Automated CO2 Risk Detection and Mitigation through Real-Time Data Integration

Abstract The effective monitoring of CO2 emissions and the implementation of robust risk mitigation strategies are pivotal in the context of carbon capture, utilization, and storage (CCUS) systems. This paper introduces an advanced, integrated framework for the real-time detection, assessment, and mitigation of CO2-related risks, leveraging a comprehensive risk management dataset. This dataset includes detailed attributes such as hazard types, mitigation protocols, remediation procedures, impact assessments, geospatial data, control measures, and the specific rules that trigger risk events. By systematically ingesting this data into a centralized datastore, we enable a dynamic, real-time risk management interface. The proposed system incorporates real-time field sensor data, continuously monitoring parameters such as CO2 concentration, pressure, and other relevant environmental variables. Through seamless integration, the system autonomously identifies risk events by detecting deviations from predefined safety thresholds, such as CO2 leaks or equipment malfunctions. Once a risk is detected, the system generates automatic alerts and initiates a cascade of predefined actions, which include immediate risk identification, severity evaluation, and automatic execution of remediation plans. This integrated approach enhances operational resilience by providing continuous situational awareness, enabling a proactive risk response without the need for manual intervention. Upon risk detection, the system autonomously triggers the corresponding mitigation strategy, ensuring that the appropriate remedial actions are promptly undertaken. The closed-loop design of the system allows for ongoing risk assessment, real-time updates, and agile responses, all of which contribute to minimizing the operational and environmental impacts associated with CO2 emissions. The proposed framework exemplifies a transformative solution for CO2 monitoring and risk mitigation, facilitating safer and more efficient CCUS operations. By harnessing the power of real-time sensor data, advanced data analytics, and automated decision-making, the system ensures swift risk identification, assessment, and mitigation. Ultimately, this research underscores the potential of an entirely automated risk management ecosystem that integrates historical risk intelligence with real-time operational data, thereby safeguarding CCUS infrastructure and ensuring regulatory compliance.