Geologic CO₂ storage is essential for achieving net-zero, but success depends on finding the right sites, injecting safely, and ensuring permanent containment. AI is transforming every step, from site selection to long-term monitoring.
🎯 How AI Can Make This Product or Solution Much Better
🗺 Optimized Site Selection for Geologic Storage
AI processes seismic surveys, well logs, and reservoir models to pinpoint high-capacity, low-risk formations like saline aquifers, depleted oil & gas fields, and basalt layers, cutting exploration time and leakage risk.
⚙ Dynamic Injection Management
Machine learning predicts pressure buildup, plume migration, and CO₂ – brine interactions in real time, ensuring safe injection below fracture limits.
🛰 Leakage Detection & Monitoring
AI fuses satellite imagery, subsurface sensors, and microseismic data to identify potential leaks early and trigger mitigation actions immediately.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist🕰 Long-Term Storage Security Prediction
Predictive models simulate mineralization rates and subsurface fluid dynamics over decades, providing confidence for regulatory and liability requirements.
🔗 Integration with Capture & Transport Networks
AI matches capture facilities with optimal storage sites, balancing pipeline capacity, injection rates, and schedules for cost-effective deployment.
🛠 How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| Uncertain subsurface geology | Integrates multi-source geoscience to reduce risk in site selection |
| Risk of CO₂ leakage | AI-enabled monitoring detects anomalies before significant release |
| High monitoring costs | Uses autonomous UAVs, remote sensing, and smart sensors to cut costs by up to 40% |
| Regulatory compliance | Automated MRV reporting for government and carbon market requirements |
🤖 Main AI Tools and Concepts Used
- Geospatial AI for screening storage basins
- Digital twins of reservoirs for simulation
- ML-based seismic interpretation
- Time-series anomaly detection for monitoring
- Predictive geomechanics modeling
📊 Case Studies
- Sleipner Project (Norway) – AI seismic analysis improved CO₂ plume tracking by 20%.
- Quest CCS (Canada) – ML optimized injection rates to maintain capacity with lower pressure.
🚀 Relevant Startups & Providers
| Company | Focus |
|---|---|
| Geoteric (UK) | AI seismic interpretation for CO₂ reservoir mapping |
| Satelytics (USA) | Satellite-based leak detection for storage facilities |
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