Geoscience Reference
In-Depth Information
Distributions assigned to each term prior to running 10,000 simulations
Distributions
structure
quality
-1.00
-0.50
-0.00
0.50
1.00
-1.00
-0.50
-0.00
0.50
1.00
thin-beds
contacts
-1.00
-0.50
-0.00
0.50
1.00
-1.00
-0.50
-0.00
0.50
1.00
0.500
0.600
architecture
orientation
0.375
0.450
0.250
0.300
0.125
0.150
0.000
0.000
-1.00
-0.50
-0.00
0.50
1.00
-1.00
-0.50
-0.00
0.50
1.00
Fig. 5.18
Parameter ranges and distribution shapes for each uncertainty
in the matrix in Fig.
5.17
, in which the high case
scenario is represented by +1, the low case by
the underlying conceptual model, and requires
the definition of a parameter distribution function
(e.g. uniform, Gaussian, triangular). The distri-
bution shapes selected for each uncertainty in this
case are shown in Fig.
5.18
. For variables where
the value can be anywhere between the 1 and
1
and a mid case by 0. In this case two additional
runs were added, one using all the mid points and
one using all the low values. Neither of these two
cases is strictly necessary but can be useful to
help understand the relationship between the
uncertainties and the ultimate modelled outcome.
The 14 models were built and the resource vol-
ume (the 'response') determined for each reservoir.
A linear least-squares function was derived from
the results, capturing the relationship between the
response and the individual uncertainties. The rela-
tive impact of the individual uncertainties on the
resource volumes is captured by a co-efficient spe-
cific to the impact of each uncertainty.
The next step in the workflow is to consider
the likelihood of each uncertainty occurring in
between the defined end-member cases, that is, in
between the '1' and the '
1
end members, a uniform distribution is appropri-
ate, for those with a central tendency a normal
distribution is preferred (simplified as a triangular
distribution) and for some variables only discrete
alternative possibilities were chosen.
Once the design is set up, and assuming the
independence of the chosen variables is still
valid, the distributions can then be sampled by
standard Monte-Carlo analysis to generate a
probabilistic distribution. The existing suite of
models can then be mapped onto a probabilistic,
or S-curve, distribution (Fig.
5.19
).
There are three distinct advantages to using
this workflow. Firstly, it makes a link between
1'. This relates back to
