We propose a novel method to predict fluid flow properties of reservoir rocks from 2D rock images by connecting pore clusters based on their intrinsic properties. Spatial proximity and connecting large clusters with priority are the two basic connecting principles of this method. A few methods from complex networks were used to analyze the topology and connectivity of the connected pore networks, resulting in more realistic and optimal pore networks from 2D images. A new topological descriptor is proposed and was found to perform well in quantifying the network topology by considering both the isolated pore cluster number and the Euler characteristic. The new method can predict permeability from the 2D rock images with a reasonable agreement compared to the reference cases for different rock types and complex pore structures.

我们提出了一种新方法，通过基于其固有特性连接孔隙簇，从二维岩石图像中预测储层岩石的流体流动特性。空间接近和优先连接大集群是该方法的两个基本连接原则。使用复杂网络中的一些方法来分析连通孔隙网络的拓扑结构和连通性，从而从二维图像中得到更真实和最优的孔隙网络。提出了一种新的拓扑描述符，通过考虑孤立孔簇数和欧拉特性，发现在量化网络拓扑方面表现良好。与不同岩石类型和复杂孔隙结构的参考案例相比，新方法可以从二维岩石图像中预测渗透率，并具有合理的一致性。