realisations is fed through directly to two quan-
titative scenarios, which allow the significance
of the uncertainty to be evaluated.
Transparency: although the models may be inter-
nally complex, the workflow is simple, and
feeds directly off the uncertainty list, which
may be no more complex than a short list of
the key issues which drive the decision at hand.
If the key issues which could cause a project to
fail are identified on that list, the model process
will evaluate the outcome in the result range.
The focus is therefore not on the intricacies of
the model build (which can be reviewed by an
expert, as required), but on the uncertainty list,
which is transparent to all interested parties.
achieved with a smaller number of scenarios, but
the full set was run simply because it was not
particularly time-consuming (the whole study ran
over roughly 5 man weeks, including static and
dynamic modelling). The case illustrates
the efficacy of multiple static/dynamic modelling
in greenfields, even when the compilation of runs
is manual. Figure 5.14b shows the results of a more
recent study (Chellingsworth, et al. 2011 )in
which 124 STOIIP-related cases were efficiently
analysed using a workflow-manager algorithm.
This issue is more pressing for brownfield sites,
although the cases described above from the Sirikit
and Gannet fields illustrate that workflows for
multiple model handling in mature fields can be
practical. This challenge is also being improved
further by the emergence of a new breed of auto-
matic history matching tools which achieve model
results according to input guidelines which can be
It is thus suggested that the running of multi-
ple models is not a barrier to scenario modelling,
even in fields with long production histories.
Once the conceptual scenarios have been clearly
defined, it often emerges that complex models
are not required. Fit-for-purpose models also
come with a significant time-saving.
Cross-company reviews by the authors
indicate that model-building exercises which
are particularly lengthy are typically those
where a very large, detailed, base-case model is
under construction. History matching is often
pursued to a level of precision disproportionate
to the accuracy of the static reservoir model it
is based on. By contrast, multiple modelling
Multiple Model Handling
It is generally assumed that more effort will be
required to manage multiple models than a single
model, particularly when brownfield sites require
multiple history matching. However, this is not
necessarily the case - it all comes down to a
choice of workflow.
Multiple model handling in greenfield sites
is not necessarily a time-consuming process.
Figure 5.14a illustrates results from a study
involving discrete development scenarios.
These were manually constructed from
permutations of 6 underlying static models and
dynamic uncertainties in fluid distribution and
composition. This was an exhaustive approach
in which all combinations of key uncertainties
were assessed. The final result could have been
124 static & dynamic