ABOUT THE SYMPOSIUM

The global energy industry is undergoing rapid transformation driven by cost pressures, maturing assets, decarbonisation, and increasing expectations for technical certainty. In this environment, artificial intelligence (AI) and machine learning (ML) have moved from experimental tools to strategic capabilities that can fundamentally reshape subsurface decision-making.

The EAGE Symposium on Subsurface Intelligence: Harnessing AI/ML to Maximise Asset Value is designed as a high-impact technical forum bringing together geoscientists, reservoir engineers, data scientists, technology providers, and researchers to explore how AI can be sustainably embedded into subsurface workflows — from exploration and development to production optimisation and carbon storage. Successful AI adoption requires more than tools. It demands new skills, new workflows, and new ways of working. This Symposium also focuses on the people and process dimensions of AI transformation alongside technology.

VISION AND PURPOSE

• Highlight state-of-the-art AI/ML applications across subsurface and geoscience domains
• Demonstrate how AI-driven intelligence unlocks value in brownfields, frontier plays, and CCUS projects 
• Bridge domain expertise and data science through collaborative methodologies
• Address AI adoption as a socio-technical transformation of People, Process, and Technology
• Share success stories, failures, and lessons learned from real-world deployments
• Explore emerging frontiers including agentic AI and physics-bounded AI systems
• Support capability building and skills transformation for sustainable AI adoption

Key Topics will Include:

1. AI/ML for Subsurface Imaging and Interpretation
2. Reservoir Characterisation and Prediction
3. Drilling, Development and Production Optimisation
4. AI for Mature Assets and Brownfields
5. Agentic AI, Process Transformation and AI-Enabled Geoscience Workflows
6. Cloud, Data Platforms & Infrastructure & AI Readiness
   
7. AI for CCUS and Low-Carbon Subsurface Applications