Clinical recording of FI is both over and underestimated compared to CBCT analysis. This was highest for probing recording grade I furcation involvement where it was highly overestimated. The occurrence of over and under estimation of FI will affect the assignment of prognosis to multirooted teeth, which can influence treatment planning for periodontal therapy and may result in inappropriate treatment.
Fluid flow in porous rocks is commonly capillary driven and thus, dependent on the surface characteristics of rock grains and in particular the connectivity of corners and crevices in which fluids reside. Traditional microfluidic fabrication techniques do not provide a connected pathway of crevices that are essential to mimic multiphase flow in rocks. Here, geo-material microfluidic devices with connected pathways of corners and crevices were created by functionalising Polydimethylsiloxane (PDMS) with rock minerals. A novel fabrication process that provides attachment of rock minerals onto PDMS was demonstrated. The geo-material microfluidic devices were compared to carbonate and sandstone rocks by using energy dispersive X-ray spectroscopy, scanning electron microscopy (SEM), contact angle measurements, and a surface profilometer. Based on SEM coupled with energy-dispersive X-ray spectrometry (SEM-EDS) analyses, roughness measurements, contact angle, wettability, and roughness were comparable to real rocks. In addition, semivariograms showed that mineral deposition across the different geo-material devices was nearly isotropic. Lastly, important multiphase flow phenomena, such as snap-off and corner flow mechanisms, equivalent to those occurring in reservoir rocks have been visualised. The presented approach can be used to visualise rock-fluid interactions that are relevant to subsurface engineering applications, such as hydrocarbon recovery and CO2 sequestration.
The concept of linking pore‐scale data to continuum‐scale characteristics of porous media relies on the existence of a representative elementary volume (REV). The current techniques for estimating REVs require access to segmented micro‐computed tomographic (micro‐CT) images and computations of petrophysical properties which are computationally intensive and time‐consuming. Herein, a texture characterization method called the Gray‐Level Size Zone Matrix (GLSZM) is applied directly to raw grayscale micro‐CT images. GLSZM representations of 3D micro‐CT images capture information regarding the connectivity of gray‐level intensities, termed as “size‐zones.” Statistical descriptors of pore space are calculated based on GLSZM to understand the connectivity of low gray‐level intensities. These GLSZM statistics capture microstructural fluctuations and offer insights into the impact of grayscale heterogeneity on REV size. This approach allows REV sizes to be estimated directly using grayscale micro‐CT images, in a reproducible, less time‐consuming and computationally efficient manner.
Modeling flow and transport in porous media using pore‐scale modeling is reliant on rock properties derived from digital rock images using segmentation techniques. These digital rock images obtained using computed tomography incorporate the variation in the intensity of phases depending on the attenuation of X‐rays. A standard technique is the segmentation of tomographic images based on user‐selected grayscale thresholding, allowing the identification of different phases. This threshold is subjective based on the operator and results in loss of essential information about the grayscale variation after segmentation. This paper implements the gray‐level co‐occurrence matrix (GLCM) incorporating the full range of grayscale information. The GLCM captures the relative occurrence of grayscale values in a spatial map. These maps show visually connected/disconnected populations of different phases such as pore space, quartz grains, minerals, and other features. We show that each rock has its own GLCM signature depending on the variations in gray‐level intensities. Several statistical measures are calculated: (1) GLCM contrast describing local variation in the gray‐level intensities, (2) GLCM angular second moment, describing the rock homogeneity; (3) GLCM mean, describing weighted average of the probability of occurrence of features based on their location on the GLCM map; and (4) GLCM correlation, measuring the linear dependencies of grayscale values and the degree of (an) isotropy in the micro–computed tomographic images of each of the rock types. The GLCM method provides a pathway to alleviate user biases and allow automation of micro–computed tomography analyses.
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