JOURNAL ARTICLE

Joint full-waveform inversion for time-lapse 4D seismic monitoring

Wei ZhouDavid LumleyXu Liu

Year: 2025 Journal:   Geophysics Vol: 90 (3)Pages: R57-R70   Publisher: Society of Exploration Geophysicists

Abstract

ABSTRACT Full-waveform inversion (FWI) can be applied to time-lapse (4D) seismic data for subsurface reservoir monitoring. Usually, baseline and monitor velocity models are built through a data-fitting process, and the difference between the models is used to estimate the time-lapse changes. However, the large-scale (or low-wavenumber) velocity structures of deep reservoirs are mainly obtained by fitting diving waves, which can be unavailable due to limited source-receiver offset apertures. Alternatively, joint FWI (JFWI) assumes a scale separation in the model space such that low wavenumbers can be recovered from diving and reflected waves, in addition to high-wavenumber impedance perturbations. We extend JFWI to time-lapse data and improve the sensitivity of waveform inversion approaches to low-wavenumber velocity changes in deep targets. A central-difference strategy (CD JFWI) is also implemented, which averages four JFWI velocity models (two baseline and two monitor models) to attenuate false changes in the 4D velocity estimate and increases the stability of the approach in data fitting. The synthetic 4D SEAM case study indicates that CD JFWI followed by 4D FWI leads to a more accurate estimate and better resolution of velocity changes than conventional 4D FWI methods in a reservoir zone that is not penetrated by diving waves.

Keywords:
Joint (building) Geology Inversion (geology) Waveform Seismology Geodesy Computer science Telecommunications Engineering Structural engineering

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Topics

Seismic Imaging and Inversion Techniques
Physical Sciences →  Earth and Planetary Sciences →  Geophysics
Seismic Waves and Analysis
Physical Sciences →  Earth and Planetary Sciences →  Geophysics
Seismology and Earthquake Studies
Physical Sciences →  Computer Science →  Artificial Intelligence

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