Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring

2025-01-30

AI Summary

Addressing the challenges of dynamic CO₂ plume monitoring and uncertainty quantification in carbon capture and storage (CCS), this paper proposes a probabilistic joint inversion method. Within a Bayesian inference framework, we construct a shared generative model to simultaneously invert multi-temporal seismic datasets, jointly estimating reservoir petrophysical parameters and the spatiotemporal evolution of the CO₂ plume—including its full posterior distribution. Unlike conventional deterministic joint reservoir modeling (JRM), our approach inherently embeds uncertainty quantification into the inversion workflow, enabling rigorous confidence assessment—a capability previously unavailable in standard JRM. The method significantly enhances the reliability of plume location and extent predictions and produces interpretable, probabilistic risk maps. These outputs provide both theoretical foundations and practical tools for CCS containment integrity assessment and risk-informed decision-making.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

Reducing CO$_2$ emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO$_2$ emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO$_2$ plume dynamics and reservoir properties. Building on existing seismic imaging methods like the Joint Recovery Method (JRM), which lacks uncertainty quantification, we propose the Probabilistic Joint Recovery Method (pJRM). By estimating posterior distributions across surveys using a shared generative model, pJRM provides uncertainty information to improve risk assessment in CCS projects.

Problem

• Carbon Capture and Storage (CCS)
• Flow Behavior
• Risk Assessment

Innovation

• Probabilistic Joint Recovery Method (pJRM)
• Carbon Capture and Storage (CCS)
• Uncertainty Assessment