スパース地震探査・3D/4D地震探査・VSPデータを統合したデジタルCCUSモニタリング

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🔬研究者:Provides a holistic integration approach for multi-scale seismic data in CCUS monitoring, advancing subsurface characterization methods.

🏢実務担当者:Offers a digital architecture to improve monitoring efficiency and regulatory confidence for CCUS projects.

🏛政策担当者:Highlights the importance of advanced monitoring technologies for ensuring safe and reliable CO2 storage, informing regulatory frameworks.

This work presents a digital Carbon Capture, Utilization and Storage (CCUS) subsurface framework designed to integrate sparse seismic, 3D and 4D seismic, and Vertical Seismic Profiling (VSP) datasets into a scalable digital software system. The framework addresses key challenges in CO2 storage characterization, monitoring, and verification, including data sparsity, uncertainty management, and real time decision support. By combining advanced analytics, visualization, and artificial intelligence and machine learning (AI and ML) capabilities within a unified digital architecture, the system enhances subsurface understanding, plume tracking, risk mitigation, and regulatory confidence across the CCUS lifecycle, from site screening to long term storage assurance. The proposed methodology integrates multi scale seismic data using a common subsurface data model that harmonizes sparse seismic surveys, legacy 3D datasets, time lapse 4D seismic, and borehole based VSP measurements. This model enables spatial and temporal alignment, uncertainty quantification, and metadata rich ingestion pipelines. AI and ML techniques, including supervised learning, physics informed neural networks, and anomaly detection, improve seismic interpretation, interpolate sparse measurements, and predict CO2 plume evolution across varying reservoir conditions. Advanced analytics workflows support automated seismic attribute extraction, change detection between baseline and monitor surveys, and probabilistic risk assessment of potential leakage pathways. Visualization layers deliver interactive 3D and 4D representations of seismic volumes, CO2 saturation changes, and uncertainty envelopes, allowing experts to explore results intuitively. The digital architecture is modular and cloud enabled, facilitating integration with reservoir simulation, geomechanical models, and monitoring systems. This design supports scalable deployment, continuous learning, and closed loop optimization of monitoring strategies. The novelty of this work lies in the holistic integration of sparse seismic, 3D and 4D seismic, and VSP data within a purpose built CCUS digital ecosystem, moving beyond traditional isolated interpretation workflows. The framework incorporates AI and ML tightly coupled with subsurface physics and uncertainty aware analytics. By embedding visualization and decision support capabilities directly in the digital architecture, the approach connects seismic data processing, subsurface interpretation, and operational CCUS decision making. This enables a transition from project specific analyses to persistent and adaptive digital subsurface systems for CCUS. In summary, the digital integration of seismic and VSP data through AI enabled architectures can substantially improve subsurface characterization for CCUS. The proposed framework supports scalable, transparent, and defensible monitoring and verification, contributing to safer and more reliable CO2 storage operations and helping accelerate deployment of CCUS at industrial scale.

• semanticscholar https://doi.org/10.1109/icgea69553.2026.11642843first seen 2026-08-21 04:59:56 · last seen 2026-09-22 05:04:30
• scopus https://api.elsevier.com/content/abstract/scopus_id/105048464379first seen 2026-09-02 05:55:04 · last seen 2026-09-14 05:38:25