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Reducing User Bias in X-ray Computed Tomography-Derived Rock Parameters through Image Filtering

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Abstract

Accurate representation of pore space is essential for predicting fluid flow through subsurface porous media. Pore volume fraction, geometry, and topology determine transport characteristics at the pore scale and are used to make upscaled projections about reservoir behavior. X-ray computed tomography (XCT) allows for nondestructive 3D imaging of rock core samples and can therefore provide valuable information about the pore network in situ, but segmentation of XCT datasets into pore and mineral space is not trivial. In this study, three filters (contrast enhancement, noise reduction, and beam hardening correction) were applied to XCT datasets of rock core samples prior to training class definition for machine learning-based segmentation. Porosities derived from segmented datasets with and without filtering were compared and were validated with experimental values. XCT-derived porosity had reduced variance and was closer to experimental data when all three filters were applied. A case study of one rock core sample compared pore size distribution and simulated permeability to experimental data. Computational fluid dynamics simulations of flow through the pore network using OpenFOAM showed improved consistency in permeability values when all three filters had been applied. This suggests that the application of these filters prior to machine learning training class definition can improve the reproducibility of the segmentation results and reduce user bias, thereby increasing confidence in digitally derived rock parameters. Reliable initial porosity and permeability data are critical for improving fluid transport and fate projections in a broad range of subsurface systems.

Article Highlights

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  • Filtered datasets were less affected by user bias in definition of training classes

  • Image filtering before segmentation improved consistency of simulated permeability

  • Image filtering improved reproducibility of digitally derived rock parameters

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Availability of data and material

The datasets generated and analyzed during the current study are available from the corresponding author upon request.

Code availability

The code used for this study is included in the Supplementary Information.

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Acknowledgements

The authors would like to thank Zixian Wang for her assistance in gathering laboratory results and Eva Albalghiti and three anonymous reviewers for their valuable feedback. Laboratory support: this study includes data produced in the CTEES facility at University of Michigan, supported by the Department of Earth & Environmental Sciences and College of Literature, Science, and the Arts. Mercury intrusion porosimetry data were collected in the Nanotechnicum lab in the Biointerfaces Institute at the University of Michigan.

Funding

This material is based upon work supported by the National Science Foundation (CAREER Grant No. 1943726) and the Alfred P. Sloan Foundation (Grant No. 2020–12,466). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This research was also supported by the University of Michigan Energy Institute through an Undergraduate Research Opportunities Program fellowship.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Michigan, 1351 Beal Ave, Ann Arbor, MI, 48109, USA

    Ellen P. Thompson, Kira Tomenchok, Tyler Olson & Brian R. Ellis

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  1. Ellen P. Thompson
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  2. Kira Tomenchok
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Contributions

Ellen P. Thompson: Formal analysis, Writing—Original draft, Conceptualization. Kira Tomenchok: Methodology, Conceptualization. Tyler Olson: Investigation. Brian R. Ellis: Supervision, Writing—Review & Editing.

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Correspondence to Brian R. Ellis.

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Thompson, E.P., Tomenchok, K., Olson, T. et al. Reducing User Bias in X-ray Computed Tomography-Derived Rock Parameters through Image Filtering. Transp Porous Med 140, 493–509 (2021). https://doi.org/10.1007/s11242-021-01690-3

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