A novel regional-minima image segmentation method for fluid transport simulations in unresolved rock images

Rui Li, Yi Yang, Yuxuan Zhang, Wenbo Zhan, Jianhui Yang, Yingfang Zhou* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Unresolved digital rock images are often used to avoid high computational costs and limited field of views associated with processing fine-resolution rock images. However, segmentation of unresolved images using classical methods is suboptimal due to the presence of the partial-volume effect. Suboptimal segmentations can significantly influence the geometry and effective properties of the reconstructed models. This study reveals that partial-volume pixels with high pore fraction remain as regional minima in intensity levels in unresolved images. By identifying these regional-minima pixels, we can effectively extract pore space that is obscured by the partial-volume effect. Based on this
observation, we propose a novel segmentation method capable of identifying these regional minima partial-volume pixels and converting them to pure pore pixels, thereby binarising the digital rock images. The method is validated on sandstone and carbonate rock samples. Our method demonstrates a notable improvement in modelled permeability accuracy, surpassing 50% compared to the thresholding method and over 30% compared to the watershed method. Moreover, models segmented by this approach exhibit smaller pore and throat sizes compared to the substantially overestimated results obtained by classical methods. These findings suggest that the regional-minima segmentation method effectively corrects for the partial-volume effect and preserves more detailed pore structures. Consequently, it enhances the quality of binarised rock geometries, leading to improved accuracy in fluid-flow simulations
Original languageEnglish
Article numbere2023WR036855
Number of pages28
JournalWater Resources Research
Issue number6
Early online date22 Jun 2024
Publication statusPublished - Jun 2024

Bibliographical note

This study would not be possible without digital rock images provided by Digital Rocks Portal and its contributors (https://www.digitalrocksportal.org/).


Data Availability Statement

The Otsu image segmentation is implemented using the open-source image processing software, ImageJ (Schneider et al., 2012), which can be accessed at https://imagej.nih.gov/ij/.The watershed segmentation is carried out in Dragonfly software, Version 2022.2 for [Windows]. Comet Technologies Canada Inc., Montreal, Canada; software available at https://www.theobjects.com/dragonfly. The pore network extraction software is pnextract (Raeini et al., 2017), available at https://github.com/ImperialCollegeLondon/pnextract. The software for viewing and post-processing the digital models is ParaView (Ahrens et al., 2005), accessible at https://www.paraview.org/. The figures are created using the open-source plotting software, Veusz, which can be downloaded from https://veusz.github.io/.


  • regional-minima
  • image segmentation
  • fluid flow
  • unresoloved rock images


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