Reconstruction of 3D Porous Media From 2D Slices

TL;DR

This paper introduces a deep learning method for reconstructing 3D porous media from 2D slices, enabling micro-level rock analysis when limited samples are available.

Contribution

A novel deep neural network architecture that models the distribution of 3D structures for accurate reconstruction from 2D slices.

Findings

Effective reconstruction demonstrated by Minkowski functional metrics

The method captures the distribution of 3D structures accurately

Numerical experiments validate the approach's robustness

Abstract

In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. We fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices), we recover the three-dimensional structure around such slices as the most probable one according to that constructed distribution. Technically, we implement this in the form of a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method provides a good reconstruction in terms of Minkowski functionals.