Harnessing Digital Innovation in CCUS: AI-Driven Solutions for Real-Time Monitoring and Risk Management

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

🔬研究者:Researchers can use the comprehensive overview of AI and monitoring technologies to identify research gaps and develop advanced algorithms for real-time risk assessment in CCUS.

🏢実務担当者:CCUS operators can apply the AI-driven monitoring systems to enhance operational safety, reduce downtime, and ensure regulatory compliance.

🏛政策担当者:Policymakers can reference this paper to understand the technological readiness of digital CCUS solutions and consider incentives for AI integration in carbon management strategies.

The digitalization of Carbon Capture, Utilization, and Storage (CCUS) technologies is significantly transforming the management of carbon emissions, process optimization, and operational safety. With growing global emphasis on achieving stringent climate targets, the need for highly efficient, scalable, and reliable CCUS solutions has never been more critical. The primary aim of this research is to provide a comprehensive overview of the cutting-edge digital innovations that are advancing the CCUS sector, particularly those technologies designed to improve system performance, safety, and data-driven decision-making. The study delves into key technological advancements such as Monitoring, Measurement, Reporting, and Verification (MMRV) systems, fiber-optic sensing, seismic processing, pipeline integrity monitoring, and CO2 capture monitoring, all of which are pivotal to addressing real-time operational challenges in CCUS projects. These technologies leverage AI algorithms and advanced analytics to enable dynamic alerts, predictive maintenance, and enhanced risk management, thereby optimizing the long-term viability of CCUS systems. To achieve this, the research employs a combination of literature review, case study analysis, and empirical data from leading global CCUS initiatives. The paper explores how these digital tools are applied across the entire CCUS value chain from CO2 capture technologies that continuously monitor separation efficiencies, to transportation and storage solutions that ensure pipeline integrity and detect potential leaks. AI-driven systems play a crucial role in monitoring subsurface conditions, providing early warnings of storage site risks such as CO2 migration or compromised well integrity. The integration of real-time monitoring data from fiber-optic sensors and seismic technology facilitates highly sensitive detection of anomalies. Advanced algorithms process vast amounts of data to support informed decision-making along with being cost-efficient. This technology is inherently capable of simultaneously measuring a range of physical parameters, which can be integrated to capture the necessary spatial and temporal resolutions. Key capabilities include DAS, DTS, DSS, pressure sensing, surface metering, and more. The potential impact of these innovations is substantial. The application of AI-powered digital tools in CCUS operations allows for greater predictive accuracy, minimizes system downtime, and mitigates operational risks. Real-time monitoring and dynamic alert systems enable a proactive approach to risk management, ensuring operational safety and regulatory compliance. Moreover, continuous CO2 capture monitoring drives optimization of separation efficiency, reducing both energy consumption and operational costs, all this helps to make CCUS technologies more economically viable.

• semanticscholar https://doi.org/10.2118/231066-msfirst seen 2026-05-05 23:41:24