Abstract
Stationarity of the random function is the key property of stochastic models to the extent that wrong hypotheses could lead to very unrealistic reservoir modeling for flow simulations.
When non stationarity is suspected at the scale of the domain to be modeled, geologists usually propose either geological drift or seismic attributes to constrain stochastic models.
However, in both cases, confidence in the soft data is limited by the rough aspect of the trends from geological input, the scale problem, and the quality of correlations between the random function and the variables from seismic data.
This paper presents an original approach for building complex 3D prior probability fields of geological facies. It constitutes a very challenging way of integrating two major advances in geosciences in recent years: geostatistics and sequence stratigraphy.
From facies descriptions on cores and logs, palaeobathymetry curves of deposits are constructed for each well. Logs of the accommodation potential (increment of space available for sediment accumulation) are then produced from those curves and deposit thicknesses in the wells.
A Principal Components Analysis of data in all wells makes it possible to separate the signal into two components: a common factor which represents the tectono-eustatic activity at field scale (easily extrapolated), and residuals which correspond to local variations in subsidence. 3D grids of accommodation potential and palaeobathymetry can then be modeled, with respect to the time intervals within each layer.
Geological inversion is therefore possible and leads to the proposal of a complex 3D prior probability field for facies modeling, with different organizations in both the transgressive and the prograding parts of the sequence.
When constrained by such trends, stochastic modeling (object based or SIS) can render very realistic images of reservoir heterogeneity.
This method was applied successfully in carbonate and mixed (silici-clastic and carbonate) platform reservoirs, in which properties are highly variable and non stationary.
