Geology Reference
In-Depth Information
a)
b)
200
200
180
180
160
160
140
140
120
120
100
100
80
80
60
60
40
0.1
40
0.1
1.0
10.0
1.0
10.0
Res shallow (ohmm)
Res deep (ohmm)
Figure 8.14
Resistivity-sonic crossplots from 1000 ft of Miocene sand/shale section onshore Texas (after Smith,
2007
) showing lithologic
differences between sonic and shallow SFL (a) and deep ILD (b) resistivity logs. Circles represent sands and crosses represent shales.
Republished by permission of the Gulf Coast Association of Geological Societies, whose permission is required for further publication use.
0
:
5
The effect of depth needs to be carefully con-
sidered when applying resistivity
R
r
R
t
¼
t
0
+c
,
ð
8
:
17
Þ
sonic transforms.
Burch (
2002
) describes an approach that utilises avail-
able sonic data in an area to estimate the first order
depth trend, which is then combined with the scaled
and filtered resistivity data to generate pseudo-sonic
curves in wells without sonic curves.
Other workers have approached the issue of sonic
log prediction from resistivity from the perspective of
combining resistivity and velocity models. For example,
Dos Santos et al.(
1988
) utilised the resistivity
-
where c is a constant dependent on lithology and R
r
is
the resistivity of water for a water-bearing sand/shale
mixture:
1
¼
+
:
ð
:
Þ
R
r
8
18
V
sh
ð
1
V
sh
Þ
R
w
R
w
shale
sand
It assumes that R
w
is constant and the resistivity is
measuring water-filled rocks. Hacikoylu et al.(
2006
)
concluded that Faust
-
porosity
model of Bussian (
1983
)andWy lieet al.
'
s(
1958
)
s relation was appropriate
mainly for consolidated rocks. For unconsolidated
rocks they propose a relation that includes the Archie
formation factor:
'
equation to generate a sonic
resistivity transform. The
practical limitation of this approach is that Wyllie
-
s
equation does not account for variations in pore geom-
etry, for example associated with the variation of shale
content in sandstones (e.g. Xu and White,
1995
).
A consideration of these issues led to the development
of the Xu
'
F
¼
ð
:
Þ
V
p
,
8
19
ð
0
:
9+cF
Þ
-
White model described in
Section 8.2.7.
where c is a coefficient which ranges from 0.27 to 0.32.
R
w
is a critical factor in these types of resistivity
-
sonic transforms. Prediction of R
w
requires knowledge
of water salinity and temperature variations with
depth. It can be directly obtained from laboratory
measurements on formation water samples or from
spontaneous potential logs which measure at least one
clean and permeable zone.
8.2.3 Gassmann
'
sequation
A key concept in interpreting seismic amplitudes is an
understanding of how rock properties are affected by
a change in fluid fill, for example from brine to
hydrocarbons (e.g. Smith et al.,
2003
). The calculation
is straightforward for density, where the rock density
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