Porous materials, such as carbonate rocks, frequently have pore sizes which span many orders of magnitude. This is a challenge for models that rely on an image of the pore space, since much of the pore space may be unresolved. In this work, sub-resolution porosity in X-ray images is characterized using differential imaging which quantifies the difference between a dry scan and 30 wt% potassium iodide brine saturated images. Once characterized, we develop a robust workflow to incorporate the sub-resolution pore space into a network model using Darcy-type elements called microlinks. Each grain voxel with sub-resolution porosity is assigned to the two nearest resolved pores using an automatic dilation algorithm. By including these microlinks with empirical models in flow modeling, we simulate single-phase and multiphase flow. By fine-tuning the microlink empirical models, we match permeability, formation factor (the ratio of the resistivity of a rock filled with brine to the resistivity of that brine), and drainage capillary pressure to experimental results. We then show that our model can successfully predict steady-state relative permeability measurements on a water-wet Estaillades carbonate sample within the uncertainty of the experiments and modeling. Our approach of incorporating sub-resolution porosity in two-phase flow modeling using image-based multiscale pore network techniques can capture complex pore structures and accurately predict flow behavior in porous materials with a wide range of pore size.

中文翻译：

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将亚分辨率孔隙度纳入具有复杂微孔岩石多尺度孔隙网络的两相流模型中

多孔材料，例如碳酸盐岩，通常具有跨越多个数量级的孔径。这对于依赖孔隙空间图像的模型来说是一个挑战，因为大部分孔隙空间可能无法解析。在这项工作中，使用差分成像来表征 X 射线图像中的亚分辨率孔隙率，该差分成像量化了干扫描和 30 wt% 碘化钾盐水饱和图像之间的差异。一旦表征，我们开发了一个强大的工作流程，使用称为微链接的达西型元素将亚分辨率孔隙空间合并到网络模型中。使用自动膨胀算法将具有亚分辨率孔隙度的每个颗粒体素分配给两个最近的解析孔隙。通过将这些微链接与流动建模中的经验模型结合起来，我们模拟了单相流和多相流。通过微调微连接经验模型，我们将渗透率、地层因子（充满盐水的岩石的电阻率与该盐水的电阻率之比）和排水毛细管压力与实验结果相匹配。然后，我们表明，我们的模型可以在实验和建模的不确定性范围内成功预测水湿 Estaillades 碳酸盐样品的稳态相对渗透率测量结果。我们使用基于图像的多尺度孔隙网络技术将亚分辨率孔隙率纳入两相流建模中的方法可以捕获复杂的孔隙结构并准确预测具有各种孔径的多孔材料的流动行为。