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| Titel |
Fluvial reservoir characterization using topological descriptors based on spectral analysis of graphs |
| VerfasserIn |
Sophie Viseur, Christophe Chiaberge, Jérémy Rhomer, Pascal Audigane |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250108064
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| Publikation (Nr.) |
 EGU/EGU2015-7795.pdf |
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| Zusammenfassung |
| Fluvial systems generate highly heterogeneous reservoir. These heterogeneities have major
impact on fluid flow behaviors. However, the modelling of such reservoirs is mainly
performed in under-constrained contexts as they include complex features, though only
sparse and indirect data are available.
Stochastic modeling is the common strategy to solve such problems. Multiple 3D
models are generated from the available subsurface dataset. The generated models
represent a sampling of plausible subsurface structure representations. From this
model sampling, statistical analysis on targeted parameters (e.g.: reserve estimations,
flow behaviors, etc.) and a posteriori uncertainties are performed to assess risks.
However, on one hand, uncertainties may be huge, which requires many models
to be generated for scanning the space of possibilities. On the other hand, some
computations performed on the generated models are time consuming and cannot, in
practice, be applied on all of them. This issue is particularly critical in: 1) geological
modeling from outcrop data only, as these data types are generally sparse and mainly
distributed in 2D at large scale but they may locally include high-resolution descriptions
(e.g.: facies, strata local variability, etc.); 2) CO2 storage studies as many scales of
investigations are required, from meter to regional ones, to estimate storage capacities and
associated risks. Recent approaches propose to define distances between models to allow
sophisticated multivariate statistics to be applied on the space of uncertainties so that only
sub-samples, representative of initial set, are investigated for dynamic time-consuming
studies.
This work focuses on defining distances between models that characterize the topology of
the reservoir rock network, i.e. its compactness or connectivity degree. The proposed strategy
relies on the study of the reservoir rock skeleton. The skeleton of an object corresponds to its
median feature. A skeleton is computed for each reservoir rock geobody and studied through
a graph spectral analysis. To achieve this, the skeleton is converted into a graph structure. The
spectral analysis applied on this graph structure allows a distance to be defined between pairs
of graphs. Therefore, this distance is used as support for clustering analysis to gather
models that share the same reservoir rock topology. To show the ability of the defined
distances to discriminate different types of reservoir connectivity, a synthetic data set of
fluvial models with different geological settings was generated and studied using the
proposed approach. The results of the clustering analysis are shown and discussed. |
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