Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics
Greenhouse gas emission (GHG) has become a serious threat to the environment due toemitting high CO2 concentration and thus depleting ozone layer. One of the potential mitigations is using carbon sequestration process by injecting CO2 back to underground. However, the stored CO2 has a potential to l...
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ump-76992018-05-15T04:08:37Z http://umpir.ump.edu.my/id/eprint/7699/ Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics Rahman, Abdul Haziq Abdul Abdullah, Bawadi Vo, Dai-Viet N. TP Chemical technology Greenhouse gas emission (GHG) has become a serious threat to the environment due toemitting high CO2 concentration and thus depleting ozone layer. One of the potential mitigations is using carbon sequestration process by injecting CO2 back to underground. However, the stored CO2 has a potential to leakage from the storage through the wellbore, due to reaction of between supercritical CO2 and cement plug. The purpose of this research is to discover the potential of CO2 leakage from abandon well, by analyzing the reaction of CO2 with Portland cement and the effect of permeability, pressure, temperature and inflow velocity towards the reaction using Computational Fluid Dynamics (CFD). The results show that for base case scenario, the penetration rate of CO2 in cement plug is 150 mm in 100 years. The sensitivity analysis shows that permeability, pressure, temperature and inflow velocity has impacts on the CO2 penetration. Trans Tech Publications 2014 Article PeerReviewed Rahman, Abdul Haziq Abdul and Abdullah, Bawadi and Vo, Dai-Viet N. (2014) Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics. Applied Mechanics and Materials, 625. pp. 780-783. ISSN 1662-7482 http://dx.doi.org/10.4028/www.scientific.net/AMM.625.780 DOI: 10.4028/www.scientific.net/AMM.625.780 |
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TP Chemical technology Rahman, Abdul Haziq Abdul Abdullah, Bawadi Vo, Dai-Viet N. Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics |
description |
Greenhouse gas emission (GHG) has become a serious threat to the environment due toemitting high CO2 concentration and thus depleting ozone layer. One of the potential mitigations is using carbon sequestration process by injecting CO2 back to underground. However, the stored CO2 has a potential to leakage from the storage through the wellbore, due to reaction of between supercritical CO2 and cement plug. The purpose of this research is to discover the potential of CO2 leakage from abandon well, by analyzing the reaction of CO2 with Portland cement and the effect of permeability, pressure, temperature and inflow velocity towards the reaction using Computational Fluid Dynamics (CFD). The results show that for base case scenario, the penetration rate of CO2 in cement plug is 150 mm in 100 years. The sensitivity analysis shows that permeability, pressure, temperature and inflow velocity has impacts on the CO2 penetration. |
format |
Article |
author |
Rahman, Abdul Haziq Abdul Abdullah, Bawadi Vo, Dai-Viet N. |
author_facet |
Rahman, Abdul Haziq Abdul Abdullah, Bawadi Vo, Dai-Viet N. |
author_sort |
Rahman, Abdul Haziq Abdul |
title |
Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics |
title_short |
Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics |
title_full |
Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics |
title_fullStr |
Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics |
title_full_unstemmed |
Modelling of Carbon Dioxide Leakage in Abandon Wells using Computational Fluid Dynamics |
title_sort |
modelling of carbon dioxide leakage in abandon wells using computational fluid dynamics |
publisher |
Trans Tech Publications |
publishDate |
2014 |
url |
http://umpir.ump.edu.my/id/eprint/7699/ http://umpir.ump.edu.my/id/eprint/7699/ http://umpir.ump.edu.my/id/eprint/7699/ |
first_indexed |
2023-09-18T22:04:34Z |
last_indexed |
2023-09-18T22:04:34Z |
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1777414622252367872 |