PANACEA · Predicting and monitoring the long-term behavior of CO2 injected in deep geological formations

PANACEA aims at developing methods and tools for improved prediction and validation of the long-term behavior of sequestered CO2. The objectives are to: 1) Identify and quantify the factors responsible for the long-term stability of the stored CO2, thus requiring a clear understanding of the dynamics of the injected CO2, chemical interactions with the host rock, and the evolution in time of its partitioning as a free phase (residual or continuous), dissolved or mineralized; 2) Provide measures for the assessment of the integrity and vulnerability of the reservoir (storage formation and cap-rock) and wells that penetrate it, to leakage of the stored CO2, through the cap-rock, faults and or wells; 3) Quantify the impact of the stored CO2 on adjacent subsurface reservoirs, such as changes in the reservoir parameters (pressure increase, pH changes or other chemical reactions) that may lead to unwanted migration of brines, or the release on pollutants trapped in the rock (such as heavy metals), into adjacent freshwater reservoirs; 4) Identify and/or suggest reliable monitoring, measurement and verification (MMV) technologies having the capability to capture relevant information on the long-term behavior of the stored CO2 both at the near and far field; and 5) Achieve an adequate degree of cooperation with other projects in order to allow the collection of data necessary for validating the investigations, including data from large injection sites (Sleipner, Norway), medium size (the future EEPR partially funded Hontomin project, Spain) and small projects (the MUSTANG Heletz, Israel). International cooperation has been arranged with high-profile institutions from the USA, Canada and Australia that have crucial expertise in geological storage of CO2. The combination of extensive exploitation of the existing datasets on CO2 storage, and the comprehensive modeling activities, will allow cross-model validation and will increase the reliability of the modeling tools.