Statistically-based Lithofacies Predictions for 3-D Reservoir Modeling:An Example from the Panoma (Council Grove) Field,Hugoton Embayment,Southwest Kansas

Kansas Geological Survey
Open-file Report 2003-30


This paper is a snap-shot of an ongoing effort with the ultimate goal of the creation of a robust three-dimensional geomodel suitable for accurate reserve analysis and reservoir simulation. The work to date demonstrates:

  1. There are significant differences in petrophysical properties between lithofacies
  2. Error in estimation of original gas in place (OGIP) and distribution are likely if lithofacies are not taken into account
  3. Lithofacies can be predicted in non-cored wells with sufficient accuracy by using a neural net model trained on lithofacies defined from a relatively small set of cores associated well logs and other defined curves (Marine-Nonmarine).
  4. A vast tops data set, availability of digital well logs, and the automation of the prediction process allows the development of a model to accurately represent the heterogeneous Panoma reservoir.

Work presented here represents the first iteration of a multiple iteration process. The model has not been taken to the reservoir simulation stage due to the need to step back and rebuild the
porosity model using shale corrected porosity (left out in this stage) followed by regeneration of the permeability model.

Further Work

Additional effort will be in several broad areas; 1) “ground truthing” lithofacies prediction and extrapolation, 2) increasing coverage, 3) improving the neural net model and Petrel models, and 4) moving to reservoir simulation phase. These will be accomplished in the following steps:

  1. Test the neural net lithofacies prediction models by comparing additional core lithofacies (from undescribed available core) with those lithofacies predicted.
  2. Test Petrel's stochastic lithofacies modeling procedure by comparing its results to neural net predictions at wells that were not used in conditioning the Petrel model.
  3. Increase well coverage by “recovering” data from the set that was removed for a variety of data quality and standardization reasons including interval skips and log curves requiring normalization (especially the gamma ray and neutron porosity).
  4. Improve porosity model by correcting porosity log curves for shale.
  5. Explore other possible lumping and splitting schemes for the training set using the digital lithologic data.
  6. Consider other predictor variables such as vertical transition probability biasing and relative position within an interval.
  7. Expand efforts in lithofacies geometry biasing in the area of inter-well extrapolation by incorporating predicted lithofacies probabilities and other statistical methods.
  8. Further optimization of input parameters in all aspects of model development.
  9. Develop a detailed, field-wide free water level map
  10. Calculate original gas in place and compare with production history.
  11. Move into reservoir modeling phase and compare with production history.

Selected References

Albus, J. S., 1981, Brains, Behavior, and Robotics, BYTE Publications, Inc., Peterborough, N.H., 352 pp.

Babcock, J.A., T.M. Olson, K.V.K. Prasad, S.D. Boughton, P.D. Wagner, M.K. Franklin, and K.A. Thompson, 1997, Reservoir characterization of the giant Hugoton Gas Field, Kansas, American Association of Petroleum Geologists Bulletin, v. 81, p. 1785-1803.

Bohling, G. C., and J. H. Doveton, 2000, Kipling.xla: An Excel Add-in for Nonparametric Regression and Classification, Kansas Geological Survey, 69 pp.

Byrnes, A.P., M.K. Dubois, and M. Magnuson, 2001, Western tight gas carbonates: comparison of Council Grove Group, Panoma Field, southwest Kansas, and western low permeability gas sands (abs), 2001 American Association of Petroleum Geologists Annual Convention, V. 10, p. A31.

Duda, R. O., P. E. Hart, and D. G. Stork, 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York, 654 pp.

Heyer, J.F., 1999, Reservoir characterization of the Council Grove Group, Texas County, Oklahoma, in D.F. Merriam, ed., Transactions of the American Association of Petroleum Geologists Midcontinent Section Meeting, Wichita, KS.

Olszewski, T.D. and M.E. Patzkowsky, 2003, From cyclothems to sequences: The record of eustacy and climate one an icehouse eperic platform (Pennsylvanian-Permian, North American Midcontinent, Journal of Sedimentary research, v. 73, no. 1, p. 15-30.

Parham, K.D., and J.A. Campbell, Wolcampian shallow shelf carbonate, Hugoton Embayment, Kansas and Oklahoma, 1993, in: Atlas of Major Midcontinent gas reservoirs, Bureau of Economic Geology and Gas Research Institute.

Pippin, L., 1970, Panhandle-Hugoton field, Texas, Oklahoma and Kansas, the first 50 years, in Halbouty, M.T., ed., Geology of giant petroleum fields: Association of Petroleum Geologists Memoir 14, p. 204-222.

Pippin, L., 1985, Prefiled testimony, rebuttal testimony, and supplementary testimony before the Kansas Corporation Commission on behalf of Northern Natural Gas Company, Docket C-164.

Puckette, G.R., D.R. Boardman, II, and Z. Al-Shaieb, 1995, Evidence for sea-level fluctuation and stratigraphic sequences in the Council Grove Group (Lower Permian) Hugoton Embayment, southern Mid continent, in Hyne, N.J., ed.: Tulsa Geological Society Special Publication no. 4, p. 269-290.

Rascoe, B. and F.J. Adler, 1983, Permo-Carboniferous hydrocarbon accumulations, Midcontinent, U.S.A.: American Association of Petroleum Geologists Bulletin, v. 67, p. 979-1001.

Venables, W. N., and B. D. Ripley, 1999, Modern Applied Statistics with S-Plus, Third Edition, Springer-Verlag New York, Inc., 501 pp.

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Last updated May 2003