Senior Scientist
May 30
Carbon dioxide (CO2) capture, utilization and storage (CCUS) is one of the most promising technologies to mitigate the anthropogenic emissions of CO2 and resulting climate change. One of the biggest advantages of CCUS is it can be deployed at large-scale and at different locations. Geologic CO2 storage (GCS) is a critical component of CCUS technology. Globally, there are multiple geologic storage options including saline aquifers as well as depleted hydrocarbon reservoirs that have the appropriate features and characteristics necessary for injecting large quantities of CO2 (10s – 100s million tons) and storing it over long time periods (100s – 1000s years). Multiple commercial scale CCUS projects are currently in operations and multiple are in the planning stages globally. One of the critical components for wider acceptance of GCS is ensuring and demonstrating its safety over long time. The potential geologic CO2 storage sites currently under consideration primarily include deep saline aquifers, depleted oil/gas reservoirs and deep un-mineable coal seams, etc. These geologic systems are inherently heterogeneous and sites with saline aquifers have limited to no characterization data. Effective risk management decisions to ensure safe, long-term CO2 storage requires assessing and quantifying risks while taking into account the uncertainties in a storage site’s characteristics. The key decisions are typically related to definition of area of review, effective monitoring strategy and monitoring duration, potential of leakage and associated impacts, etc. A quantitative methodology for predicting a sequestration site’s long-term performance is critical for making key decisions necessary for successful deployment of commercial scale geologic storage projects where projects will require quantitative assessments of potential long-term liabilities. An integrated assessment modeling (IAM) paradigm which treats a geologic CO2 storage site as a system made up of various linked subsystems can be used to predict long-term performance. The subsystems include storage reservoir, seals, potential leakage pathways (such as wellbores, natural fractures/faults) and receptors (such as shallow groundwater aquifers). CO2 movement within each of the subsystems and resulting interactions are captured through reduced order models (ROMs). The ROMs capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. The computational efficiency allows for performing Monte Carlo simulations necessary for quantitative probabilistic risk assessment. We have used the IAM to predict long-term performance of geologic CO2 sequestration systems and to answer questions related to probability of leakage of CO2 through wellbores, impact of CO2/brine leakage into shallow aquifer, etc. Answers to such questions are critical in making key risk management decisions. This talk will give an overview of the IAM approach and example showing its application to field projects.
