Geology Reference
In-Depth Information
a)
b)
Modelled
shale
Dry hole
2.05
Discovery 3
1.85
Discovery 2
Modelled
brine sand
1.65
Discovery 1
Modelled gas sand
Dry hole
1.45
7000
9000
AI
1st discovery well
Existing well
c)
Gas sands Pg
≈
90%
12m Brine sands Pg
≈
10%
Figure 10.2
Results from a Bayesian classification approach applied to deterministic inversion results; (a) rock model, (b) map showing
probability of gas sands occurring (high probability ¼ red) and (c) seismic section showing the location of a discovery well downdip from a
well with water sands and based on the probability prediction (after Lamont et al.,
2008
).
10.3 Reservoir properties from seismic
It is commonplace for seismic amplitudes and inverted
impedances to be transformed into reservoir properties
through the use of linear and nonlinear regression
techniques. Specific reservoir properties such as poros-
ity, net-to-gross and water saturation may be estimated,
although permeability prediction is usually based on a
relationship with porosity or facies. Mapping of reser-
voir properties can be enhanced by the use of geostatis-
tical techniques. Whilst linear regression techniques are
straightforward to implement the interpreter needs to
be aware that there may be a potential for bias in the use
of well log averages or simply as a result of inadequacies
in well sampling. In situations where the reservoir is a
single discrete body with thicknesses above and below
tuning, net pay thickness estimation may be possible.
10.3.1 Reservoir properties from
deterministic inversion
When there is adequate well control, deterministic
inversion can offer advantages over AVO analysis,
particularly in regard to the calibrated nature of
the inversion products and improved resolution (e.g.
Ross,
2010
). Techniques such as pre-stack simultan-
eous inversion (
chapter 9
) offer the possibility of
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