided on request from the authors.

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Acknowledgements

The authors would like to thank TotalEnergies management for the authorization to publish this work and the reviewers for their comments that contributed to improve the quality from the paper. ICE imaging platform is acknowledged for acquiring the images used in this work.

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Authors and Affiliations

  1. TotalEnergies One Tech, Pau, France

    Mohamed Regaieg & Titly Farhana Faisal

  2. Computational Hydrocarbon Laboratory for Optimized Energy Efficiency, Pau Cedex, France

    Clément Varloteaux

  3. Sorbonne University, Paris, France

    Zakaria ElAbid

Authors
  1. Mohamed Regaieg
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  2. Clément Varloteaux
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  3. Titly Farhana Faisal
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  4. Zakaria ElAbid
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Contributions

MR and ZE have developed the ESRGAN code. TFF has validated the method. CV and MR have developed the stitching methodology. MR has written the first draft of the manuscript.

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Correspondence to Mohamed Regaieg.

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Regaieg, M., Varloteaux, C., Farhana Faisal, T. et al. Towards Large-Scale DRP Simulations: Generation of Large Super-Resolution images and Extraction of Large Pore Network Models. Transp Porous Med 147, 375–399 (2023). https://doi.org/10.1007/s11242-023-01913-9

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