--> Geomorphology of Carbonate Systems and Reservoir Modeling: Carbonate Training Images, FDM Cubes, and MPS Simulations, by Marjorie Levy, Paul M. (Mitch) Harris, Sebastien Strebelle, and Eugene C. Rankey, #40289 (2008)

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PSGeomorphology of Carbonate Systems and Reservoir Modeling: Carbonate Training Images, FDM Cubes, and MPS Simulations*


Marjorie Levy1, Paul M. (Mitch) Harris1, Sebastien Strebelle1, and Eugene C. Rankey2


Search and Discovery Article #40289 (2008)

Posted July 15, 2008


*Adapted from poster presentation at AAPG Annual Convention, Long Beach, CA, April 1-4, 2007.

Click to view list of articles adapted from presentations by P.M. (Mitch) Harris or by his co-workers and him at AAPG meetings from 2000 to 2008.


1 Chevron Energy Technology Company, San Ramon, CA ([email protected]; [email protected]; [email protected])

2 University of Miami CSL, Miami, FL ([email protected])



Modern carbonates serve as important analogs for reservoir studies, especially with a recent change in emphasis to more quantification of facies characteristics. The purpose of this study is to demonstrate the utility of facies attributes from modern analogs in Multiple-Point Statistics (MPS)/Facies Distribution Modeling (FDM) of carbonate reservoirs.

To explore use of data from the Modern, an isolated platform was modeled using training images, FDM cubes, and MPS simulations for varied grainstone and reef reservoir types. Grainstone shoals are linear, sinuous, or crescent-shaped; each can contain barcrest, barflank, and island environments. These examples are based on modern analogs from the Bahamas and show a range of grainstone geometries that might be expected in the subsurface. Our reef examples are based on modern analogs from Belize. Barrier reefs are continuous and discontinuous; continuous barrier reefs can have associated sand aprons. Patch reefs and small aprons can occur in the platform interior.

MPS training images are constructed for individual facies and for combined facies associations drawing upon a dimensional database for input parameter ranges. The training image is a 3D conceptual model of the reservoir, containing information about facies dimensions and relationships among facies geobodies. Facies depocenter maps are generated for the various facies, and the stratigraphy of the reservoir is modeled by digitizing a vertical proportion curve reflecting the variations of facies proportions with depth. The map data and the vertical data are combined to generate facies probability cubes. The facies probability cube allows controlling the spatial distribution of the facies in the MPS model when combined with the training image. These facies-based MPS earth models are being used to test which input parameters have the greatest impact on flow behavior for uncertainty management.




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