Non-repeatability effects on time-lapse 4D seismic full-waveform inversion

2020 ◽  
Author(s):  
Wei Zhou ◽  
David Lumley
Keyword(s):  
Waveform Inversion ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
4D Seismic

Non-repeatability Effects on Time-Lapse 4D Seismic Full Waveform Inversion for Ocean-Bottom Node Data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Wei Zhou ◽  
David Lumley
Keyword(s):  
Waveform Inversion ◽  
Time Lapse ◽  
Seismic Survey ◽  
Double Difference ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Central Difference ◽  
Change Models ◽  
Full Waveform ◽  
4D Seismic

Full waveform inversion (FWI) can be applied to time-lapse (4D) seismic data for subsurface reservoir monitoring. However, non-repeatability (NR) issues can contaminate the data and cause artifacts in the estimation of 4D rock and fluid property changes. Therefore, evaluating and studying the NR effects on the 4D data and FWI results can help, for instance, discriminate inversion artifacts from true changes, guide seismic survey design and processing workflow. Using realistic reservoir models, data and field measurements of NR, we show the effects of NR source-receiver position and seawater velocity changes on the data and the 4D FWI results. We find that ignoring these NR effects in the inversion can cause strong artifacts in the estimated velocity change models, and thus should be addressed before or during inversion. The NR source-receiver positioning issue can be addressed by 4D FWI successfully, whereas the NR water velocity issue requires measurements or estimations of water velocities. Furthermore, we compare the accuracy and robustness of the parallel, double-difference and central-difference 4D FWI methods to realistic NR ocean-bottom node data in a quantitative way. Parallel 4D FWI fails to capture geomechanical changes and also overestimates the aquifer layer changes with NR data. Double-difference 4D FWI is capable of recovering the geomechanical changes, but is also sensitive to NR noises, generating more artifacts in the overburden. By averaging the forward and reverse bootstrap 4D estimates, central-difference 4D FWI is more robust to NR noises, and also produces the most accurate 4D estimates.

Time-lapse full waveform inversion based on curvelet transform: Case study of CO2 storage monitoring

2021 ◽  
Vol 110 ◽  
pp. 103417
Author(s):  
Dong Li ◽  
Suping Peng ◽  
Xingguo Huang ◽  
Yinling Guo ◽  
Yongxu Lu ◽  
...  
Keyword(s):  
Co2 Storage ◽  
Waveform Inversion ◽  
Curvelet Transform ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Full Waveform

Multiscale time-domain time-lapse full-waveform inversion with a model-difference regularization

2017 ◽  
Author(s):  
Musa Maharramov ◽  
Ganglin Chen ◽  
Partha S. Routh ◽  
Anatoly I. Baumstein ◽  
Sunwoong Lee ◽  
...  
Keyword(s):  
Time Domain ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Full Waveform
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