Abstract High-resolution X-ray fluorescence (XRF) analysis was conducted to characterize the vertical and lateral distribution of elemental compositions in six carbonate mudstone cores from the Late Cretaceous unconventional plays in North America. The primary objective was to develop a rapid, efficient, cost-effective, and non-destructive method for analyzing mudstone cores. This goal was achieved by combining and analyzing representative elements from each group, including Ca (carbonate-associated element), Si, Al, Fe, Ti, K (terrigenous-associated elements), and Mo, Ni, V, and Cu (redox-sensitive trace elements). Principal Component Analysis (PCA) and Hierarchical Clustering on Principal Components (HCPC) were utilized to identify chemofacies and establish chronostratigraphic subdivisions, subsequently facilitating correlations between different wells. Each dataset has a generated several clusters, and their validity has been confirmed by comparing them with core samples and measurements. This process led to the selection of the most appropriate cluster/chemofacies. Each cluster/chemofacies represents a major lithofacies defined by its chemical composition. Five rock classes/chemofacies based on elemental compositions have been identified in this study, including chalk/limestone, marly limestone, organic-rich mudstone, sandstone, and mixed mudstone. Additionally, through the integration of core measurements and chemostratigraphy, multiple stratigraphic zones have been identified within the studied formation. Each zone corresponds to a combination of lithology, elemental composition, organic richness, and mechanical properties. This integrated approach enhances our understanding of the reservoir's complexity, identifies sweet spots, and provides valuable insights for reservoir characterization and exploration activities. This approach utilizes robust and reproducible analytical techniques to scan large volumes of core in a short period typically within a few days while maintaining core integrity. The non-destructive nature of HH-XRF makes it ideal for preserving samples for additional studies and archiving. When integrated with chemostratigraphy, the method enhances stratigraphic correlation across wells and supplements traditional sedimentological core descriptions with quantitative data. This fusion of data-driven and visually based interpretation improves facies classification, reduces subjectivity, and supports better-informed decision-making in reservoir characterization and exploration workflows. Overall, this study demonstrates the value of high-resolution XRF scanning combined with multivariate analysis as a fast, economical, and effective tool for improving geological understanding in mudstone-dominated unconventional reservoirs.
Chan et al. (Tue,) studied this question.