Evaluation of Image Segmentation Techniques for Image-Based Rock Property Estimation

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.

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Work Title Evaluation of Image Segmentation Techniques for Image-Based Rock Property Estimation
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Open Access
Creators
  1. Prakash Purswani
  2. Zuleima Karpyn
  3. Khaled Ahmed Enab
  4. Yuan Xue
  5. Sharon Xiaolei Huang
Keyword
  1. X-ray imaging
  2. porous media
  3. image segmentation
  4. machine learning
  5. WEKA segmentation
  6. MIA clustering
License In Copyright (Rights Reserved)
Work Type Article
Acknowledgments
  1. The authors acknowledge the Energi Simulation Foundation, the John and Willie Leone Family Department of Energy and Mineral Engineering, and the EMS Energy Institute at Penn State University for providing financial support for this project. We also acknowledge the laboratory scientist, Tim Stecko, at Penn State’s Center for Quantitative Imaging Laboratory for assistance in carrying out the X-ray imaging experiment assumed in this research. We thank the anonymous reviewrs for insightful suggestions on this work. Purswani greatly acknowledges Dr. Nicholas Stephens, Department of Anthropology, Penn State University, for help in the implementation of the MIA clustering technique
Publication Date December 2020
Deposited February 26, 2021

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  • Updated Acknowledgments Show Changes
    Acknowledgments
    • The authors acknowledge the Energi Simulation Foundation, the John and Willie Leone Family Department of Energy and Mineral Engineering, and the EMS Energy Institute at Penn State University for providing financial support for this project. We also acknowledge the laboratory scientist, Tim Stecko, at Penn State’s Center for Quantitative Imaging Laboratory for assistance in carrying out the X-ray imaging experiment assumed in this research. We thank the anonymous reviewrs for insightful suggestions on this work. Purswani greatly acknowledges Dr. Nicholas Stephens, Department of Anthropology, Penn State University, for help in the implementation of the MIA clustering technique
  • Added Creator Prakash Purswani
  • Updated Creator Zuleima Karpyn
  • Added Creator Khaled Ahmed Enab
  • Added Creator Yuan Xue
  • Added Creator Sharon Xiaolei Huang
  • Added Segmentation_paper_JPSE_Aug_Clean.pdf
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