U-Net model for multi-component digital rock modeling of shales based on CT and QEMSCAN images
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Title
U-Net model for multi-component digital rock modeling of shales based on CT and QEMSCAN images
Authors
Keywords
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Journal
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
Volume 216, Issue -, Pages 110734
Publisher
Elsevier BV
Online
2022-06-14
DOI
10.1016/j.petrol.2022.110734
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- (2022) Changfu Liu et al. Advances in Geo-Energy Research
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- MudrockNet: Semantic segmentation of mudrock SEM images through deep learning
- (2021) Abhishek Bihani et al. COMPUTERS & GEOSCIENCES
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- (2021) Dmitriy A. Martyushev et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
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- (2021) Linqi Zhu et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Deep shale gas in China: Geological characteristics and development strategies
- (2021) Xinhua Ma et al. Energy Reports
- Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin
- (2020) Zhuoheng Chen et al. COMPUTERS & GEOSCIENCES
- Multi-scale and multi-component digital core construction and elastic property simulation
- (2020) Li-Kai Cui et al. Applied Geophysics
- X-ray tomography imaging of shale microstructures: A review in the context of multiscale correlative imaging
- (2020) Muhammad Arif et al. INTERNATIONAL JOURNAL OF COAL GEOLOGY
- Application of Machine Learning Techniques in Mineral Classification for Scanning Electron Microscopy - Energy Dispersive X-Ray Spectroscopy (SEM-EDS) Images
- (2020) Chunxiao Li et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Digital core construction of fractured carbonate rocks and pore-scale analysis of acoustic properties
- (2020) Maojin Tan et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- A gentle introduction to deep learning in medical image processing
- (2019) Andreas Maier et al. Zeitschrift fur Medizinische Physik
- Segmentation of digital rock images using deep convolutional autoencoder networks
- (2019) Sadegh Karimpouli et al. COMPUTERS & GEOSCIENCES
- Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales
- (2019) Yaokun Wu et al. FUEL
- Variable secondary porosity modeling of carbonate rocks based on μ-CT images
- (2019) Xin Nie et al. Open Geosciences
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- (2019) Yongfei Yang et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- A survey on deep learning techniques for image and video semantic segmentation
- (2018) Alberto Garcia-Garcia et al. APPLIED SOFT COMPUTING
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- (2018) Liang-Chieh Chen et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Predictions of permeability, surface area and average dissolution rate during reactive transport in multi-mineral rocks
- (2018) Min Liu et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Pore-scale imaging and modelling
- (2012) Martin J. Blunt et al. ADVANCES IN WATER RESOURCES
- Digital rock physics benchmarks—Part I: Imaging and segmentation
- (2012) Heiko Andrä et al. COMPUTERS & GEOSCIENCES
- Relative Permeability Calculations from Two-Phase Flow Simulations Directly on Digital Images of Porous Rocks
- (2011) Thomas Ramstad et al. TRANSPORT IN POROUS MEDIA
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