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Updated: Jun 18, 2026

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

Pore-network extraction from micro-computerized-tomography images.

Hu Dong1, Martin J Blunt

  • 1Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
PubMed
Summary

This study introduces a new algorithm to create rock pore network models from 3D images. This method accurately predicts rock properties like permeability, improving reservoir characterization.

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Area of Science:

  • Geosciences
  • Petroleum Engineering
  • Computational Physics

Background:

  • Network models are crucial for predicting rock properties like relative permeability.
  • Micro-computerized-tomography (micro-CT) provides detailed 3D images of rock pore space.
  • Directly inputting micro-CT images into network models is challenging due to data complexity.

Purpose of the Study:

  • To develop a method for extracting simplified pore and throat networks from micro-CT images.
  • To enable the use of detailed 3D rock images in predictive network modeling.
  • To parametrize pore network geometry and interconnectivity for computational analysis.

Main Methods:

  • A modified maximal ball algorithm was developed to extract pore-throat networks.
  • The algorithm extends previous work by Silin and Patzek.

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Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography
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  • Network parameters (coordination number, pore/throat size distributions) were computed.
  • Main Results:

    • The extracted pore network parameters showed good agreement with benchmark data.
    • Comparisons included networks from other methods, experimental data, and direct image computations.
    • The method demonstrated accuracy across a variety of rock types.

    Conclusions:

    • The developed algorithm effectively extracts simplified pore networks from micro-CT data.
    • This facilitates the use of 3D imaging in predictive reservoir modeling.
    • The approach enhances the ability to model and predict rock properties accurately.