Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

Research output: Contribution to journalArticleResearchpeer-review

Authors

  • Marcel Reinhardt
  • Arne Jacob
  • Saied Sadeghnejad
  • Francesco Cappuccio
  • Sascha Frank
  • Frieder Enzmann
  • Michael Kersten

Organisational units

External Organisational units

  • Chemical Engineering Faculty
  • Ruhr-Universität Bochum
  • Institute of Geosciences
  • University of Otago

Abstract

Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows, affecting the analysis of physical rock properties. Conventional segmentation techniques struggle with numerous image artifacts and user bias, which lead to considerable uncertainty. This study evaluates the advantages of using the random forest (RF) algorithm for the segmentation of fractured rocks. The segmentation quality is discussed and compared with two conventional image processing methods (thresholding-based and watershed algorithm) and an encoder–decoder network in the form of convolutional neural networks (CNNs). The segmented images of the RF method were used as the ground truth for CNN training. The images of two fractured rock samples are acquired by X-ray computed tomography scanning (XCT). The skeletonized 3D images are calculated, providing information about the mean mechanical aperture and roughness. The porosity, permeability, flow fields, and preferred flow paths of segmented images are analyzed by the DRP approach. Moreover, the breakthrough curves obtained from tracer injection experiments are used as ground truth to evaluate the segmentation quality of each method. The results show that the conventional methods overestimate the fracture aperture. Both machine learning approaches show promising segmentation results and handle all artifacts and complexities without any prior CT-image filtering. However, the RF implementation has superior inherent advantages over CNN. This method is resource-saving (e.g., quickly trained), does not need an extensive training dataset, and can provide the segmentation uncertainty as a measure for evaluating the segmentation quality. The considerable variation in computed rock properties highlights the importance of choosing an appropriate segmentation method.

Details

Original languageEnglish
Article number71
Number of pages20
JournalEnvironmental Earth Sciences
Volume81.2022
Issue number1
Early online date25 Jan 2022
DOIs
Publication statusPublished - Feb 2022