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- Title
Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes.
- Authors
Zhong, Zhi; Sun, Alexander Y.; Wu, Xinming
- Abstract
Carbon capture and storage is being pursued globally as a geoengineering measure for reducing the emission of anthropogenic CO2 into the atmosphere. Comprehensive monitoring, verification, and accounting programs must be established for demonstrating the safe storage of injected CO 2. 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 2 plume, and identify potential CO 2 spillover/leakage from the storage reservoirblue. However, the derivation of high‐resolution CO 2 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. Key Points: We develop a cycle generative adversarial neural network (CycleGAN) model for time lapse seismic data inversion in carbon storage reservoirsCycleGAN combines reservoir simulation and rock physics inversion to invert high‐resolution maps of CO 2 saturation from acoustic impedanceWe show that CycleGAN can successfully identify the bidirectional mappings between CO 2 saturation changes and acoustic impedance changes
- Subjects
CARBON sequestration; ENVIRONMENTAL engineering; ANTHROPOGENIC effects on nature; ACOUSTIC impedance; DEEP learning
- Publication
Journal of Geophysical Research. Solid Earth, 2020, Vol 125, Issue 3, p1
- ISSN
2169-9313
- Publication type
Article
- DOI
10.1029/2019JB018408