文章基本信息

标题：Deep learning for extracting micro-fracture: Pixel-level detection by convolutional neural network
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作者：Yejin Kim ; Seong Jun Ha ; Tae sup Yun 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2020
卷号：205
页码：3007-3011
DOI：10.1051/e3sconf/202020503007
出版社：EDP Sciences
摘要：Hydraulic stimulation has been a key technique in enhanced geothermal systems (EGS) and the recovery of unconventional hydrocarbon resources to artificially generate fractures in a rock formation. Previous experimental studies present that the pattern and aperture of generated fractures vary as the fracking pressure propagation. The recent development of three-dimensional X-ray computed tomography allows visualizing the fractures for further analysing the morphological features of fractures. However, the generated fracture consists of a few pixels (e.g., 1-3 pixels) so that the accurate and quantitative extract of micro-fracture is highly challenging. Also, the high-frequency noise around the fracture and the weak contrast across the fracture makes the application of conventional segmentation methods limited. In this study, we adopted an encoder-decoder network with a convolutional neural network (CNN) based on deep learning method for the fast and precise detection of micro-fractures. The conventional image processing methods fail to extract the continuous fractures and overestimate the fracture thickness and aperture values while the CNN-based approach successfully detects the barely seen fractures. The reconstruction of the 3D fracture surface and quantitative roughness analysis of fracture surfaces extracted by different methods enables comparison of sensitivity (or robustness) to noise between each method.