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dc.contributor.authorMansi, Moataz Khamees Abdelfattah
dc.contributor.supervisorSam Xieen_US
dc.contributor.supervisorChristopher Lagaten_US
dc.date.accessioned2023-05-09T04:08:41Z
dc.date.available2023-05-09T04:08:41Z
dc.date.issued2023en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/91978
dc.description.abstract

CO2 geological storage is an important means to decarbonize the economy, but also expensive in field applications. CO2 injection into shale gas reservoirs can significantly increase the economic incentive by enhancing shale gas production through CO2 preferential adsorption in shales. This work presents a practical framework to evaluate and predict the CO2 adsorption process and CH4 production in shales through a combination of numerical reservoir simulation and supervised machine learning.

en_US
dc.publisherCurtin Universityen_US
dc.titleEvaluation of CO2 Injection in Shale Gas Reservoirs through Numerical Reservoir Simulation and Supervised Machine Learningen_US
dc.typeThesisen_US
dcterms.educationLevelMPhilen_US
curtin.departmentWASM: Minerals, Energy and Chemical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidMansi, Moataz Khamees Abdelfattah [0000-0002-5768-1949]en_US


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