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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:
