W-5: Automated Mineral Classification: Transforming Mineralogy into Operational Intelligence

This workshop explores how automated mineral classification has evolved from specialized laboratory analysis into a fast-paced decision engine for subsurface energy workflows. Participants will learn how modern platforms—from SEM-based mapping to LIBS scanning and machine learning—deliver consistent, scalable mineralogical data tied to reservoir quality, geomechanics, and completion design. The course emphasizes practical integration strategies, connecting mineral and texture outputs to operational decisions in petroleum, geothermal, and critical minerals applications through calibration, prediction, and optimization workflows.

Materials Covered and Skills Gained
• Technology Platforms and Capabilities - Review the evolution from SEM-EDS automated mineralogy to LIBS scanning and machine learning-enhanced classification, understanding how each platform delivers mineral maps, modal mineralogy, and texture data at different scales and speeds.
• Operational Integration and Workflow Design - Learn how automated mineral classification fits into subsurface workflows through calibration of petrophysical models, prediction of reservoir quality and geomechanical behavior, and optimization of completion and stimulation strategies. Pore Architecture and Fracture Characterization - Explore how automated classification quantifies pore placement, pore throat controls, and mineral-specific occlusion patterns, while mapping fracture-fill mineralogy and overgrowth fabrics that influence permeability and completion effectiveness.
• Implementation Best Practices and Pilot Programs - Develop practical strategies for launching focused pilots aligned to operational decisions, including sample selection, QC protocols, cross-disciplinary integration, and translating mineral data into actionable subsurface intelligence.

Lead Organizer

Dave Tonner, Diversified Well Logging

Co-Organizer(s)

Susan Nash, AAPG