Recent advances in three-dimensional imaging, using X-ray micro-tomography (µCT), has allowed to observe fluid configurations in porous media and measure various pore-scale properties such as contact angle, curvature, fluid connectivity (1-6). In general, gray-scale images obtained from µCT are pre-processed to remove artefacts before reconstruction and then filtered to enhance signal-to-noise ratio. Typically, the filtered images are segmented into various phases using a watershed algorithm. However, with recent developments in machine learning algorithms (7), gray-scale images can be effectively segmented without applying a noise-reduction filter, which can produce more accurate segmentation without the risk of possible smoothening of the image (8). This is essential to compute various porous media properties.
The segmented images are traditionally used to characterize the geometry of the pore space and to construct a pore-network representation of it. Most recently, these images have been used to determine the in-situ wettability state of porous media by measuring effective contact angle at the three-phase contact line, either manually (2) or most recently automatically (1, 9). Furthermore, the segmented data can be processed to estimate fluid-fluid curvature (4, 6, 10), which can then be used to calculate capillary pressure using the Young-Laplace equation. Estimation of these pore-scale properties, along with our ability to image dynamic processes using fast synchrotron imaging (3, 11), provide a valuable tool to understand immiscible fluid displacement in porous media, which was mainly restricted to two-dimensional visualization in the past. Although most of the individual three-dimensional image processing methods have already been demonstrated in recent years, the novelty is to integrate them together for the interpretation of µCT fluid flow experiments towards a specific goal.
Apart from the above discussed advances in measuring pore-scale properties, in this talk, I will show some simple tools using an open-access ImageJ software to process tomographic data. Moreover, I will provide examples of image processing that has helped to understand biological systems, particularly, termite nests.
1 Scanziani, A., Singh, K., Blunt, M. J. & Guadagnini, A. Automatic method for estimation of in situ effective contact angle from X-ray micro tomography images of two-phase flow in porous media. Journal of Colloid and Interface Science 496, 51-59 (2017).
2 Andrew, M., Bijeljic, B. & Blunt, M. J. Pore-scale contact angle measurements at reservoir conditions using X-ray microtomography. Advances in Water Resources 68, 24-31 (2014).
3 Singh, K. et al. Dynamics of snap-off and pore-filling events during two-phase fluid flow in permeable media. Scientific Reports 7, 5192 (2017).
4 Li, T., Schlüter, S., Dragila, M. I. & Wildenschild, D. An improved method for estimating capillary pressure from 3D microtomography images and its application to the study of disconnected nonwetting phase. Advances in Water Resources (2018).
5 Herring, A. L. et al. Effect of fluid topology on residual nonwetting phase trapping: Implications for geologic CO2 sequestration. Advances in Water Resources 62, 47-58 (2013).
6 Armstrong, R. T. et al. Estimation of curvature from micro-CT liquid-liquid displacement studies with pore scale resolution. In International Symposium of the Society of Core Analysts (2012).
7 Arganda-Carreras, I. et al. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424-2426 (2017).
8 Berg, S., Saxena, N., Shaik, M. & Pradhan, C. Generation of ground truth images to validate micro-CT image processing pipelines. The Leading Edge (2018, in press).
9 AlRatrout, A., Raeini, A. Q., Bijeljic, B. & Blunt, M. J. Automatic measurement of contact angle in pore-space images. Advances in Water Resources 109, 158-169 (2017).
10 Armstrong, R. T., Porter, M. L. & Wildenschild, D. Linking pore-scale interfacial curvature to column-scale capillary pressure. Advances in Water Resources 46, 55-62 (2012).
11 Berg, S. et al. Real-time 3D imaging of Haines jumps in porous media flow. Proceedings of the National Academy of Sciences 110, 3755-3759 (2013).
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