Depositional-process controls on chemofacies in mixed-lithology submarine lobe deposits: a high-resolution core study from the Permian Wolfcamp XY Formation, Delaware Basin, Texas, U.S.A.
Authors: Putri Shaskia Herida , Jobe Zane , Melick Jesse , Wood Lesli , French Marsha 
Published: January 2025

ABSTRACT
Mixed siliciclastic–carbonate mudrocks have variable depositional processes and diagenetic pathways, creating mineralogical complexity and thus difficulty in characterizing reservoir quality using typical subsurface datasets (e.g., well logs) as well as conventional visual core-description techniques. Core-based X-ray fluorescence (XRF) data quantifies subtle elemental variations that can aid in interpreting fine-scale sedimentological packages and variations in reservoir-property distribution. XRF data has proven to be particularly useful for interpreting and defining the depositional processes of muddy, thin-bedded, mixed-lithology successions like the Wolfcamp and Bone Spring formations of the Delaware Basin, Texas, USA. These units consist of early to middle Permian siliciclastic and carbonate deep-marine deposits that form productive unconventional hydrocarbon reservoirs. However, the spatial and temporal variability in depositional processes and diagenetic evolution leads to difficulty in predicting reservoir presence and quality. Several studies have utilized core-based XRF data for this purpose, but not at a resolution sufficient to capture the true heterogeneity of these thin-bedded deposits.
Using continuous, high-resolution (1 cm, 0.39 inch) X-ray fluorescence data from 66 m (218 feet) of core and associated geomechanical and well-log data from the Wolfcamp XY interval, this study demonstrates that chemofacies derived using unsupervised machine learning correlate with event-bed interpretations and reservoir-property distribution. Unsupervised k-means clustering and principal-component analysis on 17 XRF-derived elemental concentrations derive four chemofacies that characterize geochemical heterogeneity: 1) calcareous, 2) detrital, 3) oxic–suboxic argillaceous, and 4) anoxic argillaceous. The mineralogy and paragenesis of these chemofacies are validated using scanning-electron microscopy (SEM) and thin-section petrography. Vertical variations in XRF-based chemofacies accurately represent depositional facies changes and hybrid-event-bed boundaries, often matching cm-by-cm the visually-described lithofacies. We utilize this detailed dataset to construct a predictive chemofacies model linking variable sediment routing from carbonate and siliciclastic sources and various depositional processes to reservoir properties. This research also demonstrates that reservoir properties (e.g., total organic carbon, porosity, permeability, water saturation) and geomechanical response (brittleness and unconfined compressive strength) vary with chemofacies, with argillaceous facies generally being less brittle but having higher porosity. These results can be used for log-based reservoir prediction of the Wolfcamp and Bone Spring formations in the Permian Basin, as well as for other mixed siliciclastic–carbonate deep-water reservoirs around the world.