A Deterministic Evaluation of eismic Fidelity using Velocity Modeling ...
A Deterministic Evaluation of eismic Fidelity using Velocity Modeling ...
A Deterministic Evaluation of eismic Fidelity using Velocity Modeling ...
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in geology and depth, but more importantly, I maintained the relative position and<br />
spacing <strong>of</strong> each horizon to one another (Figure 49).<br />
Using the V z velocity gradient, I converted the horizons to depth. With the seven<br />
geologic horizons in depth, I plotted the wells with respect to the horizons to determine<br />
where the logs intersected the horizons. Logs extend to different depths so not all logs<br />
penetrated the same horizons. With points <strong>of</strong> known depth from the sonic logs, I flexed<br />
the horizons to fit the logs. I adjusted horizons I interpreted from the s<strong>eismic</strong> data to<br />
match the sonic logs keeping the relative spacing between horizons. I applied the sonic<br />
log velocities at the point <strong>of</strong> intersection between the horizon and the log. Then I<br />
calculated a sloped interval velocity between horizons by <strong>using</strong> the velocity at the<br />
intersection from the top horizon to the next horizon down. By extrapolating velocities<br />
from the wells, I built the velocity cube (Figure 50). Variations in layer thickness<br />
resulted in a nonlinear extrapolation.<br />
My initial QC <strong>of</strong> the model by comparison with known structures helped validate the<br />
model. Smoothing and extrapolation initially removed all structure from the horizons I<br />
used to build the model; yet, in the velocity cube there are faults as can be seen in Figure<br />
51. By comparing the velocity model and the PSTM s<strong>eismic</strong> volume, I confirmed that<br />
the structures were real. Even with sparse sampling <strong>of</strong> well logs, this technique was<br />
sensitive enough to include structure into the initial sediment velocity model.<br />
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