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dc.contributor.authorAhmed, S.
dc.contributor.authorAlameri, W.
dc.contributor.authorAhmed, Waqas Waseem
dc.contributor.authorKhan, S. A.
dc.date.accessioned2021-03-24T08:14:03Z
dc.date.available2021-03-24T08:14:03Z
dc.date.issued2021-03-12
dc.date.submitted2020-09-16
dc.identifier.citationAhmed, S., Alameri, W., Ahmed, W. W., & Khan, S. A. (2021). Rheological behavior of scCO2-Foam for improved hydrocarbon recovery: Experimental and deep learning approach. Journal of Petroleum Science and Engineering, 203, 108646. doi:10.1016/j.petrol.2021.108646
dc.identifier.issn0920-4105
dc.identifier.doi10.1016/j.petrol.2021.108646
dc.identifier.urihttp://hdl.handle.net/10754/668231
dc.description.abstractCO2 foam as a fracturing fluid for unconventional reservoir has been of huge interest due to its potential in solving various challenges related to conventional water-based fracturing. The rheological property of CO2 foam is a key factor to control the efficiency of fracturing process that is strongly influenced by different process parameters such as foam quality, temperature, pressure, and shear rate. The quantification of these parameters under reservoir conditions leads to the design of optimum injection strategy. However, the traditional modeling approaches are unable to provide fast and accurate prediction while considering combined effect of all these parameters. Here, we proposed a data driven approach based on supervised deep learning to estimate rheological property of CO2 foam as a function of foam quality, temperature, pressure, and shear rate. We exploit deep neural networks (DNNs) that are trained to learn the complex nonlinear aspects of the data. For the data generation, we performed a series of experiments for CO2 foams by varying different process variables. CO2 foams at different qualities were generated using conventional surfactant in a flow loop system and foam viscosity measurements were performed at HPHT under wide range of shear rate. The architecture of DNN was optimized to accurately estimate the foam apparent viscosity for given foam quality, temperature, pressure, and shear rate. The predictive capability of designed network is found to be significantly high, analyzed by regression coefficient approaching unity, low mean squared error, and low average absolute relative deviation (
dc.description.sponsorshipThe authors would like to acknowledge Khalifa University of Science and Technology for financial support under project code (CIRA-2019-002) on machine learning studies. PETRONAS Research Sdn Bhd is also acknowledged for the technical support and the laboratory facilities provided to conduct the rheology experiments.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0920410521003065
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Petroleum Science and Engineering. 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 Journal of Petroleum Science and Engineering, [203, , (2021-03-12)] DOI: 10.1016/j.petrol.2021.108646 . © 2021. 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.titleRheological behavior of scCO2-Foam for improved hydrocarbon recovery: Experimental and deep learning approach
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, United Arab Emirates
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalJournal of Petroleum Science and Engineering
dc.rights.embargodate2023-03-18
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Petroleum Engineering, Khalifa University of Science, Technology and Research (KUSTAR), Abu Dhabi, United Arab Emirates
dc.identifier.volume203
dc.identifier.pages108646
kaust.personAhmed, Waqas Waseem
kaust.personKhan, S. A.
dc.date.accepted2021-03-07
dc.identifier.eid2-s2.0-85102625766
dc.date.published-online2021-03-12
dc.date.published-print2021-08


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