2021
DOI: 10.1016/j.asoc.2021.107185
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Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images

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Cited by 78 publications
(41 citation statements)
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References 73 publications
(96 reference statements)
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“…After mineral compositions were quantified by Energy-Dispersive X-ray Spectroscopy, the 2-D BSEM images were processed with NanoMin software and then registered into the 3-D μCT tomogram to align the BSEM image with the corresponding region in the registered μCT 3-D image volume (Latham et al, 2008). Through mapping the gray levels of μCT images to the mineral phases of BSEM images (Golab et al, 2010), the X-ray intensity range of each mineral is distinguished, and minerals of the μCT 3-D images are segmented, as presented by Wang et al (2020). An example of mineral segmentation along with the dry μCT tomogram and registered 2-D mineral map image acquired from QEMSCAN is illustrated in Figure S4.…”
Section: Core Flooding Experiments and Qemscan Imagingmentioning
confidence: 99%
“…After mineral compositions were quantified by Energy-Dispersive X-ray Spectroscopy, the 2-D BSEM images were processed with NanoMin software and then registered into the 3-D μCT tomogram to align the BSEM image with the corresponding region in the registered μCT 3-D image volume (Latham et al, 2008). Through mapping the gray levels of μCT images to the mineral phases of BSEM images (Golab et al, 2010), the X-ray intensity range of each mineral is distinguished, and minerals of the μCT 3-D images are segmented, as presented by Wang et al (2020). An example of mineral segmentation along with the dry μCT tomogram and registered 2-D mineral map image acquired from QEMSCAN is illustrated in Figure S4.…”
Section: Core Flooding Experiments and Qemscan Imagingmentioning
confidence: 99%
“…The data size is 1000×1000×750 voxels with a resolution of 1.8 micrometers. Further details on the micro-CT data are provided by Da Wang et al (2020). For the simulations, a mixed-wet system was generated by using a morphological approach to achieve irreducible water saturation (McClure et al 2021).…”
Section: Geometrical and Topological Characterization Of Wettingmentioning
confidence: 99%
“…Pore-scale micro-CT images of sedimentary rocks provide complex 3D images on the spatial distributions of minerals (Wildenschild and Sheppard 2013) yet lack the resolution needed to define thin brine films. In addition, the accurate quantification of micro-CT data beyond distinguishing solid from void remains a challenge (Da Wang et al 2020). Sedimentary rocks are commonly composed of many different solid materials, such as quartz, K-feldspar, muscovite, clay minerals, and other precipitated salts, as displayed in Fig.…”
Section: Introductionmentioning
confidence: 99%
“…One of the most well-attended subjects in multi-phase segmentation of porous material images is mineralogy analysis of sedimentary deposits (Andrew, 2018;Da Wang et al, 2020;Karimpouli & Tahmasebi, 2019). Sedimentary rocks are naturally formed by different minerals carried and deposited under high pressure and temperature conditions (Adams et al, 2017) and knowing their internal mineralogy in non-destructively has many applications in mining, petroleum geology and geoscience (Garfi et al, 2020;Massara et al, 2019).…”
Section: Multi-phase Segmentationmentioning
confidence: 99%