H51U-07
Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory

Friday, 18 December 2015: 09:30
3018 (Moscone West)
Matthew Grasinger, Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, NM, United States; University of Pittsburgh Pittsburgh Campus, Civil and Environmental Engineering, Pittsburgh, PA, United States
Abstract:
Carbon capture and sequestration has the potential to reduce greenhouse gas
emissions. However, care must be taken when choosing a site for CO2 seques-
tration to ensure that the CO2 remains sequestered for many years, and that
the environment is not harmed in any way. Making a rational decision be-
tween potential sites for sequestration is not without its challenges because, as
in the case of many environmental and subsurface problems, there is a lot of
uncertainty that exists. A method for making decisions under various types
and severities of uncertainty, Bayesian-Information-Gap Decision Theory (BIG
DT), is presented. BIG DT was coupled with a numerical model for CO2 well
injection and the resulting framework was then applied to a problem of selecting
between two potential sites for CO2 sequestration. The results of the analysis
are presented, followed by a discussion of the decision process.