Mackenzie-Peel Platform and Ellesmerian Foreland Composite Tectono-Sedimentary Element, northwestern Canada

The Mackenzie-Peel Platform tectono-sedimentary element, and the overlying Ellesmerian Foreland tectono-sedimentary element, consist of Cambrian to Early Carboniferous shelf and slope sedimentary deposits in Canada&s northern Interior Plains. In this chapter, these elements are combined into the Mackenzie-Ellesmerian Composite Tectono-Sedimentary Element. The history of the area includes early extensional faulting and subsidence in the Mackenzie Trough, passive margin deposition across the Mackenzie-Peel Platform, local uplift and erosion along the Keele Arch, subsidence and deposition in the Ellesmerian Foreland, possible minor folding during the Ellesmerian Orogeny, and folding and faulting in Cretaceous to Eocene time associated with the development of the Canadian Cordillera. Recorded petroleum discoveries are within Cambrian sandstone (Mount Clark Formation), Devonian carbonate strata (Ramparts and Fort Norman formations), and Devonian shale (Canol Formation). Additional oil and gas shows are documented from Cambrian to Silurian carbonate units (Franklin Mountain and Mount Kindle formations), Devonian carbonate units (Arnica, Landry, and Bear Rock formations), and Late Devonian to Early Carboniferous siliciclastic units (Imperial and Tuttle Formations). Petroleum exploration activity within the area has occurred in several phases since 1920, most of it associated with the one producing oil field at Norman Wells.

Successful complete digestion of well lithified shale and extraction of microfossils from Devonian beds in western New York

ABSTRACT: Researchers in paleontological and paleoecological sciences often need complete disaggregation of rock materials for certain lines of investigation. However, complete disaggregation of more lithified sedimentary rock is known to be problematic. A complete shale disaggregation method implementing quaternary ammonium surfactants,widely used in paleontological sciences for poorly lithified shale and mudstone, was successfully used on well lithified Devonian shale in the Appalachian Basin ofWestern New York. Over 50 Devonian gray and black shale samples were collected from multiple localities in western New York (Cashaqua, Rhinestreet, Skaneateles, Windom, and Ludlowville), coarsely crushed, and fully immersed in a quaternary ammonium surfactant until complete disaggregation was achieved (5–14 days); aliquots were run through a series of nested sieves. The sieved sediments contained hundreds of well-preserved microfossils released from the shale: ostracods, dacryoconarids, and previously unreported palymorphs, charophytes, agglutinated foraminifera, miospores, and other microspherules. These microfossils were easily found within disaggregated and sieved samples but were unrecognizable on the shale surface and destroyed in prior investigations of whole rock thin sections. In addition to more traditional approaches, inclusion of this complete rock disaggregation method may assist in a more complete analysis of material, increase our understandings of ancient basin systems and have important implications on our understanding of the paleoecology during the Late Devonian marine biotic crises.

Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.