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In-Depth Information
Moreover, having conducted an experimental
design, it may emerge that the P50 outcome is
significantly different from the previously
assumed initial 'best guess.' That is, this uncer-
tainty modelling approach can help compensate
for the biases that the user, or subsurface team,
started with.
1.00
0.75
0.50
0.25
0.00
1500
1600
1700
1800
1900
percentiles
￿
P90 1614, P50 1693, P10 1785 bcf
5.10
Scenarios and
Uncertainty-Handling
￿
P99 1503 and P1 1900 bcf
G IIP main
1500
1900
Scenario-based approaches offer an improvement
over base-case modelling, as results from the lat-
ter are anchored around best guess assumptions.
Best guesses are invariably misleading because
data from the subsurface is generally insufficient
to be directly predictive. Scenarios are defined
here as 'multiple, deterministically-driven models
of plausible development outcomes', and are pre-
ferred to multiple stochastic modelling alone, the
application of which is limited by the same data
insufficiency which limits base case modelling.
Each scenario is a plausible development future
based on a specific concept of the subsurface, the
development planning response to which can be
optimised.
The application of geostatistical techniques, and
conditional simulation algorithms in particular, is
wholly supported as a means of completing a real-
istic subsurface model - usually by infilling a
strongly deterministic model framework. Multiple
stochastic modelling can also be useful to explore
sensitivities around an individual deterministic sce-
nario. Deterministic design of each over-arching
scenario, however, is preferred because of transpar-
ency, relative simplicity and because each scenario
can be validated as a realistic subsurface outcome.
Scenario-based modelling is readily applica-
ble to greenfield sites but, as the examples shown
here confirm, is also practical for mature, brown-
field sites, where multiple history matching may
be required at the simulation stage.
The key to success is the formulation of the
uncertainty list. If the issues which could cause
the business decision to fail are identified, then
the modelling workflow will capture this and the
decision risk can be mitigated. If the issue is
structure
quality
contacts
architecture
thin-beds
orientation
Fig. 5.19 Probabilistic volumes from Monte Carlo sim-
ulation of the experimental design formulation
probabilistic reporting and discrete multiple-
deterministic models. This can be used to pro-
vide a rationale for selecting models for simula-
tion. For example, P90, P50 and P10 models can
be identified from this analysis and it may
emerge that models reasonably close to these
probability thresholds were built as part of the
initial experimental design. Alternatively, the
comparison may show that new models need to
be built. This is easier to do now that the impact
of the different uncertainties has been quantified,
and is an improvement on an arbitrary assump-
tion that a high case model, for example,
represents the P10 case. Secondly, the workflow
focuses on the end-members and on capturing the
range of input variables, avoiding the need to
anchor erroneously on a best guess. Finally, the
approach provides a way of quantifying the
impact of the different uncertainties via tornado
diagrams or simple spider plots, which can in turn
be used to steer further data gathering in a field.
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