Abstract
Tight oil and gas reservoirs have attracted many attentions and are one
of the hottest research fields in recent years. Tight sandstones have
complex pore structures and narrow pores and throats with pore sizes
varying from nanometers to micrometers, studying flow mechanisms in
tight sandstones is of significance to tight oil/gas reservoir
development. Reconstructing the digital rock which can comprehensively
represent petrophysical properties of tight sandstone is the key to
simulate the fluid flow in micro/nano pores. This paper proposes a new
method of reconstructing 3D digital rock from CT image of tight
sandstones based on a deep convolutional generative adversative network
(DCGAN), and 3D convolution in the generator and discriminator are
adopted to realize reconstruction from one dimensional data to 3D
digital rock model. Studies show that when the training effect is
slightly poor, the generated digital rock model will have noise, which
can be reduced by post-processing; when the training effect is well,
DCGAN can accurately reconstruct the 3D digital rock of tight
sandstones, the reconstructed digital rock is very consistent in pore
size, geometric structure, and connectivity of natural tight sandstones.
When multiple 3D tight sandstone CT images are used for training, the
DCGAN can learn the pore structure characteristics of entire tight
sandstones, and the porosity distribution obtained from generated
digital rock are similar to original tight sandstones.