Geoscience Reference
In-Depth Information
Fig. 3.24
Property model examples from the Lajas tidal
delta outcrop model
realisation assuming a long correlation length 500 m
horizontally and 5 m vertically.
Yellow
and
red
indicate
high permeability values while
blue
and
purple
indicate
low permeability values
(Brandsæter et al.
2005
):
(
a
)
Modelled
) Permeability
realisation assuming a short correlation length of 50 m
horizontally and 0.5 m vertically;
tidal
channel
objects;
(
b
(
c
) Permeability
3.4.6 Property Modelling:
Seismic-Based Workflow
information about the reservoir properties -
such as porosity or the spatial distribution of
high porosity sandstones.
There are numerous recipes available for
obtaining reservoir properties from seismic data
(e.g. Doyen
2007
). These are all based on the
underlying theory of seismology in which
reflected or refracted seismic waves are con-
trolled by changes in the density (
Seismic imaging has made enormous leaps and
bounds in the lasts decades - from simple detec-
tion of rock layers that might contain oil or gas
to 3D imaging of reservoir units that almost
certainly do contain oil and gas (using direct
hydrocarbon indicators). In this topic we have
assumed that seismic imaging is always avail-
able in some form to define the reservoir con-
tainer (e.g. the top reservoir surface and
bounding faults). Here we are concerned with
the potential for using seismic data to obtain
) and velocity
(V
P
,V
S
) of rock formations. More specifically,
seismic imaging is controlled by the acoustic
impedance, AI
ˁ
V
P
(for a compressional
wave). Zoeppritz, in 1919, determined the set of
equations which control
¼
ˁ
the partitioning of
