Abstract Vuggy carbonate rocks have a complex geometry with pore size ranges from nano to several millimeters (e.g., vugs). Compared to other rocks (e.g., sandstones), this multiscale nature makes pore-scale studies very challenging. Vugs have significant effects on rock properties (e.g., porosity, permeability, capillary pressure, etc.) by establishing high conductive pathways. Multiscale pore network models can capture pores across various length scales and can be used to predict the flow behavior of these porous systems. In this study, a dual-scale PNM is implemented to reconstruct the behavior of a vuggy carbonate sample. The rock sample is CT scanned at two different scales. At the macro-scale (i.e., vugular-network), a medical-CT scanner is used to image the rock sample at the resolution of 100 μm. For better visualization of macropores (i.e., vugs) of the rock sample, imaging is conducted at both dry and saturated states with an X-ray attenuating brine (i.e., NaI solution). The rock is also imaged by a micro-CT scanner at the resolution of 0.75 μm to extract the micro-scale properties (i.e., micro-network). The images at both scales are preprocessed first. Then, the networks of both scales are extracted by two network extraction algorithms (i.e., the sub-network of the over-segmented watershed (SNOW) and the Maximal Ball (MB) method), and the results compared together. Then, a stochastically equivalent network based on the extracted micro-network properties is generated with a larger field of view (FOV). Then, vugs are randomly added to the reconstructed micro-network based on the properties of the macro-scale CT images. The result is a dual-scale unstructured irregular PNM. This modeling approach can efficiently preserve the vug-to-vug and vug-to-pore connectivity of overlapping vugs. The results show that the reconstructed dual-scale PNM has very close properties to the laboratory measurement data of the real rock sample.

中文翻译：

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使用多尺度成像技术对多孔碳酸盐进行双尺度孔隙网络重建

摘要 孔状碳酸盐岩具有复杂的几何形状，孔径范围从纳米到几毫米（例如孔洞）。与其他岩石（例如砂岩）相比，这种多尺度性质使得孔隙尺度研究非常具有挑战性。孔洞通过建立高导电通路对岩石特性（例如，孔隙度、渗透率、毛细管压力等）产生显着影响。多尺度孔隙网络模型可以捕获不同长度尺度的孔隙，并可用于预测这些多孔系统的流动行为。在这项研究中，实施了双尺度 PNM 来重建空洞碳酸盐样品的行为。岩石样本在两个不同的尺度上进行 CT 扫描。在宏观尺度（即，微孔网络），医学 CT 扫描仪用于以 100 μm 的分辨率对岩石样本进行成像。为了更好地可视化大孔（即 ，孔洞）的岩石样品，在干燥和饱和状态下使用 X 射线衰减盐水（即 NaI 溶液）进行成像。岩石还通过微型 CT 扫描仪以 0.75 μm 的分辨率成像，以提取微尺度特性（即微网络）。首先对两个尺度的图像进行预处理。然后，通过两种网络提取算法（即过分割分水岭（SNOW）和Maximal Ball（MB）方法）提取两种尺度的网络，并将结果进行比较。然后，基于提取的微网络属性生成具有更大视野（FOV）的随机等效网络。然后，根据宏观 CT 图像的特性，将孔随机添加到重建的微网络中。结果是双尺度非结构化不规则 PNM。这种建模方法可以有效地保留重叠孔洞的孔洞到孔洞和孔洞到孔的连通性。结果表明，重建的双尺度 PNM 与真实岩石样品的实验室测量数据具有非常接近的特性。