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Abstract

Channel stacking pattern in deepwater reservoirs has a significant impact on reservoir producibility. These stacking patterns are the result of feedbacks between turbidity currents and associated supply variations and the evolution and aggradation of slope physiography. Turbidite flow events that leave significant physiographic relief due to underfilled within-channel deposition or local erosion tend to have greater influence on subsequent flow events, resulting in organized channel stacking patterns. Turbidite flow events that fill their channels do not confine subsequent flows and result in disorganized channel stacking patterns. High rates of system aggradation result in a greater degree of inter-element disconnection, low rates result in amalgamated elements.

Event-based models are amenable to the integration of expert rules related to the influence of channel fill (fraction of active channel fill) and aggradation rate on channel stacking pattern. The event-based approach is part of a new subset of geostatistical modeling that focuses on greater integration of stratigraphic concepts and sedimentological process. In the event-based method stochastic models are constructed as a sequence of depositional events. The sedimentological process and allogenic forcing are approximated as a set of empirical and predictive rules. The resulting numerical laboratory efficiently constructs high-resolution reservoir models and allows calibration of model response to changes in input values.

Event-based models are constructed based in part on outcrop examples with various channel stacking patterns. The resulting models are used to explore the relationship between stacking pattern, preservation potential of axis, off-axis and margin facies assemblages and reservoir producibility. In addition, these models can be used to illustrate hierarchical relationships, explore larger issues related to the value of architectural information and to aid in the discovery of new rules by induction combining outcrop observations and models results.

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