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Geostatistics is often used to build multiple models of reservoir geological heterogeneity for the probabilistic assessment of reservoir flow response. Current geostatistical algorithms, object-based or pixel-based, using semivariograms or training images, enable the reproduction of spatial statistics inferred from available conditioning data and analogs but rarely integrate information related to depositional processes. Indeed, because conventional geostatistical models are constructed without any concept of time or depositional sequence, their ability to incorporate sedimentological rules, which explain facies geobodies interactions and intra-body porosity/permeability heterogeneity, is quite limited. One consequence of such a limitation is that, unless spatial constraints tediously derived from alternative depositional interpretations are explicitly imposed to the simulation, conventional geostatistical methods only generate stationary statistical models that may not be representative of the full range of actual reservoir heterogeneity uncertainty.

Recently, the event-based approach has been introduced recently as a new branch of geostatistics, in which stochastic models are constructed as a sequence of depositional events. The sedimentological process is incorporated as a set of numerical rules that control architectural element geometry and the sequence of events through the occurrence of avulsion, meander migration, progradation, retrogradation, and aggradation. In addition, event-based models can be conditioned to sparse well data and soft data (seismic), typically available in deep-water systems.

The integration of sedimentological process into geostatistical modeling may provide a more geologically realistic representation of reservoir heterogeneity and help better assess reservoir flow response. Also, event-based geostatistics may be applied as a new framework to test distilled sedimentological rules and analyze their impact on reservoir heterogeneity.

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