We used a stochastic 3D pore-scale simulation approach to statistically elucidate the effect of stochastic pore connectivity on permeability and hydraulic tortuosity of highly heterogeneous porous media such as carbonate rocks. The novel nature of our workflow lies in the generation of multiple 3D pore microstructures of the same effective porosity, pore size distribution, number of pores, but different stochastic pore connectivity where the only pore microstructural feature changing is pore connectivity. This workflow allows the explicit study of the role pore connectivity plays in permeability and hydraulic tortuosity without the interference of other pore microstructural factors or noise. Permeability and hydraulic tortuosity of the 3D pore microstructures of the aforementioned characteristics was obtained from direct pore-scale simulations using STAR CCM+. Our approach suppresses the necessity of conducting hundreds of experimental measurements and allows the training of neural network models to predict permeability and hydraulic tortuosity. We show that an approximate twofold increase in heterogeneity (pore size standard deviation), results in a two orders of magnitude reduction in permeability, and that an increase in heterogeneity results in a systematic shift of permeability from normal distribution to lognormal distribution. While the stochastic connectivity of pores has a significant impact on permeability, it has only minimal effect on hydraulic tortuosity. Furthermore, the predictability of permeability from hydraulic tortuosity decreases with an increasing heterogeneity. The high coefficient of determination obtained in permeability prediction with a feedforward neural network (NN) model trained with of PTSD data along with pore surface area parameters indicates that NN algorithms can capture the effect of stochastic pore connectivity on permeability. Since PTSD data and surface parameters can be obtained from mercury injection capillary pressure (MICP) measurements, our findings have large implication toward the prediction of permeability and hydraulic tortuosity in highly heterogeneous porous media.

我们使用随机 3D 孔隙尺度模拟方法来统计地阐明随机孔隙连通性对高度非均质多孔介质（如碳酸盐岩）的渗透率和水力弯曲度的影响. 我们工作流程的新颖性在于生成具有相同有效孔隙率、孔径分布、孔隙数量但随机孔隙连通性不同的多个 3D 孔隙微观结构，其中唯一改变孔隙微观结构特征是孔隙连通性。该工作流程允许明确研究孔隙连通性在渗透率和水力弯曲度中所起的作用，而不受其他孔隙微观结构因素或噪声的干扰。上述特征的 3D 孔隙微观结构的渗透率和水力弯曲度是通过使用 STAR CCM+ 的直接孔隙尺度模拟获得的。我们的方法抑制了进行数百次实验测量的必要性，并允许训练神经网络模型来预测渗透率和水力弯曲度。正态分布到对数正态分布。虽然孔隙的随机连通性对渗透率有显着影响，但对水力弯曲度的影响很小。此外，水力曲折对渗透率的可预测性随着​​非均质性的增加而降低。前馈渗透率预测中获得的高确定系数使用 PTSD 数据和孔隙表面积参数训练的神经网络 (NN) 模型表明，NN 算法可以捕捉随机孔隙连通性对渗透率的影响。由于 PTSD 数据和表面参数可以从压汞毛细管压力 (MICP) 测量中获得，因此我们的研究结果对预测高度非均质多孔介质中的渗透率和水力弯曲度具有重要意义。