Stokes‐Brinkman Flow Simulation Based on 3‐D μ‐CT Images of Porous Rock Using Grayscale Pore Voxel Permeability

Abstract

The direct flow simulation using high‐resolution micro‐computed tomographic (μ‐CT) images of porous rock can be used to help understand the flow characteristics at the pore‐scale and to estimate fluid properties; however, segmentation of pore space in grayscale 3‐D μ‐CT images, a necessary step in this process, is challenging because of issues related to the image resolution and pore‐filling matrix in the gray‐level images. We present a novel process for determining the voxel porosity and permeability of the gray‐level regime and evaluating the bulk permeability using the Brinkman force lattice Boltzmann method. In this study, μ‐CT images of Berea sandstone are acquired with two spatial resolutions. After the pore size distribution curve is experimentally obtained, the “apparent pore” voxels and “gray pore” voxels are determined based on the designated gray‐level values in the gray‐level (CT number) histogram, the cumulative and fractional pore volumes, and the linear relationship between CT number and individual voxel porosity. The results show that the boundary between apparent and gray pores in terms of size determines the volumetric fraction and specific surface area. Additionally, the permeability computed by considering the gray pore regime based on the proposed method is more similar to the experimentally measured value than the results of other segmentation methods because the gray pore domain is fully incorporated into the flow domain while preserving the pore connectivity. The proposed sequence of pore segmentation presents a method for handling gray‐level pore space without compromising pore connectivity.

Equivalent Pore Channel Model for Fluid Flow in Rock Based on Microscale X-ray CT Imaging

“…Thus, an irregular pore geometry cannot be resolved accurately. Efforts to supplement binarization have been conducted by applying ternary thresholding methods [24][25][26][27]. Ternary segmentation is based on the determination of a threshold intensity value, which denotes whether each voxel corresponds to a solid particle, pore, or another phase.…”

“…Various methods for identifying a suitable threshold value have been attempted, like matching an experimentally measured porosity with the CT voxel volume, running a region-growing algorithm or performing image clustering from a bimodal graph of intensity values [25,[28][29][30]. A pore size distribution (PSD) curve estimated by an MIP test can also be used for classifying those regions [27,31]. Kang et al [27] proposed a new variable named the separating diameter, and developed a method of comparing the classified voxel volume with the pore fraction volume from the PSD curve.…”

“…A pore size distribution (PSD) curve estimated by an MIP test can also be used for classifying those regions [27,31]. Kang et al [27] proposed a new variable named the separating diameter, and developed a method of comparing the classified voxel volume with the pore fraction volume from the PSD curve. They assigned various separating diameters as multiple values of the unit voxel size depending on the resolution of micro-CT.…”

“…Only for the REV region, the three phases could be defined as (1) apparent pores (hereafter P ap ) with a low intensity, (2) indistinct pores ( P indis ) with an intermediate intensity, and (3) apparent solid ( S ap ) with a high intensity. The threshold values for distinguishing the three phases were examined through a modified method based on that proposed by Kang et al [ 27 ]. In the MIP test, the volume of intruded mercury was measured as a function of increasing intrusion pressure in several stages, as shown through a graph of log differential intruded mercury versus intrusion pressure ( Figure 2 a; Choi et al [ 16 ]).…”

Dynamic capillary pressure analysis of tight sandstone based on digital rock model

“…From the qualitative evaluation of pores develops into the quantitative evaluation of pore distribution at the micron-nanometer level (Loucks et al, 2009;Andrew et al, 2014;Liu et al, 2018;Zhu et al, 2018;Li et al, 2019;Qu et al, 2020;Shan et al, 2020;, from 2D analysis relying on scanning electron microscopy (SEM) develops into 3D feature analysis using focused ion beam scanning electron microscopy (FIB-SEM) (Curtis et al, 2012;Tartakovsky et al, 2015;Shaina et al, 2016;Alexandra et al, 2020;Chen et al, 2020;Choi et al, 2020;Goral et al, 2020). But most of these researches are focus on imaging technology, the essence of the pore segmentation method is still based on the gray-scale threshold without any significantly improvement (Kang et al, 2019;Shou et al, 2020). While using the gray-scale threshold to segment the pores from the rock, lots of uncontrollable reasons can cause the inconsistent of gray-scale ranges (Chen et al, 2017).…”