  DeePore is a deep learning workflow for rapid estimation of a wide range of
porous material properties based on the binarized micro-tomography images. By
combining naturally occurring porous textures we generated 17700 semi-real 3-D
micro-structures of porous geo-materials with size of 256^3 voxels and 30
physical properties of each sample are calculated using physical simulations on
the corresponding pore network models. Next, a designed feed-forward
convolutional neural network (CNN) is trained based on the dataset to estimate
several morphological, hydraulic, electrical, and mechanical characteristics of
the porous material in a fraction of a second. In order to fine-tune the CNN
design, we tested 9 different training scenarios and selected the one with the
highest average coefficient of determination (R^2) equal to 0.885 for 1418
testing samples. Additionally, 3 independent synthetic images as well as 3
realistic tomography images have been tested using the proposed method and
results are compared with pore network modelling and experimental data,
respectively. Tested absolute permeabilities had around 13 % relative error
compared to the experimental data which is noticeable considering the accuracy
of the direct numerical simulation methods such as Lattice Boltzmann and Finite
Volume. The workflow is compatible with any physical size of the images due to
its dimensionless approach and can be used to characterize large-scale 3-D
images by averaging the model outputs for a sliding window that scans the whole
geometry.
