气候变化领域集成服务门户(CCPortal): Inversion of Time-Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning-Based Approach for Estimating Dynamic Reservoir Property Changes

CCPortal

DOI	10.1029/2019JB018408
	Inversion of Time-Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning-Based Approach for Estimating Dynamic Reservoir Property Changes
	Zhong Z.; Sun A.Y.; Wu X.
发表日期	2020
ISSN	21699313
卷号	125 期号:3
英文摘要	Carbon capture and storage is being pursued globally as a geoengineering measure for reducing the emission of anthropogenic (Formula presented.) into the atmosphere. Comprehensive monitoring, verification, and accounting programs must be established for demonstrating the safe storage of injected CO (Formula presented.). One of the most commonly deployed monitoring techniques is time-lapse seismic reservoir monitoring (also known as 4-D seismic), which involves comparing 3-D seismic survey data taken at the same study site but over different times. Analyses of 4-D seismic data volumes can help improve the quality of storage reservoir characterization, track the movement of injected CO (Formula presented.) plume, and identify potential CO (Formula presented.) spillover/leakage from the storage reservoirblue. However, the derivation of high-resolution CO (Formula presented.) saturation maps from 4-D seismic data is a highly nonlinear and ill-posed inverse problem, often requiring significant computational effort. In this research, we apply a physics-based deep learning method to facilitate the solution of both the forward and inverse problems in seismic inversion while honoring physical constraints. A cycle generative adversarial neural network (CycleGAN) model is trained to learn the bidirectional functional mappings between the reservoir dynamic property changes and seismic attribute changes, such that both forward and inverse solutions can be obtained efficiently from the trained model. We show that our CycleGAN-based approach not only improves the reliability of 4-D seismic inversion but also expedites the quantitative interpretation. Our deep learning-based workflow is generic and can be readily used for reservoir characterization and reservoir model updates involving the use of 4-D seismic data. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	carbon sequestration; cross-domain learning; generative adversarial neural networks; machine learning; rock physics model; seismic inversion
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187929
作者单位	Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan, China; Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, United States; School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
推荐引用方式 GB/T 7714	Zhong Z.,Sun A.Y.,Wu X.. Inversion of Time-Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning-Based Approach for Estimating Dynamic Reservoir Property Changes[J],2020,125(3).
APA	Zhong Z.,Sun A.Y.,&Wu X..(2020).Inversion of Time-Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning-Based Approach for Estimating Dynamic Reservoir Property Changes.Journal of Geophysical Research: Solid Earth,125(3).
MLA	Zhong Z.,et al."Inversion of Time-Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning-Based Approach for Estimating Dynamic Reservoir Property Changes".Journal of Geophysical Research: Solid Earth 125.3(2020).