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

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 x 4 and x 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 1500(3) 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 4900(3) voxels.

Automated summary

The representativity and accuracy of digital rock physics (DRP) simulations depend on the size and resolution of the image volume. This paper presents the usage of a super-resolution technique called enhanced super-resolution generative adversarial network (ESRGAN) to generate well-resolved micro-CT images with enhancement factors of x 4 and x 8. The results show that the ESRGAN images provide more accurate pore network extraction and multiphase simulation results compared to highly resolved images.