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