5/1/2023 0 Comments Ammonite drawing![]() Mechanical characterization was established through the employment of core (Micro-Rebound Testing (MRT) on 700 ft of the core at 0.5 ft resolution), log-based methods (Young's modulus (E*), Poisson's ratio, and UCS), and automated scanning (impulse-hammer and ultrasonic wave velocities), and scratch testing. My original design was rainbow coloured but this tim. My wonderfully bright and cheerful interpretation in glass of an ammonite. Further, open-system pyrolysis was employed to evaluate organic richness, kerogen type, generation potential, and thermal maturity. This is a stained glass suncatcher, entirely handmade by me, using the Tiffany copper foil technique. A non-destructive hand-held energy-dispersive X-ray fluorescence spectrometer (ED-XRF) was employed to acquire elemental compositions with high stratigraphic resolution. Core logging and description were used to define lithofacies heterogeneity and well-to-well variability, including texture, sedimentary features/structures, and bioturbation index (BI). This study integrates sedimentology, geochemistry, and geomechanics to define relationships among lithofacies, elemental compositions, and mechanical properties of five cores from an Early Kimmeridgian carbonate mudstone in an Arabian intrashelf basin. Further, the identified zones can be used to characterize/correlate zones in nearby un-cored wells, with the data generated serving as an indispensable input for establishing a well-log data zonation using unsupervised machine learning algorithms. These techniques can be applied to elemental data acquired using x-ray fluorescence measured from core or cuttings samples or spectroscopy logs to provide robust analysis for unconventional assessment regarding sweet-spot identification, sequence stratigraphic interpretations, and drilling and completion designs. In this study, we propose a novel integrated approach combining sedimentological core description with geochemical data to establish chemofacies and chemostratigraphic zonation using a set of unsupervised statistical tools, i.e., Principal Component Analysis (PCA) and Hierarchical Clustering on Principal Components (HCPC). Given critical mm- to cm-scale variability in mudstones, it is daunting to try to infer compositional variability from well logs and seismic data unless core data and laboratory analyses are available to calibrate the results. Stratigraphic correlation in mudstone intervals is challenging as compared to coarser-grained sedimentary rocks because of the microscale heterogeneity and other constraints. ![]()
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