Premium
Uncertainty quantification for the reliability of the analytical analysis for the simplified model of CO 2 geological sequestration
Author(s) -
Bao Jie,
Xu Zhijie,
Fang Yilin
Publication year - 2015
Publication title -
greenhouse gases: science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 32
ISSN - 2152-3878
DOI - 10.1002/ghg.1436
Subject(s) - reliability (semiconductor) , monte carlo method , range (aeronautics) , computer science , work (physics) , uncertainty quantification , measure (data warehouse) , mathematical optimization , reliability engineering , mathematics , statistics , data mining , engineering , physics , machine learning , aerospace engineering , mechanical engineering , power (physics) , quantum mechanics
A hydromechanical model with analytical solutions including pressure evolution and geomechanical deformation for geological CO 2 injection and sequestration were introduced in our previous work. However, the reliability and accuracy of the hydromechanical model and the companion analytical solution are uncertain because of the assumptions and simplifications in the analytical model, though it was validated by a few example cases. This study introduces a method to efficiently measure the accuracy of the analytical model and specify the range of acceptable input parameters that can guarantee the accuracy and reliability of the analytical solution. A coupled hydro‐geomechanical subsurface transport simulator, Subsurface Transport over Multiple Phases (STOMP), was adopted as a reference to justify the reliability of the hydromechanical model and the analytical solution. A quasi‐Monte Carlo sampling method was applied to efficiently sample the input parameter space.