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Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone, southwest Kansas

Lianshuang Qi,1 Timothy R. Carr,2 Robert H. Goldstein3

1Kansas Geological Survey, University of Kansas, Lawrence, Kansas 66047-3726; present address: Chevron Energy Technology Company, 6001 Bollinger Canyon Road, Room D1260, San Ramon, California 94583; [email protected]
2Kansas Geological Survey, University of Kansas, Lawrence, Kansas 66047-3726; [email protected]
3Department of Geology, University of Kansas, Lawrence, Kansas 66045-7613; [email protected]


In the Hugoton embayment of southwestern Kansas, reservoirs composed of relatively thin (lt4 m; lt13.1 ft) oolitic deposits within the St. Louis Limestone have produced more than 300 million bbl of oil. The geometry and distribution of oolitic deposits control the heterogeneity of the reservoirs, resulting in exploration challenges and relatively low recovery. Geostatistical three-dimensional (3-D) models were constructed to quantify the geometry and spatial distribution of oolitic reservoirs, and the continuity of flow units within Big Bow and Sand Arroyo Creek fields.

Lithofacies in uncored wells were predicted from digital logs using a neural network. The tilting effect from the Laramide orogeny was removed to construct restored structural surfaces at the time of deposition. Well data and structural maps were integrated to build 3-D models of oolitic reservoirs using stochastic simulations with geometry data.

Three-dimensional models provide insights into the distribution, the external and internal geometry of oolitic deposits, and the sedimentologic processes that generated reservoir intervals. The structural highs and general structural trend had a significant impact on the distribution and orientation of the oolitic complexes. The depositional pattern and connectivity analysis suggest an overall aggradation of shallow-marine deposits during pulses of relative sea level rise followed by deepening near the top of the St. Louis Limestone. Cemented oolitic deposits were modeled as barriers and baffles and tend to concentrate at the edge of oolitic complexes. Spatial distribution of porous oolitic deposits controls the internal geometry of rock properties. Integrated geostatistical modeling methods can be applicable to other complex carbonate or siliciclastic reservoirs in shallow-marine settings.

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