Pore-network modeling is a widely used predictive tool for pore-scale studies in various applications that deal with multiphase flow in heterogeneous natural rocks. Despite recent improvements to enable pore-network modeling on simplified pore geometry extracted from rock core images and and its computational efficiency compared to direct numerical simulation methods, there are still limitations to modeling a large representative pore-network for heterogeneous cores. These are due to the technical limits on sample size to discern void space during X-ray scanning and computational limits on pore-network extraction algorithms. Thus, there is a need for pore-scale modeling approaches that have the natural advantages of pore-network modeling and can overcome these limitations, thereby enabling better representation of heterogeneity of the 3D complex pore structure and enhancing the accuracy of prediction of macroscopic properties. This paper addresses these issues with a workflow that includes a novel pore-network stitching method to provide large-enough representative pore-network for a core. This workflow uses micro-CT images of heterogeneous reservoir rock cores at different resolutions to characterize the pore structure in order to select few signature parts of the core and extract their equivalent pore-network models. The space between these signature pore-networks is filled by using their statistics to generate realizations of pore-networks which are then connected together using a deterministic layered stitching method. The output of this workflow is a large pore-network that can be used in any flow and transport solver. We validate all steps of this method on different types of natural rocks based on single-phase and two-phase flow properties such as drainage relative permeability curves of carbon dioxide and brine flow. Then, we apply the stochastic workflow on two large domain problems, connecting distant pore-networks and modeling a heterogeneous core. We generate multiple realizations and compare the average results with properties from a defined reference pore-network for each problem. We demonstrate that signature parts of a heterogeneous core, which are a small portion of its entire volume, are sufficient inputs for the developed pore-network stitching method to construct a representative pore-network and predict flow properties.

中文翻译：

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孔网拼接方法：一种用于多相流的孔到核心放大方法

孔隙网络建模是一种广泛用于孔隙尺度研究的预测工具，用于处理非均质天然岩石中的多相流的各种应用。尽管最近改进了从岩心图像中提取的简化孔隙几何结构的孔隙网络建模及其与直接数值模拟方法相比的计算效率，但对非均质岩心的大型代表性孔隙网络建模仍然存在局限性。这是由于在 X 射线扫描期间识别空隙空间的样本大小的技术限制和孔隙网络提取算法的计算限制。因此，需要具有孔隙网络建模的天然优势并可以克服这些限制的孔隙尺度建模方法，从而能够更好地表示 3D 复杂孔隙结构的异质性，并提高宏观特性预测的准确性。本文通过一个工作流程解决了这些问题，该工作流程包括一种新颖的孔隙网络拼接方法，可为岩心提供足够大的代表性孔隙网络。该工作流程使用不同分辨率的非均质储层岩心的显微 CT 图像来表征孔隙结构，以便选择岩心的少数特征部分并提取其等效的孔隙网络模型。这些特征孔隙网络之间的空间通过使用它们的统计数据生成孔隙网络的实现来填充，然后使用确定性分层拼接方法将这些孔隙网络连接在一起。此工作流的输出是一个大孔隙网络，可用于任何流动和输运求解器。我们基于单相和两相流动特性，如二氧化碳和盐水流的排水相对渗透率曲线，在不同类型的天然岩石上验证了该方法的所有步骤。然后，我们将随机工作流应用于两个大领域问题，连接遥远的孔隙网络和建模异构核心。我们生成多个实现并将平均结果与每个问题的定义参考孔隙网络的属性进行比较。我们证明了异质核心的特征部分，即其整个体积的一小部分，对于开发的孔隙网络拼接方法来说是足够的输入，以构建具有代表性的孔隙网络并预测流动特性。我们基于单相和两相流动特性，如二氧化碳和盐水流的排水相对渗透率曲线，在不同类型的天然岩石上验证了该方法的所有步骤。然后，我们将随机工作流应用于两个大领域问题，连接遥远的孔隙网络和建模异构核心。我们生成多个实现并将平均结果与每个问题的定义参考孔隙网络的属性进行比较。我们证明了异质核心的特征部分，即其整个体积的一小部分，对于开发的孔隙网络拼接方法来说是足够的输入，以构建具有代表性的孔隙网络并预测流动特性。我们基于单相和两相流动特性，如二氧化碳和盐水流的排水相对渗透率曲线，在不同类型的天然岩石上验证了该方法的所有步骤。然后，我们将随机工作流应用于两个大领域问题，连接遥远的孔隙网络和建模异构核心。我们生成多个实现并将平均结果与每个问题的定义参考孔隙网络的属性进行比较。我们证明了异质核心的特征部分，即其整个体积的一小部分，对于开发的孔隙网络拼接方法来说是足够的输入，以构建具有代表性的孔隙网络并预测流动特性。