1. AI/ML for Subsurface Imaging and Interpretation
• ML-based seismic inversion and attribute extraction
• Automated fault, fracture, and stratigraphic interpretation
• Imaging enhancement in complex geological settings
• Uncertainty-aware interpretation using probabilistic AI
2. Reservoir Characterisation and Prediction
• Digital core analysis and rock typing using ML
• AI-enabled petrophysical interpretation workflows
• Probabilistic reservoir modelling and upscaling
• Physics-Informed Neural Networks (PINNs) for constrained property prediction
• Automated facies and reservoir architecture prediction
3. Drilling, Development and Production Optimisation
• Real-time drilling optimisation and hazard prediction
• Intelligent completion and development planning
• AI-driven production surveillance and forecasting
• Autonomous reservoir management concepts
4. AI for Mature Assets and Brownfields
• Identifying bypassed pay using AI-assisted interpretation
• Decline forecasting and life-of-field optimisation
• Intelligent workover and enhanced recovery planning
5, Agentic AI in Subsurface and Geoscience Workflows
• Autonomous interpretation and QC assistants
• Multi-agent collaboration across disciplines
• Hybrid physics–AI agents for reliable decision support
• Governance, trust, and deployment challenges
6. Cloud, Data Platforms & Digital Infrastructure
• Unified subsurface data ecosystems
• Interoperability between legacy and modern AI platforms
• High-performance computing for large-scale models
• Data governance and cybersecurity
7. AI for CCUS and Low-Carbon Subsurface Applications
• Injection plume prediction and monitoring
• Seal integrity and containment assessment
• Risk modelling and scenario evaluation using AI
8. AI Capability Building, Skills & Organisational Readiness
• AI literacy for geoscientists and engineers
• Developing hybrid AI–domain professionals
• Human–AI collaboration models
• Trust, explainability, and validation workflows
• Capability maturity models for AI adoption
• Leadership and cultural transformation
9. Process Transformation and AI-Enabled Ways of Working
• Redesigning interpretation and modelling cycles with AI
• Continuous AI-augmented workflows
• Human-in-the-loop vs human-on-the-loop models
• Integrating AI into technical assurance and governance
• Embedding AI into business decision-making