A framework for history matching - StreamSim Technologies, Inc.

Mepo a framework for history matching
Mepo is an invaluable tool and framework
for the reservoir engineer performing history
matching (HM). The benefits offered by Mepo
are explained below.
I. A match will be found
A simulation model with a poor match can give misleading
results. Planning new wells with a low risk of missing the
reservoir is of paramount importance. In addition, a well
matched model will be reliable to the extent that it can be
used in decline analysis and other predictions of future field
performance.
Mepo optimises model parameter sets until convergence
is reached, i.e. until a match is obtained. Even in cases in
which convergence is not reached, Mepo offers concepts
and methods to investigate the solution space and to provide
information on changing the HM strategy. Implementing
Mepo in the HM-workflow will define a guideline and
facilitate the process to get a match.
II. Assessing the viability of the simulation model
In order to increase the acceptance of results from the
simulation model, the uncertainty of any acceptable HM
result should be quantified. When a model reproduces all
history properly, the HM is considered acceptable. However,
due to the nature of the problem, there are a number of
acceptable solutions. Each solution might generate different
predictions. Mepo investigates the model diversity, which
can give insights into the model uncertainty.
Mepo includes global optimisation methods, which have
the potential to identify various good matches. A number
of acceptable matches can be used for prediction runs.
Differences in the predicted results reflect and quantify the
model uncertainty. This approach goes far beyond the linear
perturbation of parameter values based on one acceptable
match. The method adds value by quantifying uncertainty
and increasing reliability on the reservoir model used for
reservoir predictions.
Mepo supports the identification of several acceptable matches
to assess the uncertainty of the simulation model.
Several simulations fall within an acceptable uncertainty range
in the history period, but the predictions from the same models
define an uncertainty in the simulated production forecast.