Carbon Sequestration: Enhanced Evaluation of Uncertainty

Carbon capture and sequestration (CCS) is an option to mitigate impacts of atmospheric carbon emission. Initial studies indicate that for long-term geologic storage of carbon to be effective, the leakage rates must be less than 0.1 - 0.01%/yr. Recent efforts have been made to apply the existing probabilistic performance assessment (PA) methodology developed for deep nuclear waste geologic repositories to evaluate the effectiveness of subsurface carbon storage. However, to address the most pressing management, regulatory, and scientific concerns with subsurface carbon storage (CS), the existing PA methodology and tools must be enhanced and upgraded. For example, in the evaluation of a nuclear waste repository, a PA model is essentially a forward model that samples input parameters and runs multiple realizations to estimate future consequences and determine important parameters driving the system performance. In the CS evaluation, however, a PA model must be able to run both forward and inverse calculations to support real-time site monitoring as an integral part of the design and operational phases. The monitoring data must be continually fused into the PA model through model inversion and parameter estimation. Model calculations will in turn guide the design of optimal monitoring and carbon-injection strategies (e.g., in terms of monitoring techniques, locations, and time intervals). This study formulates the advanced PA concept for CS systems and establishes a prototype PA framework for the concept. The new PA framework includes a built-in optimization capability for model parameterization and monitoring system design. The capabilities of this framework will be demonstrated with a hypothetical CS system. The work lays the foundation for the development of a new generation of PA tools for effective management of CS activities. The work supports energy security and climate change/adaptation by furthering the capability to effectively manage proposed carbon capture and sequestration activities (both research and development as well as operational), and it greatly enhances the technical capability to address this national problem. This work focuses on conceptual development and the feasibility demonstration of the concept. This initial work links an existing reservoir simulator (TOUGH2) with an uncertainty and optimization code (DAKOTA), and then performs PA calculations for a simple hypothetical carbon storage (CS) system. These calculations demonstrate the key capabilities of the PA framework for uncertainty quantification, sensitivity analysis, and system optimization. Preliminary analyses were conducted to optimize natural system parameters including caprock properties, as well as engineering system parameters including the carbon injection rates. The next phase of the work will include more detailed application of the model system to carbon storage systems.