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It is often assumed that velocities associated with good quality seismic images can be directly used for time-todepth conversion, pore-pressure prediction or even as a driver for seismic amplitude interpretation. However, such applications require seismic velocities to be properly integrated with velocities derived from well-data using relevant geological insights to increase the usefulness of this type of geophysical data. Forward modelling can help to manage the different scales involved and to assess consistency between different velocity data sources. Seismic velocities, irrespective of the method used to obtain them, should be compared, and calibrated to welldata. Well-based velocities include edited VSP data; time-depth tables derived over intervals with a quality synthetic/seismic tie; and rock physics depth trends derived from sonic and other wireline logs. Geological insight is obtained from inspection of velocity trends; use of well markers; stratigraphic mapping; and basin modelling insights. Integration software can help by converting between common velocity types and by deriving regional burial trends and depth-dependent calibrations between velocity data from different sources. An example from the Campos Basin, Brazil, shows how well data alone can underpin time-to-depth conversion to reveal hidden structures below shelf-slope breaks and salt diapirs, while also addressing the interpretation of amplitudes for prospect derisking. In the Browse Basin, Australia, velocities derived from well-data can help to QC processing velocities. Comparing seismic and wellbased velocities then helps to identify possible overpressured zones. More efficient and sustainable exploration could result from treating velocities as a valuable geophysical dataset requiring interpretation, and not just a by-product of seismic processing.
Open-Access Online Publication: March 01, 2023
seismic velocities, AvO, integration., pore-pressure prediction, quantitative interpretation
seismic velocities, AvO, integration., pore-pressure prediction, quantitative interpretation
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