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sampling and combination of parameters to yield

a range of results, which can be interpreted

probabilistically. If the input data can be

specified accurately, and if the combination pro-

cess maintains a realistic relationship between all

variables, the outcome may be reasonable. In

practice, however, input data is imperfectly

defined and the 'reasonableness' of the

automated combination of variables is hard to

verify. Statistical rigour is applied to data sets

which are not necessarily statistically significant

and an apparently exhaustive analysis may

have been conducted on insufficient data.

The validity of the outcome may also be

weakened by centre-weighting of the input data

to variable-by-variable best-guesses, which

creates an inevitability that the 'most likely'

probabilistic outcome will be close to the initial

best guess (Wynn and Stephens
2013
). The

geostatistical simulation itself is thus 'anchored'.

It is therefore argued that the application of

geostatistical simulation does not in itself com-

pensate for a natural tendency towards a rational-

ist best guess - it often tends to simply reflect it.

The crucial step is to select a workflow which

removes the opportunity for anchoring on a

best guess; and this is what scenario modelling,

as defined here, attempts to achieve.

the building of multiple, deterministically-driven

models of plausible development outcomes

Each scenario is a complete and internally

consistent static/dynamic subsurface realisation

with an associated plan tailored to optimise its

development. In an individual subsurface sce-

nario, there is clear linkage between technical

detail in a model, and an ultimate commercial

outcome; a change in any element of the model

prompts a quantitative change in the outcome

and the dependency between
all
parameters in

the chain (between the changed element and the

outcome) is unbroken.

This contrasts with many probabilistic

simulations, in which model design parameters

are statistically sampled and cross-multiplied,

and in which dependencies between variables

are either

lost, or collapsed into correlation

coefficients.

The scenario approach therefore places a

strong emphasis on deterministic representation

of a subsurface concept: geological, geophys-

ical, petrophysical and dynamic. Without a

clearly defined concept of the subsurface -

clear in the sense that a geoscientist could rep-

resent it as a simple sketch - the modelling

cannot progress meaningfully. We have used

the mantra:
if you can sketch it, you can model

it
. Geostatistical simulation may be a key tool

required to build an individual scenario but

the design of the scenarios is determined

directly by the modeller. Multiple models are

based on multiple, deterministic designs. This

distinguishes the workflows for scenario

modelling, as defined here, from multiple sto-

chastic modelling which tends to be based on

statistical sampling from a single initial design.

Note that multiple stochastic modelling is a

powerful tool for understanding reservoir

model ranges and outcomes; it is simply not

sufficient to fully explore subsurface uncer-

tainties from poorly sampled reservoirs.

Scenario-based approaches place an emphasis

on listing and ranking of uncertainties, from

which a suite of scenarios will be built, with no

attempt being made to select a best guess case

up-front.

5.4

Scenarios Defined

The definition of 'scenario' adopted here follows

that described by van der Heijden (
1996
), who

discussed the use of scenarios in the context of

corporate strategic planning and defined

scenarios as
a set of reasonably plausible, but

structurally different futures
.

Alternative scenarios are not incrementally

different models based on slight changes in con-

tinuous input data (as with multiple probabilistic

models), but models which are
structurally
dis-

tinct based on some defined design criteria.

Translated to oil and gas field development, a

'scenario' is therefore a plausible development

outcome, and the 'scenario-based approach' to

reservoir modelling is defined as: