Download slice the cake other slices rar series#The geometrical and mathematical models of the multi-scale pores in series are proposed. A dynamic model for the apparent diffusion coefficient is proposed, and it can accurately describe the complete unsteady flow process of gas in a cylindrical coal sample. This apparent diffusion coefficient shows two different multi-scale characteristics in time, one is the smooth dynamic attenuation and the other is the dynamic attenuation in a two-stage step. The experimental results show that, the apparent diffusion coefficient of a cylindrical coal sample attenuates with time. Meantime, the steady method is adopted to conduct the experiment with the same stress loading for comparison. A cylindrical coal sample with a height of 100 mm and diameter of 50 mm is used to conduct the unsteady diffusion-seepage experiment with and without stress loading using methane and helium. However, the current experiment and theory of steady-state permeability cannot reflect the multi-scale characteristics. The pore aperture differential can reach one million orders of magnitude, which causes the multi-scale characteristics in space and time for coal permeability and significantly influences gas drainage. Keywords: Super-resolution, GAN, deep-learning, image processing, PNM, simulationĪbstract: Coal is a porous medium that contains multi-scale pores with a pore aperture from millimeter level to nanometer level. The super-resolved images were more realistic visually and produced better single and multiphase flow simulations results. Promising results when applied on low resolution micro-CT images. Large super-resolved images up to 4000 voxels cube were produced and the technique showed This is followed by presenting flow simulations performed on low resolution and super-resolved images showing how the ESRGAN can considerably improve the accuracy of DRP simulations. Subsequently, we apply it to 3D micro-CT images of several rocks, and we compare the super-resolved images against the high-resolution ones of the same rock volume. We first describe the ESRGAN method and our training strategy. In this talk, we present such a strategy to digitally increase the resolution of 3D micro-CT using a deep learning approach called Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). Recent advances in deep learning methods have led to major advances in computer vision techniques, and notably in the field of super-resolution imaging. Furthermore, during image acquisition a compromise is often made between the speed of the image acquisition, the size of the scanned volume and the resolution obtained: increasing the resolution decreases the field of view, in turn limiting the quantity of information obtained from the image and thus making DRP simulations less representative. However, the geometry of a real rock is not always well characterized, notably due to the lack of image resolution which in turn introduces uncertainty in the pore/throat geometry and consequently introduces errors in rock property computations. Yang et al (2017) have proved that when the geometry of the pore space is well characterized, the flow simulators perform well. In DRP, the first step is to obtain micro-CT images of a rock, this is then followed by segmenting the images to distinguish the rock from the pore space, and finally flow simulations are performed to compute advanced rock properties such as relative permeability and capillary pressure. Mohamed Regaieg1*, Zakaria El Abid 2, Erwann Camberlin 3ĭigital Rock Physics (DRP) provides a new way to compute rock properties and carry-out related sensitivity analysis to complement laboratory measurements.
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