Summary

This poster illustrates the construction of an improved 3D stochastic model and simulation on St. Louis carbonate systems.

1. Six lithofacies were recognized and classified through the description of 10 cored wells of St. Louis oolite shoal reservoirs in Southwestern Kansas.
2. The reservoir lithofacies, ooid skeletal grainstone, has distinctive petrophysical properties.
3. Neural Network models were developed using digital logs (GR, Rt, PE and Porosity) from cored wells. Llithofacies were predicted in cored and non-cored wells with a high degree of accuracy.
4. Lithofacies predicted from Neural Network model can be interpolated between wells to better understand depositional patterns and external geometry of St Louis Limestone.
5. Stratigraphic surfaces were used to build a 3D stratigraphic framework.
6. Object-based stochastic modeling was performed to build lithofacies models in three St. Louis oolite reservoirs to better understand the 3D external geometries of St. Louis oolite shoals.
7. Internal geometries of St. Louis oolite shoal reservoirs were illustrated by building 3D porosity and permeability distribution models using stochastic simulation.
8. Multiple realizations can be built for all the stochastic simulation results.
   

Further Work

1. Add facies proportion trends and evaluate effect of azimuthal trend.
2. Rank the stochastic simulation models and select the reasonable models to upscale for streamline simulation test.
3. Compare the streamline simulation results and select the best model to export for flow simulation.
4. Perform reservoir simulation to verify the geostatistical models by production history match.

References

Carr, D. D., 1973, Geometry and origin of oolite bodies in the Ste. Genevieve Limestone (Mississippian) in the Illinois basin: Indiana geological survey Bulletin 48, 81 p.
Carr, T. R. and Lundgren, Carl E, 1994, Use of Gamma Ray Spectral Log to recognize Exposure Surfaces at the Reservoir Scale: Big Bow Field (St. Louis Limestone). Kansas: Unconformity-related hydrocarbons in sedimentary sequences, Rocky mountain association of geologists, p. 79-88, 281.
Gary, Richard J., 1994, Petrophysical Characterization of Mississippian Ooid-Shoal Reservoirs, Hugoton Embayment, Southwest Kansas, Armco, 46 p.
Handford, C. R., 1988, Review of carbonate sand-belt deposition of ooid grainstones and application to Mississippian reservoir, Damme Field, southwestern Kansas: AAPG Bulletin, V. 72, p. 1184-1199.
Garsmueck, Mark, and Weger, Ralf, 2002, 3D GPR reveals complex internal structure of Pleistocene oolitic sandbar: The Leading Edge, p 634-639.

Acknowledgments

I would like to thank the Kansas Geological Survey for providing the funds for this project as a part of my doctoral research at the University of Kansas. Special appreciation is extended to Dr. Timothy R. Carr and Dr. Robert H. Goldstein for advise and insights for my dissertation research. Roxar Inc. is thanked for providing an academic license to RMS. I also would like to thank Geoplus Corporation for providing access to PETRA. Geoff Bohling provide assistance with neural network modeling. I also want to acknowledge Martin Dubois, Bill Guy, Ed Washburn, Troy Johnson, and Matt Brown for comments and insights; and Ken Stalder for technical support.