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Updated: Jan 22, 2026
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
Published on: September 7, 2019
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Quantification of Uncertainty and Best Practice in Computing Interfacial Curvature from Complex Pore Space Images
Takashi Akai1, Qingyang Lin2, Abdulla Alhosani1
1Department of Earth Science & Engineering, Imperial College London, London SW7 2AZ, UK.
Materials (Basel, Switzerland)
|July 7, 2019
Summary
Quantifying curvature from micro-CT images is challenging due to voxelization errors. This study identifies the best methods for curvature computation and quantifies errors in multiphase flow simulations.
Area of Science:
- Pore-scale physics
- Multiphase flow
- Image analysis
Background:
- High-resolution 3D X-ray CT imaging enables pore-scale visualization of fluid configurations during multiphase displacement.
- Quantifying curvature from voxelized micro-CT data presents challenges due to potential errors that remain unquantified.
Purpose of the Study:
- To identify optimal methods for computing interfacial curvature from micro-CT images.
- To quantify the errors associated with different curvature computation techniques.
Main Methods:
- Direct numerical simulations of oil/water drainage and imbibition were performed on a bead pack and Bentheimer sandstone.
- Investigated curvature computation on both simulated and segmented interfaces.
- Compared computed curvature with simulated capillary pressure, enabling pore-by-pore analysis.
Main Results:
- Local capillary pressures can be estimated within 30% error when the average radius of curvature is >6 times image resolution.
- Average capillary pressure can be estimated within 11% error when the average radius of curvature is >10 times image resolution.
Conclusions:
- This study provides a validated method for accurate pore-scale curvature measurement from micro-CT data.
- Established error quantification for curvature-based capillary pressure estimations in porous media.
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