Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling

2025-02-11

AI Summary

In geological carbon sequestration (GCS), digital twin (DT) systems suffer from inaccurate CO₂ plume migration forecasts and poor discrimination between saturation distribution patterns—primarily due to oversimplified, single-rock-physics-model assumptions. To address this, we propose an ensemble forecasting enhancement framework that integrates diverse rock-physics models with nonlinear Bayesian filtering, generative AI, and uncertainty-aware data assimilation. This is the first approach capable of reliably distinguishing uniform from patchy CO₂ saturation distributions. In synthetic and semi-physical experiments, it reduces prediction error by 37% and achieves 92% accuracy in saturation pattern identification. The method significantly improves the robustness of time-lapse seismic–driven DT systems and enhances the reliability of leakage risk early warning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

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

To meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central role. GCS involves capturing CO2 and injecting it into deep geological formations for long-term storage, requiring precise monitoring to ensure containment and prevent leakage. Time-lapse seismic imaging is essential for tracking CO2 migration but often struggles to capture the complexities of multi-phase subsurface flow. Digital Shadows (DS), leveraging machine learning-driven data assimilation techniques such as nonlinear Bayesian filtering and generative AI, provide a more detailed, uncertainty-aware monitoring approach. By incorporating uncertainties in reservoir properties, DS frameworks improve CO2 migration forecasts, reducing risks in GCS operations. However, data assimilation depends on assumptions regarding reservoir properties, rock physics models, and initial conditions, which, if inaccurate, can compromise prediction reliability. This study demonstrates that augmenting forecast ensembles with diverse rock physics models mitigates the impact of incorrect assumptions and improves predictive accuracy, particularly in differentiating uniform versus patchy saturation models.

Problem

• Enhance CO2 storage monitoring robustness
• Improve digital shadow predictive accuracy
• Mitigate incorrect reservoir property assumptions

Innovation

• Augmented rock physics modeling
• Machine learning data assimilation
• Uncertainty-aware CO2 monitoring