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Updated: Mar 30, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock.

Kirill M Gerke^1,2,3,4, Marina V Karsanina^1,3,4, Dirk Mallants¹

¹CSIRO Land and Water, Glen Osmond, PB2, SA 5064, Australia.

Scientific Reports

|November 3, 2015

Summary

We developed a method to merge spatial data from different resolutions into one comprehensive image. This technique enhances the analysis of multiscale data, improving the quantification of properties in materials like shale rock.

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Last Updated: Mar 30, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

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

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Published on: February 25, 2015

Area of Science:

Geosciences
Material Science
Data Science

Background:

Spatial data from various sensor resolutions contain rich information but are difficult to integrate.
Merging multiscale data is crucial for accurate property quantification and analysis.

Purpose of the Study:

To develop a general method for merging multiscale categorical spatial data into a single dataset.
To demonstrate the method's versatility by merging macro, micro, and nanoscale images of shale rock.

Main Methods:

Utilized stochastic reconstructions with rescaled correlation functions for data merging.
Fused images from X-ray microtomography and scanning electron microscopy into a single, predefined resolution image.

Main Results:

Successfully merged multiscale spatial data from shale rock images.
Created a unified dataset representing mineral, organic matter, and porosity distribution across scales.

Conclusions:

The developed methodology provides a versatile solution for integrating multiscale spatial data.
This approach has broad applications in geosciences, material science, climatology, and remote sensing, aiding in property assessment and data compression.