Image-based modelling of elastic properties using unresolved rock images
Rui Li, Yi Yang, Wenbo Zhan, Jianhui Yang, Yingfang Zhou

TL;DR
This paper introduces a new method to accurately predict sub-resolution pore fractions in unresolved digital rock images, improving elastic property simulations by combining statistical modeling with effective medium theory.
Contribution
The study presents a novel approach using the Beta distribution to model sub-resolution pore fractions, significantly enhancing elastic property predictions in digital rock models.
Findings
Beta distribution effectively models unresolved pore fractions.
Proposed method reduces prediction errors compared to existing techniques.
Improved elastic property predictions across various rock types.
Abstract
The trade-off between image resolution and model field-of-view has long been a limitation for numerical simulations in digital rock models. A significant amount of sub-resolution pore space cannot be captured in the unresolved digital rock images, which hinders the accuracy of numerical simulations, especially those predicting the rock effective elastic properties. This work uses paired digital rock images at multiple resolutions to investigate the sub-resolution solid and pore fraction distributions. It demonstrates that the cumulative Beta distribution function can effectively represent the solid and pore fractions in unresolved rock images. Based on this finding, we propose a novel methodology to predict the sub-resolution pore fractions. Compared to the pore fractions extracted from paired Bentheimer sandstone images at resolutions of 2, 6, and 18um, the proposed method yields…
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