Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States). Theoretical Division; Colorado School of Mines, Golden, CO (United States)
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States). Earth and Environmental Sciences Division
- Colorado School of Mines, Golden, CO (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Earth and Environmental Sciences Division
In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction accuracy, which hinders their application to time-lapse monitoring of CO2 sequestration. We propose an efficient “hybrid” time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is vali dated on a synthetic CO2-sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO2 movement in the reservoir. In conclusion, the developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Fossil Energy and Carbon Management (FECM)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 2246873
- Journal Information:
- Geophysics, Journal Name: Geophysics Journal Issue: 1 Vol. 88; ISSN 0016-8033
- Publisher:
- Society of Exploration GeophysicistsCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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