The observation and analyses of cores are the bases of various studies on oil and gas exploration and development. However, the real natural cores suffer from their own instability and constant weathering or erosion as well as experimental damages, leading to the changes of their physical and chemical characteristics. Digital cores can address the above issues by core digitalization and then reusing the original core images or data without damaging the real samples. The 3D reconstruction of pore space actually is an important step for the construction of 3D digital cores. There are two main ways for the reconstruction of pore space including physical experimental methods and numerical reconstruction methods. Physical experimental methods usually are quite time-consuming and expensive while numerical reconstruction methods are relatively inexpensive and more efficient. With the flourishing development of deep learning and its variants, the reconstruction of pore space possibly can benefit from the strong inherent ability of extracting characteristics from training images (TIs) hidden in deep learning. This paper proposes a reconstruction method using a deep residual deconvolution network (DRDN), considered as a variant of deep learning, in which the characteristics of TI are learned by using constant residual convolution in training and then the pore space is reconstructed by adding the previous residual convolution results to each deconvolution layer. Compared to some other typical numerical reconstruction methods, our method shows its efficiency and practicability in the reconstruction of 3D pore space.

岩心的观测和分析是开展各种油气勘探开发研究的基础。然而，真正的天然岩心由于其自身的不稳定性和不断的风化或侵蚀以及实验破坏，导致其物理和化学特性发生变化。数字岩心可以通过岩心数字化，然后在不破坏真实样本的情况下重用原始岩心图像或数据来解决上述问题。孔隙空间的 3D 重建实际上是构建 3D 数字岩心的重要步骤。孔隙空间重构的方法主要有物理实验法和数值重构法两种。物理实验方法通常非常耗时且昂贵，而数值重建方法相对便宜且效率更高。随着深度学习及其变种的蓬勃发展，孔隙空间的重建可能受益于从隐藏在深度学习中的训练图像（TI）中提取特征的强大内在能力。本文提出了一种使用深度残差反卷积网络（DRDN）的重构方法，被认为是深度学习的一种变体，其中在训练中通过使用恒定残差卷积来学习TI的特征，然后通过添加前面的重构孔空间残差卷积结果到每个反卷积层。与其他一些典型的数值重建方法相比，