Abstract
The modeling and characterization of porous media is quite significant to explore and develop oil and natural gas resources. Traditional numerical simulation methods obtain reconstruction results through the statistical probability in training images (TIs), while the reconstruction process is lengthy and the probability information cannot be reused. The development of deep learning has provided reliable support for the reconstruction of porous media due to its strong ability of learning and extracting characteristics from TIs and the reuse of network parameters. As a common deep learning method, generative adversarial networks (GANs) can obtain images highly similar to the structural characteristics of the TI through adversarial training between the generator and the discriminator. Based on GAN, a three-dimensional multi-scale pattern generative adversarial network (3D-MSPGAN) that can achieve 3D super-resolution reconstruction of porous media is proposed in this paper. The use of scaling transformation allows 3D-MSPGAN to reconstruct super-resolution images of porous media that are much larger than the TI, while the multi-scale discrimination in the discriminator provides a guarantee for the generation of high-quality super-resolution images, by which the discriminator can retain the global structure and local characteristics of the TI simultaneously during the reconstruction process. In addition, due to the multi-scale patch information used in the discriminator, only a single TI is needed to complete the reconstruction of porous media. Experimental comparison with some typical methods proves that 3D-MSPGAN can achieve the reconstruction of 3D porous media with faster speed and higher quality.
NSTL