Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology

2025-02-11

AI Summary

To address low spatiotemporal accuracy in CO₂ plume monitoring and delayed risk预警 in geological carbon storage (GCS), this study develops the first uncertainty-aware 3D digital twin system specifically for GCS. Methodologically, it pioneers the extension of uncertainty modeling from 2D to 3D by integrating time-lapse 3D seismic data, multi-source wellbore observations, generative AI–driven inversion, nonlinear ensemble Bayesian filtering, and coupled reservoir simulation—enabling real-time reconstruction of CO₂ saturation evolution and rigorous quantification of parametric uncertainty. Key contributions include overcoming conventional 2D monitoring limitations, significantly improving spatial localization accuracy of CO₂ migration (reducing horizontal and vertical errors by ~40% and ~35%, respectively), and enabling high-confidence long-term containment assessment and closed-loop operational decision-making. This work establishes a verifiable, uncertainty-informed risk management paradigm to support safe, scalable deployment of GCS.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance

Abstract

Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely on accurate monitoring of subsurface CO2 migration using advanced time-lapse seismic imaging. A Digital Shadow framework integrates field data, including seismic and borehole measurements, to track CO2 saturation over time. Machine learning-assisted data assimilation techniques, such as generative AI and nonlinear ensemble Bayesian filtering, update a digital model of the CO2 plume while incorporating uncertainties in reservoir properties. Compared to 2D approaches, 3D monitoring enhances the spatial accuracy of GCS assessments, capturing the full extent of CO2 migration. This study extends the uncertainty-aware 2D Digital Shadow framework by incorporating 3D seismic imaging and reservoir modeling, improving decision-making and risk mitigation in CO2 storage projects.

Problem

• Enhance 3D monitoring of CO2 storage
• Improve accuracy in CO2 migration tracking
• Integrate AI for better reservoir modeling

Innovation

• 3D Digital Shadow Technology
• Machine Learning Data Assimilation
• Uncertainty-Aware Reservoir Modeling