The overall objective of the proposed project is the application of AI-powered products to understand the relationship between subsurface systems utilised for geological storage, primarily CO2, but including competing demands of other streams such as H2, with the design and operation of a regional CO2 Capture, Utilisation and Storage (CCUS) Network. The focus is on tactical dynamic operability of the network in the short term, which has been largely ignored in earlier CCUS supply chain optimisation studies. Such models have been treating subsurface storage as a static box, defined by geological storage type, depth and number of injection wells, where the dynamics conditions of injectivity and storage capacity are ignored. In deep geological storage, the performance of individual wells is especially critical during injection because the operations are likely to be scheduled with a minimum number of injection wells for cost and risk assessment reasons. In several deep CO2 geological storage and CO2-EOR projects, the injectivity was found to be unexpectedly smaller than was anticipated during CO2 injection/floods due to complex interactions which should be included in dynamic network optimisation.