--> Abstract: Process Based Modelling of Deep Marine Reservoir Systems; #90063 (2007)

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Process Based Modelling of Deep Marine Reservoir Systems

 

Aas, Tor E.1, Ragnar Hauge2, Bjorn Fjellvoll2, Petter Abrahamsen2, John Howell1, Stephen Tucker3 (1) University of Bergen, 5020 Bergen, Norway (2) Norwegian Computing Centre, NO-0373 Oslo, Norway (3) Conoco Phillips, Aberdeen, United Kingdom

 

Preserved sedimentary and reservoir architecture is function of accommodation, sediment supply and depositional process. Recent advances in numerical, process based modelling have allowed the recreation of flow and deposition within single turbidity events. However, such simulations are computationally very expensive and unsuitable for re-creating reservoirs which are comprised of the results of hundreds of flow events.

 

A new method for combining a simplified simulation of event based sedimentation in turbidites is presented. Physical processes are simplified while still account for seabed topography, gravity, fluid friction, kinematics, ocean currents, sedimentation and erosion rates. Individual events start in a confined feeder channel and experience a hydraulic jump where the flow stalls and widens into a lobe. Modeling individual events is very fast and hundreds can be simulated. This method is combined with stochastic elements to allow reservoir uncertainty modeling and conditioning to well data.

 

The basic inputs are paleobathymetry and well data. Paleobathymetry is derived from structural restoration and decompaction of key surfaces. The complete workflow has been tested on two datasets. The Early Cretaceous Kopervik Sandstone from the Outer Moray Firth, North Sea comprises a sand-rich, series of fill and spill deposits sourced from the west. The trend contains a number of important fields and numerous leads. The second dataset is from the Eocene-Oligocene Peira Cava turbidite system which crops out the Alpine foreland in southeastern France. The basin fill architecture comprises proximal scour-and-fill facies deposited in a syndepositional syncline. These datasets have been used to test this new modeling approach.

AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California