Multiscale Geologic and Petrophysical Modeling of the Giant Hugoton Gas Field (Permian), Kansas and Oklahoma, U.S.A.
Martin K. Dubois, Alan P. Byrnes, Geoffrey C. Bohling, John H. Doveton, 2006. "Multiscale Geologic and Petrophysical Modeling of the Giant Hugoton Gas Field (Permian), Kansas and Oklahoma, U.S.A.", Giant Hydrocarbon Reservoirs of the World: From Rocks to Reservoir Characterization and Modeling, P. M. (Mitch) Harris, L. J. (Jim) Weber
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Reservoir characterization and modeling from pore to field scale of the Hugoton field (central United States) provide a comprehensive view of a mature giant Permian gas system and aid in defining original gas in place and the nature and distribution of gas saturation and reservoir properties. Both the knowledge gained and the techniques and workflow employed have implications for understanding and modeling reservoir systems worldwide that have similar geologic age and reservoir architecture (e.g., Gwahar and North fields, Persian Gulf). The Kansas-Oklahoma part of the field has yielded 34 tcf (963 billion m3) gas throughout a 70-yr period from more than 12,000 wells. Most remaining gas is in lower permeability pay zones of the 557-ft (170-m)-thick, differentially depleted, layered reservoir system.
The main pay zones represent 13 shoaling-upward, fourth-order marine-continental cycles comprising thin-bedded (6.6-33-ft; 2-10-m), marine carbonate mudstone to grainstone and siltstones to very fine sandstones and have remarkable lateral continuity. The pay zones are separated by eolian and/or sabkha red beds having low reservoir quality. Petrophysical properties vary among 11 major lithofacies classes. Neural network procedures, stochastic modeling, and automation facilitated building a detailed full-field three-dimensional (3-D) 108-million-cell cellular reservoir model of the 10,000-mi2 (26,000-km2) area 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 noncored wells; (3) populate a 3-D cellular model with lithofacies using stochastic methods; and (4) populate model with lithofacies-specific petrophysical properties and fluid saturations.