Shale oil and shale gas, as widely distributed unconventional resources, have attracted much attention at present due to the fast-increasing demands for energy. Since shale is the storage medium for shale oil and gas, its microscopic characteristics surely will influence its own storage ability and the difficulty of exploitation. The internal structures of shale can be studied based on establishing a digital core that can describe the characteristics of shale. There are two mainstream methods for the reconstruction of digital cores: the physical experimental methods and numerical reconstruction methods. The physical experimental methods usually need real rock samples as experimental materials, causing the high cost of experimental equipment and great difficulty of preparing the samples, especially the fragile samples such as shale. Numerical reconstruction methods are therefore often combined with the physical experimental methods to reconstruct digital cores, while the traditional numerical methods suffer from numerous reconstruction time. Recently, with the rapid development of deep learning and its branches, e.g., generative adversarial networks (GANs), the reconstruction method of digital cores possibly can benefit from the inherent ability of extracting characteristics by GAN. However, GAN may have slow training speed and unstable training process, so autoencoders (AEs) are introduced to address the issue of GAN. In this paper, combining the advantages of the two models (i.e., AE and GAN), a method AE-GAN is proposed to implement the 3D reconstruction of shale, and the effectiveness of this method is proven by comparing to some typical numerical methods.

页岩油和页岩气作为分布广泛的非常规资源，由于能源需求的快速增长，目前备受关注。由于页岩是页岩油气的储存介质，其微观特征必将影响其自身的储存能力和开采难度。建立能够描述页岩特征的数字化核心，可以研究页岩内部结构。数字岩芯重建的主流方法有两种：物理实验方法和数值重建方法。物理实验方法通常需要真实的岩石样品作为实验材料，导致实验设备成本高，样品制备难度大，尤其是页岩等脆性样品。因此，数值重建方法往往与物理实验方法相结合来重建数字岩心，而传统的数值方法重建时间长。最近，随着深度学习及其分支，例如生成对抗网络（GAN）的快速发展，数字核心的重建方法可能会受益于 GAN 提取特征的固有能力。然而，GAN 可能训练速度慢且训练过程不稳定，因此引入了自动编码器 (AE) 来解决 GAN 的问题。本文结合两种模型（即AE和GAN）的优点，提出了一种AE-GAN方法来实现页岩的3D重建，并通过与一些典型数值方法的对比验证了该方法的有效性。