Semi-supervised Workflow to Generate Petrofacies Logs from Thin Sections and XRF Data

Core data provide valuable in situ information on the chemical and physical characteristics of subsurface formations. Thin sections, for instance, allow us to define rock types with similar mineral composition, lithologies, and pore types (i.e., petrofacies). Petrofacies logs illustrate the stratigraphic variability within a reservoir and are often used to constrain 3-D facies and petrophysical-property models. However, core data are often scarce, prompting us to explore alternative sources. Well logs, with their different vertical resolution, are commonly used to characterize mineralogy, lithology, and porosity. The challenge lies in reconciling the differences between thin sections and well logs. To address this, we developed novel Machine Learning (ML) methodologies. Our goal was to bridge the resolution gap between these two data types and identify subtle variations in rock properties. Specifically, we focused on collocated cores that provided thin-section-based petrofacies and X-ray fluorescence (XRF) data. Our approach involved two semi-supervised methods: self-training and labeled clustering. By combining XRF data with dimensionality reduction techniques, we achieved a reliable classification of thin-section-based petrofacies. Remarkably, both approaches achieved accuracies exceeding 90% on Sycamore Formation data. Among the dimensionality reduction techniques tested. Uniform Manifold Approximation and Projection (UMAP) yielded the best results. The outcome? Petrofacies logs that bridge the resolution gap between core-based thin sections and well-log data. Furthermore, integrating semi-supervised methods into routine core analysis offers substantial cost and time savings. These methods enhance stratigraphic correlation, aid in identifying target zones, design horizontal wells, and constrain subsurface models.