HIGH-RESOLUTION, MULTI-SCALE GEOLOGICAL AND PETROPHYSICAL ASSESSMENT OF HETEROGENEITY IN THE WOODFORD SHALE IN SOUTHERN OKLAHOMA, USA

Geochemical and geophysical log studies of the Woodford Shale, Oklahoma, USA, have broadly divided the formation into three informal stratigraphic members. However, a lack of high-resolution analyses of within- and between-member heterogeneity has limited the evaluation of this unconventional reservoir. Specifically, this research seeks to characterize compositional, surface, and mechanical heterogeneities over various scales, combining field observations, petrographic imaging, geochemical analysis, and mechanical behaviors. To achieve our objective, this study emphasizes both field and laboratory components. Field components includes detailed stratigraphic sections, lithofacies characterization, joint and fracture measurements, and gamma-ray data from six Woodford Shale outcrops exposed in Murray and Carter Counties in Oklahoma. These datasets, in conjunction with previous studies, confirmed the presence of three members. For laboratory investigations, each shale sample was cut perpendicular to bedding, producing at least four 1-inch by 0.5-inch to 1-inch billets to allow the necessary destructive tests to be performed on the same surfaces. Geochemical heterogeneities were determined by integrating loss-on-ignition, X-ray diffractometry, and X-ray fluorescence results to determine total organic and carbonate contents, mineralogical composition, and elemental composition. Sub-millimeter to nano-scale surface heterogeneities, both along and across the bedding planes, in terms of morphology, fracture, composition, and pore system, were quantified by analyzing blue-dye-impregnated thin sections and scanning electron microscope images with ImagePro Primer. Geomechanical heterogeneities in the samples include brittleness and indirect uniaxial compressive strength derived from datasets of the mineral composition, bed to nano-scale fractures, and Schmidt hammer test. Finally, geochemical, surface and mechanical heterogeneities were analyzed by statistical and logistic tools like analysis of covariance, artificial neural networks for multivariate correlations, and principal component analysis to develop relationships among the three members of the Woodford Shale.