Premium
Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes
Author(s) -
Zhong Zhi,
Sun Alexander Y.,
Wu Xinming
Publication year - 2020
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2019jb018408
Subject(s) - inversion (geology) , reservoir modeling , computer science , seismic inversion , inverse problem , artificial neural network , deep learning , seismic to simulation , algorithm , seismology , geology , artificial intelligence , petroleum engineering , data assimilation , meteorology , mathematical analysis , physics , mathematics , tectonics
Carbon capture and storage is being pursued globally as a geoengineering measure for reducing the emission of anthropogenic C O 2into the atmosphere. Comprehensive monitoring, verification, and accounting programs must be established for demonstrating the safe storage of injected CO2 . 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 CO2plume, and identify potential CO2spillover/leakage from the storage reservoirblue. However, the derivation of high‐resolution CO2saturation 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.