Accurate characterization of rock and fluid properties in porous media using X-ray imaging techniques depends on reliable identification and segmentation of the involved phases. Segmentation is critical for the estimation of porosity, fluid saturations, fluid and rock topology, and pore connectivity, among other pore-scale properties. Therefore, the purpose of this study is to compare the effectiveness of different image segmentation techniques when applied to image data analysis in porous media. Two machine learning based segmentation techniques – a supervised ML technique called Fast Random Forest, and an unsupervised method combining k-means and fuzzy c-means clustering algorithms – were compared using an experimental data set. Comparisons are also presented against traditional thresholding segmentation. In addition, we discuss the potential and limitations of applying deep learning based segmentation algorithms. The performance of the segmentation techniques were compared on estimates of porosity, saturation, and surface area, as well as pore-scale estimates such as fluid-fluid interfacial areas, and Euler characteristic. X-ray micro-computed tomography images for a sintered glass frit, saturated with two-phases (air and brine), were acquired at two different voxel resolutions. The high-resolution images (6 μm) were used as the benchmark case, while the low-resolution images (18 μm) were segmented by three segmentation techniques: Fast Random Forest, clustering, and thresholding. The results for porosity and phase saturation from thresholding and from the supervised ML method (i.e. Fast Random Forest) were found to be close to the benchmark case. Segmentation results from the unsupervised ML method (i.e. clustering) were largely unsatisfactory, except for total surface area measurements. The supervised ML segmentation results provided better measurements for air-brine interfacial areas by capturing three-phase interfacial regions. Also, all segmentation techniques resulted in similar measurements for air-phase Euler characteristic confirming poor connectivity of the trapped air phase, although the closest results were obtained by the supervised ML method. Finally, despite the supervised ML segmentation technique being more computationally intensive, it was found to require less user intervention and its implementation was more straightforward. In summary, this work provides insights into different segmentation techniques, their implementation, as well as advantages and limitations with regards to quantitative analysis of pore-scale properties in saturated porous media.
All Science Journal Classification (ASJC) codes
- Fuel Technology
- Geotechnical Engineering and Engineering Geology