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Multiscale Geologic and Previous HitPetrophysicalNext Hit Modeling of the Giant Hugoton Gas Previous HitFieldNext Hit (Permian), Kansas and Oklahoma

Martin K. Dubois, Alan P. Byrnes, Geoffrey C. Bohling, and John H. Doveton
Kansas Geological Survey, University of Kansas, Lawrence, KS

Reservoir characterization and modeling from pore to Previous HitfieldNext Hit scale of the Hugoton Previous HitFieldNext Hit (central U.S.) provides a unique and comprehensive view of a mature giant Permian gas system and has implications for production strategies in similar reservoirs worldwide. Volumetric calculations of the static model for the 16,000 square km Previous HitfieldNext Hit indicate the 963 billion m3 (34 TCF) gas produced in seventy years from the Kansas-Oklahoma portion of the Previous HitfieldNext Hit represents approximately 65-70% of original gas in place. Most remaining gas is in lower permeability pay zones of the 170-meter thick, differentially depleted, layered reservoir system.

Thin-bedded (2-10 meter), marine carbonate mudstones to grainstones and siliciclastics in thirteen fourth-order marine-nonmarine cycles, illustrated in core, are the main pay zones separated by eolian and sabkha redbeds of low reservoir quality. The heterolithic system is a classic example of sedimentary response to rapid glacio-eustatic sea level fluctuations on an extremely gently sloped ramp of an asymmetric foreland basin (Anadarko) on a craton. Previous HitPetrophysicalNext Hit properties vary between eleven major lithofacies classes. Water saturations cannot be interpreted from logs due to deep filtrate invasion. Geostatistical methods (neural network and stochastic modeling) and data Previous HitanalysisNext Hit automation facilitated building a detailed 3D cellular reservoir model using a four step workflow: 1) define lithofacies in core and correlate to electric log curves (training set), 2) train a neural network and predict lithofacies at non-cored Previous HitwellsNext Hit, 3) populate a 3D cellular model with lithofacies using stochastic methods, and 4) populate model with lithofacies associated Previous HitpetrophysicalTop properties and fluid saturations.