Methodology of organic-rich shale lithofacies identification and prediction: A case study from Marcellus Shale in the Appalachian basin

Fingerprinting Marcellus Shale waste products from Pb isotope and trace metal perspectives

“…Expected differences in mineral content, such as increased calcite in the limestone units, are preserved in the cuttings and suggest there was little to no downhole contamination during the dry drilling process. XRD analysis reveals major minerals of the Marcellus Shale include quartz, calcite, pyrite, chlorite and illite, similar to previous reports of Marcellus Shale mineralogy (e.g., Hosterman and Whitlow, 1983;Wang and Carr, 2012;Jin et al, 2013). A mineralogical transition from chlorite group clays in the upper Marcellus Shale, to presence of calcite and more abundant pyrite in the middle and lower Marcellus Shale was indicated by XRD results.…”

“…Upper highest natural gas production rates (Wang and Carr, 2012). In our study, as a proxy for organic matter content we report loss on ignition values, which range between 1% and 4% for non-Marcellus Shale samples, and between 5% and 10% for Marcellus Shale samples (Table S2).…”

“…(API), to help find high production sweet spots in shale-gas reservoirs (Wang and Carr, 2012). Lash and Engelder (2011) and Kohl et al (2014) used gamma ray responses from down hole wireline logs to help predict natural gas production potential on a local and regional basis in the Appalachian Basin.…”

Geochemical and mineralogical sampling of the Devonian shales in the Broadtop synclinorium, Appalachian basin, in Virginia, West Virginia, Maryland, and Pennsylvania

“…Wang and Carr (2012a,b) found the population distribution of quartz, clay, and carbonate mineral content from 195 core samples collected from 18 wells to be similar to that for the outcrop samples analyzed in this study. The mineral composition of Marcellus Shale core data shown in figure 5 of Wang and Carr (2012a) The mineralogy of the Marcellus Shale samples in this study indicates low-grade metamorphism. It is possible that smectite reacted with aluminum and potassium to produce illite, chlorite, and quartz (Hosterman and Whitlow, 1983).…”

Petro-Elastic Log-Facies Classification Using the Expectation–Maximization Algorithm and Hidden Markov Models

“…Chen and Hiscott (1999) present a statistical facies classification based on clustering analysis in a marine turbiditic environment. Wang and Carr (2012) presented a lithofacies classification in a shale gas reservoir in the Marcellus formation. Similar methods have also been presented in seismic facies classification where the well log-facies classification is extended to the three-dimensional reservoir model using seismic data and inverted seismic attributes.…”