Statistically-based Lithofacies Predictions for 3-D Reservoir Modeling:
Example from the Panoma (Council Grove) Field, Hugoton Embayment, Southwest Kansas

Martin K. Dubois¹, Alan P. Byrnes¹, Geoffrey C. Bohling¹, Shane C. Seals², and John H. Doveton^1
1Kansas Geological Survey, University of Kansas
² Pioneer Natural Resources USA, Inc.

Abstract

The Panoma (Council Grove) Field in southwest Kansas lies stratigraphically subjacent to the more prolific Hugoton (Chase) Field, and has recovered 2.8 TCF of gas from approximately 2,600 wells across 1.7 million acres since its discovery in the early 1960's. Field-wide upscaling of lithofacies distribution for reservoir characterization has proven problematic in large heterogeneous reservoirs like the Panoma Field, but prediction tools, neural networks and the Excel add-in Kipling.xla, a non-parametric discriminant analysis tool, provide solutions to the facies prediction dilemma.

Panoma produces gas from the upper seven fourth-order sequences of the Permian Council Grove Group containing 50% nonmarine siliciclastics and 50% marine carbonates and siliciclastics. Lithofacies controlled petrophysical properties dictate gas saturations and discrimination of lithofacies reduces standard error in permeability prediction in marine carbonate facies by a factor of twelve. Nonmarine siliciclastic facies error was reduced by a factor of three. At low gas column heights, lithofacies discrimination can result in predicted saturation differences of 20-40% while differences at high gas column heights, near "irreducible", are less than 10%.

Both a neural network and Kipling.xla were "trained" on data from eight wells including half-foot digital wireline log data and descriptions of two thousand feet of core utilizing a digital rock classification scheme. Both models were then used to predict lithofacies in non-cored wells based on their log attributes. Techniques employed in this study could be applied to other large and complex reservoirs where accurate representations of lithofacies heterogeneity in the 3D volume are key to realistic reservoir analysis.

Kansas Hugoton Project

The Hugoton Project (http://www.kgs.ku.edu/Hugoton/index.html) is an Industry, University and Governmental funded consortium whose purpose is to develop technology and information to better understand the oil and gas resources of the Hugoton Embayment in Southwest Kansas. This paper is one of the outcomes of the five year project.

We wish to acknowledge members of the Hugoton Consortium that contributed data including Pioneer Natural Resources USA, Inc., BP, OXY USA, Inc., and Anadarko Petroleum Corp. We are grateful to those who served as technical advisors including Kevin Schepel, Louis Goldstein, and Randy Offenberger, Pioneer, and those that provided technical support including Bob Perry, Bill Tulp Jenna Anaya and Susan Leigh, Pioneer, Tim McGinnley, McGinnley and Associates, David Hamilton and Jeff Kiester, SCM, Inc., and Ken Dean and Mike Maroney, Kansas Geological Survey.

Purpose

To construct a geologic and petrophysical model of the Panoma Field in sufficient detail to accurately represent the fine-scale vertical and lateral heterogeneities for accurate reservoir modeling of the entire field.