MRI
Glass beads:
The first sample, a 4.0-mm glass bead pack, was used to test the Mathematica notebook workflow described in the materials and methods section. The high connectivity and simplicity of this porous medium makes it ideal for testing the Mathematica notebook for accuracy in porosity calculations and PCA. The larger pore space between individual beads allows for water molecules to yield T2 values longer than those they would have in a rock sample due to less confinement. These conditions prompted the use of the RARE-Inv-Rec pulse sequence to reproduce the structural features of the simple bead sample. Figure 4 shows the results of image collection with RARE-Inv-Rec (Figure 4A) and the application of the Mathematica notebook to the glass bead sample (workflow steps 1-6, Figure 4B-4E). The set of MRI images was uploaded into the notebook and some slices in the set were selected. The selected 2D slices were used to construct a 3D rendering of the sample. The 3D rendering was then taken through a series of cropping stages including Cartesian and cylindrical cropping (Figure 4B and Figure 4C). Once the 3D rendering was finalized through the cropping stages, a set of filters was applied. These filters include color convert from RBG to greyscale, sharpen, and a Gaussian filter (Figure 4D). As shown by the color scale to the right of the image in Figure 4D, the higher the proton density in the sample, the more yellow the color in the figure will be. This also translates to pixel values between 0 and 1, which are later used in the binarization process. The bright yellow pixels n Figure 4D represent the water that fills the pore space around the beads, whereas the beads appear as empty spaces. Lower proton density can also be observed near the surface of the beads. The high connectivity of the fluid expected from this simple is also clearly detected. Once the filters are applied to the cropped 3D image, the 3D image is then transformed to its binary form (Figure 4E), which allows for further analysis to be applied to the image via PCA. The binary transformation was applied using threshold values between 0.05 and 1.0 in 0.05 increments. This process permits a comprehensive analysis of the sample.