Advanced electromagnetic monitoring for geologic CO2 storage with measurement enhancement with improved anomaly resolution

Accurate monitoring of geological CO2 storage is critical for verifying containment and tracking the movement of injected CO2. Current electromagnetic (EM) monitoring methods face significant challenges due to high levels of electromagnetic noise in industrial areas and the complex anisotropic nature of geological formations, which hinder the precise detection and imaging of CO2 plumes. We are addressing this problem by developing advanced EM monitoring technologies that incorporate noise-compensated electromagnetic sensors using adaptive noise cancellation, integration of surface and borehole measurements, and accounting for anisotropy in advanced 3D modeling. Our approach includes real-time data processing workflows enhanced with automation and AI integration, enabling near-real-time quality assurance and improved anomaly detection. This comprehensive solution aims to enhance the accuracy and effectiveness of CO2 plume monitoring in high-noise environments by suppressing environmental and industrial noise, integrating diverse data sources, and utilizing sophisticated modeling techniques. In Phase I, we will focus on: • Designing and developing prototypes of noise-compensated EM sensors that significantly improve performance in high-noise environments by suppressing ambient electromagnetic interference before sensing. • Extending borehole measurement capabilities by upgrading our existing shallow borehole sensors from 30 meters to 100 meters depth, enhancing the resolution and sensitivity of subsurface measurements. • Integrating surface and borehole data in our real-time, cloud-based data acquisition and processing system to provide a more comprehensive understanding of subsurface conditions. • Incorporating anisotropy into advanced 3D modeling, allowing for more accurate representation of complex geological formations and improving the precision of CO2 plume imaging. • Enhancing real-time data handling workflows, including automation and AI integration, to accelerate data processing and interpretation, enabling swift decision-making in CO2 storage management. • Assessing operational durability by designing sensors to meet IP67 standards for dust and water resistance, ensuring reliable long-term functionality under various environmental conditions. These efforts are expected to improve CO2 plume anomaly detection sensitivity by more than ten times and reduce monitoring costs by 20-30% through faster data processing and improved data quality. Advancing into Phase II and beyond, the project will deliver EM monitoring technologies with substantial technical and economic benefits. Enhanced monitoring capabilities will improve the accuracy of CO2 plume detection and imaging, supporting the broader adoption of carbon capture and storage technologies. Accurate tracking of CO2 plumes reduces uncertainties in storage assessments, making carbon sequestration more efficient and cost-effective for industries. The improved technologies will also support the transition to sustainable energy practices by enabling better management of CO2 emissions, contributing to climate change mitigation efforts.