W-6: Autonomous Energy Geoscience: Digital Twins, Physics-Informed AI, and Intelligent Automation

This workshop explores digital twins, physics-informed AI, and autonomous systems revolutionizing energy geoscience. Participants examine real-time reservoir models coupled with machine learning for closed-loop control in drilling, production, and carbon storage. Sessions cover physics-ML hybrid modeling, agentic AI workflows, and field deployment case studies. Attendees gain practical insights into implementing autonomous technologies that reduce costs, improve safety, and accelerate sustainable energy development while addressing key technical challenges and opportunities in intelligent automation for subsurface operations.

Materials Covered and Skills Gained
1. Digital Twin Foundations and Real-Time Monitoring Explore how dynamic virtual replicas of reservoirs and wells integrate continuously updated physics-based models with sensor data to enable real-time visualization and control of subsurface operations.
2. Physics-Informed Machine Learning for Subsurface Modeling Learn hybrid approaches that combine domain physics with machine learning to achieve orders-of-magnitude computational speed-ups while preserving physical realism for rapid decision-making.
3. Agentic AI Systems and Autonomous Workflows Examine intelligent agents and multi-agent systems that coordinate data analysis, modeling, and decision-making with minimal human intervention across complex geoscience tasks.
4. Field Applications and Implementation Strategies Review case studies from automated drilling, carbon storage monitoring, and geothermal operations while addressing practical challenges in deploying autonomous systems in real-world energy projects.

Lead Organizer

Sashi Gunturu, Petrabytes

Co-Organizer(s)

Susan Nash, AAPG