--> Carbonate Reservoir Modeling Using Multiple-Point Statistics (MPS) / Facies Distribution Modeling (FDM), by Marjorie Levy, Paul M. (Mitch) Harris, and Sebastien Strebelle, #40293 (2008)

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PSCarbonate Reservoir Modeling Using Multiple-Point Statistics (MPS) / Facies Distribution Modeling (FDM)*


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


Search and Discovery Article #40293 (2008)

Posted August 1, 2008


*Adapted from poster presentation at AAPG Annual Convention, Houston Texas, April 9-12, 2006.

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, California, USA ([email protected]; [email protected]; [email protected])



We have explored the use of MPS/FDM modeling in a carbonate reservoir. We have modeled the platform top of an isolated platform example and tested various scenarios for the distribution of grainstone facies.

The training image is a 3D conceptual model of the reservoir, containing information about facies dimensions and relationships among facies. Five facies were considered: Bar crest = best reservoir quality due to sorting in “highest energy” setting; Bar flank = good reservoir quality between bar crests, includes flanks of bars and intervening tidal channels; Island = localized areas where permeability is enhanced by dissolution during meteoric diagenesis; Deeper platform = poorer reservoir quality in platform areas away from bars and channels; and Background = “tight” intervals due to muddier facies or to porosity-plugging cementation.

The facies probability cube allows controlling the spatial distribution of the facies in the MPS model. First, facies depocenter maps were generated for deeper platform, bar flank, bar crest and island. Then, the stratigraphy of the reservoir was modeled by digitizing a vertical proportion curve reflecting the variations of facies proportions with depth. Three alternative vertical proportion curves were created, representing respectively a gradual trend, cyclicity at the scale of composite sequences, and high cyclicity at the scale of individual sequences. Corresponding alternative facies probability cubes were generated for these three cases.

Several scenarios were run: the gradual, cyclic and highly cyclic cases; both narrow and wide bar crests and bar flanks; and with constant and variable azimuth. The wide bar crest/bar flank and very cyclic simulation produce results that qualitatively appear most reasonable in both cross section and map views.





uModeling example






















uModeling example



MPS (Multiple-Point Statistics)

MPS is an innovative depositional facies modeling technique, developed by Chevron in collaboration with Stanford University, that uses 3D conceptual geological models as training images to integrate geological information into reservoir models. Replacing the traditional variogram with a training image allows MPS to capture complex spatial relationships between multiple facies, and to model non-linear shapes such as sinuous channels that conventional variogram-based modeling techniques typically fail to reproduce. In addition, because MPS is not an object-based, but still a pixel-based algorithm, MPS models can be constrained by very large numbers of wells and 3D facies probability cubes derived from seismic data or from reservoir facies deposition interpretation (using FDM).

For the last three years, the MPS/FDM workflow has been preferred to variogram-based and object-based techniques to model important Chevron assets both shallow-water clastic reservoirs (Indonesia and Angola), and deepwater reservoirs (Gulf of Mexico, North Sea, Angola, and Nigeria). In those projects, the MPS/FDM workflow enabled the generation of geologically realistic facies models, which significantly improved reservoir OOIP and oil recovery uncertainty assessment.


Carbonate Reservoir Modeling Example Using MPS/FDM

1. Workflow.

2. Study rationale.

3. Model region and layering.

4. Build training images.

5. Generate facies probability cube.

6. Add well data conditioning and other modeling constraints.

7. Run MPS simulation.

8. Model summary.

Matches facies regions of platform top which were defined during reservoir characterization; Matches stratigraphic variation defined during sequence stratigraphy studies with facies proportion curves; Matches conceptual models for facies types from modern analogs with training images; Generally matches facies identified in cored wells.


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