Show simple item record

dc.contributor.authorSaad, Bilal Mohammed
dc.contributor.authorAlexanderian, Alen
dc.contributor.authorPrudhomme, Serge
dc.contributor.authorKnio, Omar
dc.date.accessioned2017-09-21T09:25:34Z
dc.date.available2017-09-21T09:25:34Z
dc.date.issued2017-09-18
dc.identifier.citationSaad BM, Alexanderian A, Prudhomme S, Knio OM (2017) Probabilistic modeling and global sensitivity analysis for CO 2 storage in geological formations: a spectral approach. Applied Mathematical Modelling. Available: http://dx.doi.org/10.1016/j.apm.2017.09.016.
dc.identifier.issn0307-904X
dc.identifier.doi10.1016/j.apm.2017.09.016
dc.identifier.urihttp://hdl.handle.net/10754/625502
dc.description.abstractThis work focuses on the simulation of CO2 storage in deep underground formations under uncertainty and seeks to understand the impact of uncertainties in reservoir properties on CO2 leakage. To simulate the process, a non-isothermal two-phase two-component flow system with equilibrium phase exchange is used. Since model evaluations are computationally intensive, instead of traditional Monte Carlo methods, we rely on polynomial chaos (PC) expansions for representation of the stochastic model response. A non-intrusive approach is used to determine the PC coefficients. We establish the accuracy of the PC representations within a reasonable error threshold through systematic convergence studies. In addition to characterizing the distributions of model observables, we compute probabilities of excess CO2 leakage. Moreover, we consider the injection rate as a design parameter and compute an optimum injection rate that ensures that the risk of excess pressure buildup at the leaky well remains below acceptable levels. We also provide a comprehensive analysis of sensitivities of CO2 leakage, where we compute the contributions of the random parameters, and their interactions, to the variance by computing first, second, and total order Sobol’ indices.
dc.description.sponsorshipResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) under the Academic Excellency Alliance (AEA) UT Austin-KAUST project ”Uncertainty quantification for predictive modeling of the dissolution of porous and fractured media”. Computational resources for the simulations presented in this publication have been made available by KAUST Research Computing and KAUST SuperComputing Lab. Bilal Saad is grateful for the support by the Saudi Arabia Basic Industries Corporation (SABIC). Bilal Saad, Serge Prudhomme, and Omar Knio are also participants of the KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0307904X1730567X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Applied Mathematical Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Mathematical Modelling, [, , (2017-09-18)] DOI: 10.1016/j.apm.2017.09.016 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCarbon sequestration
dc.subjectMultiphase flow
dc.subjectRisk assessment
dc.subjectParametric Uncertainty
dc.subjectPolynomial Chaos
dc.subjectSensitivity analysis
dc.titleProbabilistic modeling and global sensitivity analysis for CO 2 storage in geological formations: a spectral approach
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalApplied Mathematical Modelling
dc.eprint.versionPost-print
dc.contributor.institutionThe Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
dc.contributor.institutionDepartment of Mathematics, North Carolina State University, Raleigh, NC, USA
dc.contributor.institutionDepartment of Mathematical and Industrial Engineering, École Polytechnique de Montréal, Montréal, Canada
dc.contributor.institutionDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA
dc.identifier.arxividarXiv:1602.02736
kaust.personSaad, Bilal Mohammed
kaust.personKnio, Omar


Files in this item

Thumbnail
Name:
1-s2.0-S0307904X1730567X-main.pdf
Size:
5.036Mb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record