Multiscale geologic and petrophysical modeling of the giant Hugoton Gas Field (Permian), Kansas and Oklahoma

Multiscale geologic and petrophysical modeling of the giant Hugoton Gas Field (Permian), Kansas and Oklahoma

Kansas Geological Survey

KGS Open-file 2006-12

American Association of Petroleum Geologists© AAPG Memoir 88 Giant Hydrocarbon Reservoirs of the World. Reprinted by permission of AAPG whose permission is required for further use.

Multiscale geologic and petrophysical modeling of the giant Hugoton Gas Field (Permian), Kansas and Oklahoma

Martin K. Dubois, Alan P. Byrnes, Geoffrey C. Bohling, and John H. Doveton
Kansas Geological Survey, University of Kansas, Lawrence, Kansas 66047

Abstract

Reservoir characterization and modeling from pore to field scale of the Hugoton Field (central U.S.) provides a comprehensive view of a mature giant Permian gas system and is aiding 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, Arabian Gulf). The Kansas-Oklahoma portion of the field has yielded 963 billion m3 (34 tcf) gas over a seventy year period from over 12,000 wells. Most remaining gas is in lower permeability pay zones of the 170-meter thick, differentially depleted, layered reservoir system.

The main pay zones represent thirteen shoaling-upward, fourth-order marine-continental cycles, comprising thin-bedded (2-10 meter), marine carbonate mudstone to grainstone and siltstones to very fine sandstones, and have remarkable lateral continuity. They are separated by low reservoir quality eolian and sabkha redbed. Petrophysical properties vary among eleven major lithofacies classes. Neural network procedures, stochastic modeling, and automation facilitated building a detailed full-field 3D 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 non-cored wells, 3) populate a 3D cellular model with lithofacies using stochastic methods, and 4) populate model with lithofacies-specific petrophysical properties and fluid saturations.

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