The 3D digital rock technology is extensively utilized in analyzing rock physical properties, reservoir modeling, and other related fields. This technology enables the visualization, quantification, and analysis of microstructures in rock cores, leading to precise predictions and optimized designs of reservoir properties. Although the accuracy of 3D digital rock reconstruction algorithms based on physical experiments is high, the associated acquisition costs and reconstruction processes are expensive and complex, respectively. On the other hand, the 3D digital rock random reconstruction method based on 2D slices is advantageous in terms of its low cost and easy implementation, but its reconstruction effect still requires significant improvement. This article draws inspiration from the Concurrent single-image generative adversarial network and proposes an innovative algorithm to reconstruct 3D digital rock by improving the generator, discriminator, and noise vector in the network structure. Compared to traditional numerical reconstruction methods and generative adversarial network algorithms, the method proposed in this paper is shown to achieve good agreement with real samples in terms of Dykstra-Parson coefficient, porosity, two-point correlation function, Minkowski functionals, and visual display.

3D数字岩石技术广泛应用于岩石物性分析、储层建模等相关领域。该技术可以实现岩心微观结构的可视化、量化和分析，从而实现储层特性的精确预测和优化设计。虽然基于物理实验的3D数字岩石重建算法的精度很高，但相关的采集成本和重建过程分别昂贵且复杂。另一方面，基于2D切片的3D数字岩石随机重建方法具有成本低、易于实现的优势，但其重建效果仍需显着提高。本文从并发单图像生成对抗网络中汲取灵感，通过改进网络结构中的生成器、鉴别器和噪声向量，提出了一种创新的算法来重建 3D 数字岩石。与传统的数值重建方法和生成对抗网络算法相比，本文提出的方法在Dykstra-Parson系数、孔隙率、两点相关函数、Minkowski泛函和视觉显示方面与真实样本取得了良好的一致性。