Fast and accurate reconstruction of large-scale 3D porous media using deep learning

The accurate and efficient reconstruction of the porous media is the fundamental link to revealing its structural features and physical properties. In this work, we propose a deep learning (DL)-based algorithm to reconstruct large-scale three-dimensional (3D) porous media that can be treated as the representative element volumes (REVs), based on generative adversarial networks (GAN) and convolutional neural networks (CNN), named LGCNN. The proposed framework consists of a machine learning based (ML-based) reconstruction method for small-scale porous media and an adjustable splicing algorithm to achieve the REVs reconstruction. On this basis, four special neural networks are established to reconstruct the porous media and ensure the connectivity between the adjacent porous media during the splicing process. Subsequently, the detailed validation of LGCNN against traditional reconstruction methods and other deep learning algorithms is performed. The results show that the reconstruction speed (6003 voxels) of LGCNN (10 min) is much faster than traditional numerical reconstruction methods including QSGS (642 min), CCSIM (5973 min), and SD (33,628 min) with higher accuracy on structural parameters (e.g., porosity and pore size distribution), when compared with real porous media. In particular, the size of constructed porous media is far larger than previous ML-based reconstruction algorithms as much as 3–4 orders of magnitude, indicating the puissant ability of LGCNN to be used for high-resolution or multi-scale reconstruction.