Annual Report 2000 - WIT

106
PreSDM of Porous layer
0.358
receiver offset
0.92 1.42 1.92
1
0.3
0.25
AVO for porous Fault
Analytically calculated
Migration result
0.42
0.2
35 % Porosity
1 1.5 2 2.5
Depth [km]
|Rpp|
0.15
0.1
0.05
25 % Porosity
0.1 0.2 0.3 0.4 0.5 0.6 0.7
Distance [km]
0
0
0 0.5 1 1.5
offset [km]
Figure 9: (a) Migrated image of the fault with porosity variations. The pulses of opposite
polarity from both reflectors interfere to a high positive lobe in the middle. Along
the offset axis, a strong pulse stretch due to high incidence angles can be observed. (b)
Comparison of picked amplitudes with analytical values for two image gathers.
culated values at two depth locations on the fault that correspond to 25 % and 35 %
porosity, respectively. The variation in porosity is manifested mainly in the zero-offset
reflection coefficient and the slope for near offsets. The reduction in P–wave velocity
governs the zero-offset reflection coefficient, whereas the reduction in S–wave velocity,
and respectively the Poisson ratio, determines the slope of the AVO curve. Differences
between migrated and analytically calculated amplitudes for large offsets are due
to the smoothing of the background velocity model. Note, however, that the relative
difference in slopes for different porosities remains constant.
We are thus able to map the porosity variation along the dip of the fault for the
whole target zone. This is depicted in Figure 10 where AVO curves for every depth
are plotted as a surface. The decreasing porosity with depth can be drawn from the
decreasing zero-offset reflection coefficient on the left side. Different slopes along
the AVO direction stem from the variation of Poisson's ratio, i.e., the ratio of P–wave
velocity to S–wave velocity.
CONCLUSION
True-amplitude Kirchhoff–type migration and demigration provides a fast and efficient
tool for modeling and imaging of small time–dependent variations within a reservoir.
As the two integrals represent an asymptotically inverse transformation pair, data can
be regularized or transformed to other survey configurations without loss of dynamic
information. In this sense, our algorithm provides a method to improve input data
as well as to fine-tune reservoir models by subsequent switching from time to depth