DOI: 10.1016/j.earscirev.2021.103555
论文题名: Deep learning in pore scale imaging and modeling
作者: Wang Y.D. ; Blunt M.J. ; Armstrong R.T. ; Mostaghimi P.
刊名: Earth Science Reviews
ISSN: 00128252
出版年: 2021
卷: 215 语种: 英语
中文关键词: Deep learning
; Permeability
; Pore-scale
; Reconstruction
; Segmentation
; Super resolution
英文关键词: accuracy assessment
; artificial neural network
; computer simulation
; core analysis
; data acquisition
; Earth science
; identification method
; imaging method
; multiphase flow
; numerical model
; permeability
; segmentation
英文摘要: Pore-scale imaging and modeling has advanced greatly through the integration of Deep Learning into the workflow, from image processing to simulating physical processes. In Digital Core Analysis, a common tool in Earth Sciences, imaging the nano- and micro-scale structure of the pore space of rocks can be enhanced past hardware limitations, while identification of minerals and phases can be automated, with reduced bias and high physical accuracy. Traditional numerical methods for estimating petrophysical parameters and simulating flow and transport can be accelerated or replaced by neural networks. Techniques and common neural network architectures used in Digital Core Analysis are described with a review of recent studies to illustrate the wide range of tasks that benefit from Deep Learning. Focus is placed on the use of Convolutional Neural Networks (CNNs) for segmentation in pore-scale imaging, the use of CNNs and Generative Adversarial Networks (GANs) in image quality enhancement and generation, and the use of Artificial Neural Networks (ANNs) and CNNs for pore-scale physics modeling. Current limitations and challenges are discussed, including advances in network implementations, applications to unconventional resources, dataset acquisition and synthetic training, extrapolative potential, accuracy loss from soft computing, and the computational cost of 3D Deep Learning. Future directions of research are also discussed, focusing on the standardization of datasets and performance metrics, integrated workflow solutions, and further studies in multiphase flow predictions, such as CO2 trapping. The use of Deep Learning at the pore-scale will likely continue becoming increasingly pervasive, as potential exists to improve all aspects of the data-driven workflow, with higher image quality, automated processing, and faster simulations. © 2021 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166533
Appears in Collections: 气候变化与战略
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作者单位: School of Minerals and Energy Resources Engineering, University of New South Wales, Australia; Department of Earth Science and Engineering, Imperial College London, United Kingdom
Recommended Citation:
Wang Y.D.,Blunt M.J.,Armstrong R.T.,et al. Deep learning in pore scale imaging and modeling[J]. Earth Science Reviews,2021-01-01,215