气候变化领域集成服务门户(CCPortal): Deep learning in pore scale imaging and modeling

CCPortal

DOI	10.1016/j.earscirev.2021.103555
	Deep learning in pore scale imaging and modeling
	Wang Y.D.; Blunt M.J.; Armstrong R.T.; Mostaghimi P.
发表日期	2021
ISSN	0012-8436
卷号	215
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.
英文关键词	Deep learning; Permeability; Pore-scale; Reconstruction; Segmentation; Super resolution
语种	英语
scopus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
来源期刊	EARTH-SCIENCE REVIEWS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/209365
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
推荐引用方式 GB/T 7714	Wang Y.D.,Blunt M.J.,Armstrong R.T.,et al. Deep learning in pore scale imaging and modeling[J],2021,215.
APA	Wang Y.D.,Blunt M.J.,Armstrong R.T.,&Mostaghimi P..(2021).Deep learning in pore scale imaging and modeling.EARTH-SCIENCE REVIEWS,215.
MLA	Wang Y.D.,et al."Deep learning in pore scale imaging and modeling".EARTH-SCIENCE REVIEWS 215(2021).