Multiresolution Approach to Condition Categorical Multiple-Point Realizations to Dynamic Data With Iterative Ensemble Smoothing
Dan-Thuy Lam, Philippe Renard & Jaouhar Kerrou
| Résumé |
A new methodology is presented for the conditioning of categorical
multiple-point statistics (MPS) simulations to dynamic data with an
iterative ensemble smoother (ES-MDA). The methodology relies on a
novel multiresolution parameterization of the categorical MPS
simulation. The ensemble of latent parameters is initially defined
on the basis of the coarsest-resolution simulations of an ensemble
of multiresolution MPS simulations. Because this ensemble is
non-multi-Gaussian, additional steps prior to the computation of
the first update are proposed. In particular, the parameters are
updated at predefined locations at the coarsest scale and
integrated as hard data to generate a new multiresolution MPS
simulation. The performance of the methodology was assessed on a
synthetic groundwater flow problem inspired from a real situation.
The results illustrate that the method converges towards a set of
final categorical realizations that are consistent with the initial
categorical ensemble. The convergence is reliable in the sense that
it is fully controlled by the integration of the ES-MDA update into
the new conditional multiresolution MPS simulations. Thanks to a
massively reduced number of parameters compared to the size of the
categorical simulation, the identification of the geological
structures during the data assimilation is particularly efficient
for this example. The comparison between the estimated uncertainty
and a reference estimate obtained with a Monte Carlo method shows
that the uncertainty is not severely reduced during the
assimilation as is often the case. The connectivity is successfully
reproduced during the iterative procedure despite the rather large
distance between the observation points. |
| Mots-clés |
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| Citation | Lam, D. T., Renard, P., & Kerrou, J. (2023). Multiresolution Approach to Condition Categorical Multiple-Point Realizations to Dynamic Data With Iterative Ensemble Smoothing. Water Resources Research, 56(2), 25875-25904. |
| Type | Article de périodique (Anglais) |
| Date de publication | 19-1-2023 |
| Nom du périodique | Water Resources Research |
| Volume | 56 |
| Numéro | 2 |
| Pages | 25875-25904 |
| URL | https://doi.org/10.1029/2019WR025875 |