Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes-Reference-Cited by-同舟云学术

Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes

Published:2020-03 Issue:3 Volume:125 Page:
ISSN:2169-9313
Container-title:Journal of Geophysical Research: Solid Earth
language:en
Short-container-title:J. Geophys. Res. Solid Earth

Author:

Zhong Zhi¹²^ORCID,Sun Alexander Y.²^ORCID,Wu Xinming²³

Affiliation:

1. Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education China University of Geosciences Wuhan China

2. Bureau of Economic Geology, Jackson School of Geosciences The University of Texas at Austin Austin TX USA

3. School of Earth and Space Sciences University of Science and Technology of China Hefei China

Funder

U.S. Department of Energy

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JB018408

Reference62 articles.

1. Amini H. &MacBeth C.(2018).A Bayesian approach for resolving OWC and GOC from 4‐D seismic data. In80th EAGE Conference and Exhibition.

2. Monitoring of CO2 injected at Sleipner using time-lapse seismic data

3. Assunção G. S. Davolio A. Schiozer D. J. et al. (2016).A methodology to integrate multiple simulation models and 4‐D seismic data considering their uncertainties. InSPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.

4. Joint estimation of porosity and saturation using stochastic rock-physics modeling

5. Seismic properties of pore fluids

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