** Next:** : REFERENCES
** Up:** Clapp: Effect of velocity
** Previous:** Tomography

For the AVA analysis I chose the simple slope*intercept (A*B)
methodology used in Castagna et al. (1998); Gratwick (2001).
Figure 11 shows the slope (left), intercept (center),
and slope*intercept (right) for the migrated image
without model variability.
Note the positive, hydrocarbon indicating, anomalies circled at approximately 2.3 km.
**ava-none
**

Figure 11 AVA analysis for the migrated
image in Figure 7. The left panel shows the
slope, the center the intercept, and the right panel the
slope*intercept.

I then performed the same procedure on all of the migrated images
obtained from the various realizations (Figure 12).
The left panel shows
intercept, the center panel slope, the right panel,
slope*intercept. The top shows the average of
the realizations. The center panel shows
the variance of the realizations. The bottom
panel shows the variance scaled by the inverse of the
smoothed amplitude.
What is interesting is the varying behavior at
the three zones with hydrocarbon indicators.
Figure 13 shows
a closeup in the zone with the hydrocarbon
indicators. The left blob `A' shows a
high variance in the AVA indicator. The
center blob `B' shows a mild variance,
and the right blob `C' shows low variance.
This would seem to indicate that
at location `C' the hydrocarbon indicator
is more valid. Without drilling
of each target a more general
conclusion cannot be drawn.

**ava-multi
**

Figure 12 AVA analysis for the the
various velocity realizations. The left panel shows
intercept, the center panel slope, the right panel,
slope*intercept. The top shows the average of
the realizations. The center panel shows
the variance of the realizations. The bottom
panel shows the variance inverse scaled by a
smoothed amplitude.

**ava-multi-close
**

Figure 13 A close up
of the reservoir zone. The left panel shows
the slope*intercept. The right panel shows
the variance of the slope*intercept for
the various realizations. Note
how the left blob `A' shows a
high variance in the AVA indicator. The
center blob `B' shows a mild variance,
and the right blob `C' shows low variance.

Conclusions
I showed how AVA parameter variability
can be assessed by adding a random
component to our fitting goals when
estimating velocity.
The methodology shows promise in
allowing error bars to be placed
upon AVA parameter estimates.

I would like to thank Ecopetrol for the data used in this
paper.

** Next:** : REFERENCES
** Up:** Clapp: Effect of velocity
** Previous:** Tomography
Stanford Exploration Project

6/8/2002