Speaker
Description
Repeated laboratory experiments of complex physics often exhibit significant physical variability. Image-based analysis provides a powerful approach to investigate such variability. In this work, we employ optimal transport metrics to quantify differences across entire experimental datasets, enabling systematic clustering and identification of structural similarities. We present a complete workflow implemented within the Python framework DarSIA [1], which facilitates the transformation of image data into quantitative measures. The methodology is demonstrated on laboratory-scale CO₂ storage experiments conducted in a complex sandbox setup composed of sand layers and the use of a pH indicator, which together allow visualization of multiphase flow. This process involves conversion from raw photographs to concentration maps, followed by comparative analysis using advanced image-processing techniques. This general workflow can be applied across a wide range of imaging-based studies, making it suitable for diverse applications, including comparing numerical simulation outputs like those of the recent SPE11 benchmark [2].
| References | [1] Nordbotten, J. M., Benali, B., Both, J. W., Brattekås, B., Storvik, E., & Fernø, M. A. (2024). DarSIA: An open-source Python toolbox for two-scale image processing of dynamics in porous media. Transport in Porous Media, 151(5), 939-973. [2] Nordbotten, Jan M., et al. "Benchmarking CO₂ storage simulations: Results from the 11th Society of Petroleum Engineers Comparative Solution Project." International Journal of Greenhouse Gas Control 148 (2025): 104519. |
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| Country | Norway |
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