--> High-Resolution Sequence Stratigraphic Architecture and Reservoir Characterization of the Mississippian Burlington/Keokuk Formation, Northwestern Arkansas

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High-Resolution Sequence Stratigraphic Architecture and Reservoir Characterization of the Mississippian Burlington/Keokuk Formation, Northwestern Arkansas

Abstract

The Upper Osagean (Mississippian) Burlington-Keokuk Formation of the Mid-Continent is a limestone and chert interval that provides an analog to subsurface reservoirs of the “Mississippian Lime”. Using an integrated sequence stratigraphic approach combining facies descriptions from outcrop and thin-section analysis, high-resolution photography, and spectral gamma-ray logs from an outcrop in northwest Arkansas, coarsening and thickening upward successions of skeletal packstones and grainstones were identified. Accurate identification of cyclic stacking patterns allowed for the recognition and correlation of high frequency sequences across the outcrop. Within each sequence, regressive crinoidal grainstones exhibit abundant early syntaxial cement, effectively occluding all primary pore space. Transgressive packstones, however, contain little to no void-filling cement and exhibit varying degrees of silicification and dissolution, creating adequate reservoir rock in many cases. The sequence hierarchy thus creates a series of potential stacked reservoir and seal units within the interval. Facies associations suggest deposition occurred within a high energy skeletal shoal complex. Analysis of depositional analogs was utilized to provide first approximations of the scale of facies geometries and distributions in plan-view not provided by outcrop. Stochastic modeling allowed for integration of spatial and geometric facies data from analogs to be combined with the stratigraphic architecture from outcrop to visually represent facies variability and potential reservoir distribution in the formation. Direct comparison of geostatistical models with two-dimensional rock data from outcrop provide a means to test multiple iterations of the model and to identify the most effective modeling parameters necessary for accurate subsurface representation and visualization.