The Energy & Environmental Research Center (EERC) at the University of North Dakota is developing new, real-time-datacapable workflows designed to automate the integration of carbon dioxide (CO₂) storage site monitoring data within an intelligent monitoring system (IMS). The algorithms and workflows developed are capable of handling both periodic and real-time data. Compared with traditional manual processing, interpretation, and integration workflows, the software allows a CO₂ storage site operator to more efficiently monitor and manage a site’s evolving risk profile. If incorporated into an active reservoir management control system, the software will allow for definition of risk trigger points based on user-defined criteria. These triggers can hence be used to automate field operations, such as flow rates within specified injection zones to optimize storage performance and efficiency and/or reduce the project’s risk profile while simultaneously minimizing human error and operational response times. This is being accomplished by integrating continuous monitoring data (such as pressure, temperature, and injection rate measurements), periodic monitoring data (such as seismic data, well logs, gravity surveys, and near-surface monitoring data), and reservoir performance simulations (which have been improved with monitoring data) with software algorithms linked to a technical user interface for visualization and real-time decision-making support.