Carbon capture and sequestration (CCS) is an option to mitigate impacts of atmospheric carbon
emission. Numerous factors are important in determining the overall effectiveness of long-term
geologic storage of carbon, including leakage rates, volume of storage available, and system
costs. Recent efforts have been made to apply an existing probabilistic performance assessment
(PA) methodology developed for deep nuclear waste geologic repositories to evaluate the
effectiveness of subsurface carbon storage (Viswanathan et al., 2008; Stauffer et al., 2009).
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 optimization of CO2 injection and real-time site monitoring as an integral part of the
system design and operation. 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).
Under the support of Laboratory-Directed Research & Development (LDRD), a late-start LDRD
project was initiated in June of Fiscal Year 2010 to explore the concept of an enhanced
performance assessment system (EPAS) for carbon sequestration and storage. In spite of the tight
time constraints, significant progress has been made on the project:
(1) Following the general PA methodology, a preliminary Feature, Event, and Process
(FEP) analysis was performed for a hypothetical CS system. Through this FEP analysis,
relevant scenarios for CO2 release were defined.
(2) A prototype of EPAS was developed by wrapping an existing multi-phase, multicomponent
reservoir simulator (TOUGH2) with an uncertainty quantification and
optimization code (DAKOTA).
(3) For demonstration, a probabilistic PA analysis was successfully performed for a
hypothetical CS system based on an existing project in a brine-bearing sandstone. The
work lays the foundation for the development of a new generation of PA tools for
effective management of CS activities.
At a top-level, 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.
The next phase of the work will include (1) full capability demonstration of the EPAS, especially
for data fusion, carbon storage system optimization, and process optimization of CO2 injection,
and (2) application of the EPAS to actual carbon storage systems.