Hugoton Geomodel: a Hybrid Stochastic-Deterministic Approach

Bohling, Geoffrey C.¹, Martin K. Dubois¹, Alan P. Byrnes¹ (1) Kansas Geological Survey, University of Kansas, Lawrence, KS

Development of the geomodel for the 170-m thick, 16,000 km2 Permian-age Hugoton giant gas field in southwest Kansas involved various modeling methodologies on the continuum from stochastic to deterministic. Significant differences in lithofacies-specific permeability-porosity and capillary pressure properties and inability to determine saturations from wireline logs because of deep filtrate invasion prompted development of a lithofacies-based matrix properties model. The 108-million cell geomodel was developed by: 1) defining lithofacies in wells with neural network models trained on core lithofacies-to-log correlations and estimating porosity from wireline logs at 1600 node wells; 2) modeling between wells using sequential indicator simulation (SIS) for lithofacies and sequential Gaussian simulation (SGS) for porosity; and 3) calculating permeability, capillary pressure, and relative permeability for each lithofacies-porosity combination using empirical transforms with water saturation calculated using the lithofacies/porosity-specific capillary pressure and a location-specific height above free-water level. Although the SIS and SGS processes are both stochastic, the large horizontal ranges of the estimated variograms lead to multiple realizations that are nearly identical and approaches deterministic prediction. The neural network lithofacies predictions at node wells are also probabilistic. Aspects of the geomodel were validated using reservoir flow simulations at the well and multi-well, multi-section scales. The Hugoton geomodel workflow illustrates the continuum between stochastic and deterministic modeling and the dependence of the methodology used for each property on the available data, the scale of prediction, and the order (predictability) of the system relative to the property being modeled.