Geoscience Reference
In-Depth Information
uncertainty of that factor with discrete cases,
rather than simply input a data distribution for a
higher level parameter such as net-to-gross.
5.5
The Uncertainty List
The key to success in scenario modelling lies in
deriving an appropriate list of key uncertainties,
a matter of experience and judgement. However,
there is a strong tendency to conceptualise key
uncertainties for at least the static reservoir
models in terms of the parameters of the STOIIP
equation
, i.e
. when asked to define the key
uncertainties in the field, modellers will often
quote parameters such as 'porosity' or 'net
sand' as key factors. If the model-build
progresses with these as the key uncertainties to
alter, this will most likely be represented as a
range for a continuous variable, anchored around
a best guess.
A better approach is to question why 'poros-
ity' or 'net sand count' are considered significant
uncertainties. It will either emerge that the uncer-
tainty is not that significant or, if it is, then it
relates to some underlying root cause, such as
heterogeneous diagenesis, or some local facies
control which has not been extracted from the
data analysis.
For example, in Fig.
5.10
a PDF of net-to-
gross is shown. A superficial approach to model
uncertainty would involve taking the PDF, input-
ting it to a geostatistical algorithm and allowing
sampling of the spread to account for the uncer-
tainty. As the data in the figure illustrates, this
would be misleading, because the range is
reflecting mixed geological concepts. The real
need is to understand the facies distribution, and
isolate the facies-based factors (in this case the
proportion of different channel types), and then
establish whether this ratio is known within rea-
sonable bounds. If not known, the uncertainty
can be represented by building contrasting, but
realistic, depositional models (the basis for two
scenarios) in which these elements are specifi-
cally contrasted. The uncertainty in the net-to-
gross parameter within each scenario is a second-
order issue to the geological uncertainty.
In defining key uncertainties, the need is
therefore to chase the source of the uncertainty
to the underlying causative factor - 'root cause
analysis' - and model the conceptual range of
5.6
Applications
5.6.1 Greenfield Case
The application of scenario modelling has been
most successfully reported for cases involving
new or 'greenfield' reservoir studies.
One of the first published examples was that
of van de Leemput et al. (
1996
), who described
an application of scenario-based modelling in the
context of an LNG field development. Once suf-
ficient proven volumes were established to sup-
port the scheme, the commercial structure of the
project focussed attention on the issue of
the associated capital expenditure (CAPEX).
CAPEX therefore became the prime quantitative
outcome of the modelling exercise, driven
largely by well numbers and the requirements
for, and timing of, gas compression facilities.
The model scenarios were driven by a
selection of principal uncertainties, summarised
in Fig.
5.11
. Six static and five dynamic
uncertainties were drawn up, based on the judge-
ment of the project team and input from peers.
Maintaining the uncertainty list became a
continuing process, iterating with new well data
from appraisal drilling, and the changing views
of the group.
For the field development plan itself, the
uncertainty list generated 22 discrete scenarios,
each of which was matched to the small amount
of production data, then individually tailored to
optimise the development outcome over the life
of the LNG scheme. The outcomes, in term of
impact on project cost (CAPEX), are shown in
Fig.
5.11
.
A key learning outcome from this exercise
was that a list of 11 uncertainties was unneces-
sarily long to generate the ultimate result,
although convenient for satisfying concerns of
stakeholders. The effect of statistical dominance
meant
that
the range was not driven by all
