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are built, each one reflecting a complete
real-world outcome following an explicitly-
defined reservoir concept. Geostatistical sim-
ulation may be applied in the building of the
3D model but the
selection
of the model
realisations is made manually (or mathemati-
cally) rather than by statistical simulation
(e.g. van de Leemput et al.
1996
).
Each of the above approaches have been
referred to as 'scenario modelling' by different
reservoir modellers. The argument we develop
here is that although all three approaches have
some application in subsurface modelling,
multiple-deterministic scenario-building is the
preferred route in most circumstances.
In order to make this case, we need to recall
the underlying philosophy of uncertainty
handling and give a definition for 'scenario
modelling'.
It is often stated that for mature fields, a
simple, rationalist approach may suffice because
uncertainty has reduced through the field life
cycle. This is a fallacy. Although, the
magnitude
of the initial development uncertainties tends to
decrease with time, we generally find that as the
field life cycle progresses new, more subtle,
uncertainties arise and these now drive the deci-
sion making. For example, in the landscape
image in Fig.
5.5
, 100 samples would signifi-
cantly improve the ability to describe the
image, but this is still insufficient to specify the
location of an unsampled house. The impact of
uncertainties in terms of their ability to erode
value may, in fact, be as great near the end of
the field life as at the beginning.
Despite this, rationalist, base-case modelling
remains common across the industry. In a review
of 90 modelling studies conducted by the authors
and colleagues across many companies, field
modelling was based on a single, best-guess
model in 36 % of the cases (Smith et al.
2005
).
This was the case, despite a bias in the sampling
from the authors' own studies, which tended to
be scenario-based. Excluding the cases where the
model design was made by the authors, the pro-
portion of base case-only models rose to 60 %.
5.3
Anchoring
5.3.1 The Limits of Rationalism
The rationalist approach, described above as the
'best-guess' method, is effectively simple
forecasting - and puts faith in the ability of an
individual or team to make a reasonably precise
judgement. If presented as the best judgement
of a group of professional experts then this
appears reasonable. The weak point is that the
best guess is only reliable when the system
being described is well ordered and well under-
stood, to the point of being highly predictable
(Mintzberg
1990
). It must be assumed that
enough data is available from past activities to
predict a future outcome with confidence, and
this applies equally to production forecasting,
exploration risking, volumetrics or well
prognoses. Although this is rarely the case in
the subsurface, except perhaps for fields with a
large (100+) number of regularly spaced wells,
there is a strong tendency for individuals or
teams (or managers) to desire a best guess,
and to subsequently place too much confidence
in that guess (Baddeley et al.
2004
).
5.3.2 Anchoring and the Limits
of Geostatistics
The process of selecting a best guess in spite of
wide uncertainty is referred to as 'anchoring',
and is a well-understood cognitive behaviour
(Kahneman and Tverky
1974
). Once anchored,
the adjustment away from the initial best guess is
too limited as the outcome is overly influenced
by the anchor point.
This often also occurs in statistical approaches
to uncertainty handling, as these tend to be
anchored in the available data and may therefore
make the same rational starting assumption as the
simple forecast, although adding ranges around a
'most probable' prediction (see examples in
Chellingsworth et al.
2011
).
Geostatistical simulation allows definition
of ranges for variables, followed by rigorous
