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

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.