Carbon Integrity Intelligence: An AI-Driven Framework for Dynamic Regional Governance and High-Fidelity CCUS Operations

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive AI architecture for CCUS monitoring and carbon credit governance, offering a basis for further empirical validation and method development.

🏢実務担当者:Offers a blueprint for integrating AI and digital MMRV systems to enhance operational integrity and streamline carbon credit issuance and verification.

🏛政策担当者:Highlights the importance of interoperable digital infrastructure for credible carbon accounting and suggests regulatory frameworks for AI-enabled MMRV.

The environmental credibility and financial sustainability of Carbon Capture, Utilization, and Storage (CCUS) systems increasingly depend on continuous containment assurance, transparent Monitoring, Measurement, Reporting, and Verification (MMRV), and region-specific carbon credit accountability. Conventional monitoring approaches are often fragmented, reactive, and insufficient to support near real-time leakage attribution, uncertainty quantification, and adaptive carbon market compliance. This study presents an AI-enabled integrated digital framework for high-fidelity CO₂ leakage detection, dynamic modelling, and regionalized carbon credit governance across the CCUS value chain. The framework integrates multi-modal operational, geospatial, and subsurface datasets, including Distributed Acoustic and Temperature Sensing (DAS/DTS), microseismic monitoring, wellhead pressure-temperature measurements, plume migration simulations, soil gas concentrations, integrity surveillance data, geomechanical indicators, and satellite-based emissions observations. These heterogeneous data streams are harmonized through cloud-native digital infrastructure and interoperable data pipelines to establish a unified operational intelligence layer for containment monitoring. A layered analytics architecture combining anomaly detection, Bayesian inference, spatio-temporal machine learning, probabilistic uncertainty modelling, and causal reasoning algorithms is employed to distinguish true CO₂ leakage signatures from natural geological variability and sensor noise. Dynamic reservoir and geomechanical models continuously recalibrate plume migration behaviour, containment confidence, injectivity performance, and leakage probability using near real-time operational inputs. To strengthen operational integrity, the framework incorporates risk-informed recommendation intelligence supported by a Bowtie risk matrix to identify vulnerabilities and recommend prioritized mitigation actions. To address jurisdictional variability in carbon markets, the proposed solution introduces a regionalized carbon credit management capability that dynamically adjusts carbon accounting, reversal risk assessment, and storage assurance metrics based on local regulatory requirements, emissions policies, and market mechanisms. Verified leakage estimates are linked to adaptive carbon credit recalibration, supporting transparent issuance, suspension, or reversal workflows. An API-enabled interoperability layer facilitates secure integration with third-party monitoring vendors, certification agencies, regulators, and digital MMRV platforms, enabling automated approval workflows, traceable data lineage, auditability, and evidence-based verification. By combining AI, dynamic subsurface modelling, recommendation intelligence, and interoperable digital ecosystems, the proposed framework enhances CCUS operational integrity and strengthens trust in carbon credit systems for scalable, long-term decarbonization.

• semanticscholar https://doi.org/10.2118/233046-msfirst seen 2026-08-09 05:48:42