ABSTRACT Scanning electron microscopy (SEM) has revolutionized our understanding of shale petroleum systems through microstructural characterization of dispersed organic matter (OM). However, as a result of the low atomic weight of carbon, all OM appears black in SEM (BSE [backscattered electron] image) regardless of differences in thermal maturity or OM type (kerogen types or solid bitumen). Traditional petrographic identification of OM uses optical microscopy, where reflectance (%R o ), form, relief, and fluorescence can be used to discern OM types and thermal maturation stage. Unfortunately, most SEM studies of shale OM do not employ correlative optical techniques, leading to misidentifications or to the conclusion that all OM (i.e., kerogen and solid bitumen) is the same. To improve the accuracy of SEM identifications of dispersed OM in shale, correlative light and electron microscopy (CLEM) was used during this study to create optical and SEM images of OM in the same fields of view (500× magnification) under white light, blue light, secondary electron (SE), and BSE conditions. Samples ( n = 8) of varying thermal maturities and typical of the North American shale petroleum systems were used, including the Green River Mahogany Zone, Bakken Formation, Ohio Shale, Eagle Ford Formation, Barnett Formation, Haynesville Formation, and Woodford Shale. The CLEM image sets demonstrate the importance of correlative microscopy by showing how easily OM can be misidentified when viewed by SEM alone. Without CLEM techniques, petrographic data from SEM such as observations of organic nanoporosity may be misinterpreted, resulting in false or ambiguous results and impairing an improved understanding of organic diagenesis and catagenesis.

ABSTRACT The Cenomanian–Turonian Eagle Ford of South Texas is largely composed of two interbedded rock types: marls and limestones. The marls consist mainly of coccoliths with sand- and silt-size grains predominantly comprised of planktonic foraminifera with lesser amounts of inoceramid fragments and other carbonate grains. The limestones are recrystallized, and they contain calcified radiolarians and calcispheres, with almost all pore spaces having been filled with calcite cement. Most of the hydrocarbons in the Eagle Ford, regardless of thermal maturity, reside in the pore network of the marls. Economic production of hydrocarbons stored in these marls, which have nanodarcy permeabilities, can only be obtained by inducing and maintaining fractures with hydraulic stimulation. The interbedding of the marls with limestones form centimeter-scale brittle–ductile (or stiff-compliant) couplets that influence hydraulic fracturing over a range of scales, and at the smallest scale it may increase production by hosting complex near-wellbore fracture systems. Natural fractures that were already present may be open or cemented and reactivated during hydraulic stimulation and contribute to production. This can generate a hybrid fracture system with a larger drainage area and fracture surface area to allow for crossflow from the matrix to fractures. The Eagle Ford is a dual-porosity system, with the hydrocarbon stored in the marls feeds a network of progressively larger natural and induced fractures that carry those hydrocarbons to the wellbore. In most cases, the Eagle Ford will be most productive when the “right” mixture of marl and limestone are present. Too much limestone lowers the storage capacity of the system, and too much marl reduces the complexity of the fracture system. The distribution of the limestones is important: Even if the percentage of limestone in two sections is equal, hydraulic stimulation will produce a more complex fracture network when the limestone is present as a series of thin interbeds rather than as a single thick limestone. The interbedding of limestone and marl can be measured using limestone frequency—the number of limestone beds per unit thickness. Variation in production is observed in wells on the same pad completed with the same treatment but landed in zones of differing limestone frequency, with production in these wells increasing with limestone frequency. Also, in a multivariate analysis involving numerous engineering and geologic variables and over 1000 wells, all measures of interbedding reduced to a single factor, which we call limestone frequency, which positively correlated with production.