[논문]딥러닝 기반 균열 추출 기법을 통한 수압 파쇄 균열 형상 분석

박지민; 김광염; 윤태섭

doi:10.7843/kgs.2023.39.8.17

딥러닝 기반 균열 추출 기법을 통한 수압 파쇄 균열 형상 분석
Morphological Analysis of Hydraulically Stimulated Fractures by Deep-Learning Segmentation Method 원문보기

韓國地盤工學會論文集 = Journal of the Korean geotechnical society, v.39 no.8, 2023년, pp.17 - 28

박지민 (연세대학교 건설환경공학과) , 김광염 (한국해양대학교 에너지자원공학과) , 윤태섭 (연세대학교 건설환경공학과)

초록
AI-Helper

본 연구에서는 화강암 시편을 대상으로 파쇄 유체의 점성과 주입 속도를 변화시키며 실내 수압 파쇄 실험을 수행하였고, 3D X-ray CT 촬영을 통해 파쇄 후 시편 내부를 관찰하였다. 이미지 처리에 탁월한 성능을 보이는 합성곱 신경망(Convolutional Neural Network, CNN) 기반 Nested U-Net 모델 구조를 활용하여 CT 이미지 내 수압 파쇄 균열 추출을 수행하였고, 복잡한 형상의 미세균열을 정교하게 추출할 수 있었다. CNN 기반 모델로 추출된 균열을 3차원으로 재구성하여 균열의 부피, 두께, 굴곡도, 균열면 거칠기를 분석하였다. 그 결과 파쇄 유체의 점성이 클수록 균열 부피와 두께가 증가하였고, 굴곡도와 균열면의 거칠기가 감소하는 경향을 보였다. 또한 균열면의 굴곡도와 거칠기 이방성이 존재함을 확인할 수 있었다. 본 연구는, CNN 기반의 균열 추출 모델을 활용해 전통적인 이미지 처리 방법보다 정교한 균열 추출을 수행하고, 이를 기반으로 수압 파쇄 균열의 정량 분석을 성공적으로 수행하였다.

Abstract ▼ AI-Helper

Laboratory-scale hydraulic fracturing experiments were conducted on granite specimens at various viscosities and injection rates of the fracturing fluid. A series of cross-sectional computed tomography (CT) images of fractured specimens was obtained via a three-dimensional X-ray CT imaging method. Pixel-level fracture segmentation of the CT images was conducted using a convolutional neural network (CNN)-based Nested U-Net model structure. Compared with traditional image processing methods, the CNN-based model showed a better performance in the extraction of thin and complex fractures. These extracted fractures extracted were reconstructed in three dimensions and morphologically analyzed based on their fracture volume, aperture, tortuosity, and surface roughness. The fracture volume and aperture increased with the increase in viscosity of the fracturing fluid, while the tortuosity and roughness of the fracture surface decreased. The findings also confirmed the anisotropic tortuosity and roughness of the fracture surface. In this study, a CNN-based model was used to perform accurate fracture segmentation, and quantitative analysis of hydraulic stimulated fractures was conducted successfully.

주제어

표/그림 (11)

$Fig. 1. Configuration of the experimental fracturing system and following CT images$ 그림 Fig. 1. Configuration of the experimental fracturing system and following CT images
$Table 1. Experimental hydraulic fracturing settings. W-specimens indicate the fracturing conducted with water, O-specimens indicate the fracturing conducted with oil, and S-specimens indicate fracturing conducted on sleeved hole specimens$ 표 Table 1. Experimental hydraulic fracturing settings. W-specimens indicate the fracturing conducted with water, O-specimens indicate the fracturing conducted with oil, and S-specimens indicate fracturing conducted on sleeved hole specimens
$Fig. 2. (a) Thick fracture in CT image of S-3 specimen. (b) Profile of pixel values along a line across the S-3 fracture (a-a'). (c) Thin fracture in CT image of W-1 specimen. (d) Profile of pixel values along a line across the W-1 fracture (b-b')$ 그림 Fig. 2. (a) Thick fracture in CT image of S-3 specimen. (b) Profile of pixel values along a line across the S-3 fracture (a-a'). (c) Thin fracture in CT image of W-1 specimen. (d) Profile of pixel values along a line across the W-1 fracture (b-b')
$Fig. 3. Overall sequence of fracture segmentation based on deep learning model$ 그림 Fig. 3. Overall sequence of fracture segmentation based on deep learning model
$Fig. 4. (a) Fracture surface and RoI. (b) Morphological parameters on a profile of the fracture$ 그림 Fig. 4. (a) Fracture surface and RoI. (b) Morphological parameters on a profile of the fracture
$Fig. 5. Comparison of segmented fracture by locally adaptive thresholding method, canny edge method, CNN-based method$ 그림 Fig. 5. Comparison of segmented fracture by locally adaptive thresholding method, canny edge method, CNN-based method
표 Table 2. Indicators for the performance evaluation
$Fig. 6. Reconstructed three-dimensional fracture surfaces by CNN and LAT methods. (a) CNN prediction on thick and less tortuous fractures. (b) LAT prediction on thick and less tortuous fractures. (c) CNN prediction on thin and branched fractures. (d) LAT prediction on thin and branched fractures$ 그림 Fig. 6. Reconstructed three-dimensional fracture surfaces by CNN and LAT methods. (a) CNN prediction on thick and less tortuous fractures. (b) LAT prediction on thick and less tortuous fractures. (c) CNN prediction on thin and branched fractures. (d) LAT prediction on thin and branched fractures
$Fig. 7. Correlations between the viscosity of the fracturing fluid and morphological parameters. (a) Fracture volume. (b) Aperture. (c) Tortuosity. (d) JRC$ 그림 Fig. 7. Correlations between the viscosity of the fracturing fluid and morphological parameters. (a) Fracture volume. (b) Aperture. (c) Tortuosity. (d) JRC
표 Table 3. Average value of each morphological parameter depending on injection rate
그림 Fig. 8. Comparison of each morphological parameter computed along i- and j-directions. (a) aperture. (b) tortuosity. (c) JRC

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