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DOI | 10.1029/2018WR023528 |
Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media | |
Mo, Shaoxing1,2; Zhu, Yinhao2; Zabaras, Nicholas2; Shi, Xiaoqing1; Wu, Jichun1 | |
2019 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:1页码:703-728 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multiphase flow problems is hindered by the curse of dimensionality, the saturation discontinuity due to capillarity effects, and the time dependence of the multi-output responses. In this paper, we propose a deep convolutional encoder-decoder neural network methodology to tackle these issues. The surrogate modeling task is transformed to an image-to-image regression strategy. This approach extracts high-level coarse features from the high-dimensional input permeability images using an encoder and then refines the coarse features to provide the output pressure/saturation images through a decoder. A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field. To characterize the high-dimensional time-dependent outputs of the dynamic system, time is treated as an additional input to the network that is trained using pairs of input realizations and of the corresponding system outputs at a limited number of time instances. The proposed method is evaluated using a geological carbon storage process-based multiphase flow model with a 2,500-dimensional stochastic permeability field. With a relatively small number of training data, the surrogate model is capable of accurately characterizing the spatiotemporal evolution of the pressure and discontinuous CO2 saturation fields and can be used efficiently to compute the statistics of the system responses. |
英文关键词 | multiphase flow geological carbon storage uncertainty quantification deep neural networks high dimensionality response discontinuity |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000459536500038 |
WOS关键词 | PROBABILISTIC COLLOCATION METHOD ; BAYESIAN EXPERIMENTAL-DESIGN ; MONTE-CARLO ; POLYNOMIAL CHAOS ; HYDRAULIC CONDUCTIVITY ; SURROGATE MODELS ; SUBSURFACE FLOW ; SPARSE GRIDS ; POROUS-MEDIA ; CO2 STORAGE |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20071 |
专题 | 资源环境科学 |
作者单位 | 1.Nanjing Univ, Sch Earth Sci & Engn, Minist Educ, Key Lab Surficial Geochem, Nanjing, Jiangsu, Peoples R China; 2.Univ Notre Dame, Ctr Informat & Computat Sci, Notre Dame, IN 46556 USA |
推荐引用方式 GB/T 7714 | Mo, Shaoxing,Zhu, Yinhao,Zabaras, Nicholas,et al. Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media[J]. WATER RESOURCES RESEARCH,2019,55(1):703-728. |
APA | Mo, Shaoxing,Zhu, Yinhao,Zabaras, Nicholas,Shi, Xiaoqing,&Wu, Jichun.(2019).Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media.WATER RESOURCES RESEARCH,55(1),703-728. |
MLA | Mo, Shaoxing,et al."Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media".WATER RESOURCES RESEARCH 55.1(2019):703-728. |
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