2016_EJRNL_PP_ARTHUR_THENON_1.pdf
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Defining representative reservoir models usually
calls for a huge number of fluid flow simulations, which
may be very time-consuming. Meta-models are built to
lessen this issue. They approximate a scalar function from
the values simulated for a set of uncertain parameters. For
time-dependent outputs, a reduced-basis approach can be
considered. If the resulting meta-models are accurate, they
can be called instead of the flow simulator. We propose
here to investigate a specific approach named multi-fidelity
meta-modeling to reduce further the simulation time. We
assume that the outputs of interest are known at various levels of resolution: a fine reference level, and coarser levels
for which computations are faster but less accurate. Multifidelity meta-models refer to co-kriging to approximate the
outputs at the fine level using the values simulated at all
levels. Such an approach can save simulation time by limiting the number of fine level simulations. The objective of
this paper is to investigate the potential of multi-fidelity for
reservoir engineering. The reduced-basis approach for timedependent outputs is extended to the multi-fidelity context.
Then, comparisons with the more usual kriging approach
are proposed on a synthetic case, both in terms of computation time and predictivity. Meta-models are computed to
evaluate the production responses at wells and the mismatch
between the data and the simulated responses (history
matching error), considering two levels of resolution. The results show that the multi-fidelity approach can outperform kriging if the target simulation time is small. Last, its
potential is evidenced when used for history matching.