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dc.contributor.authorZhang, Tao
dc.contributor.authorLi, Yiteng
dc.contributor.authorSun, Shuyu
dc.contributor.authorBai, Hua
dc.date.accessioned2020-09-27T05:38:58Z
dc.date.available2020-09-27T05:38:58Z
dc.date.issued2020-09-15
dc.date.submitted2019-12-29
dc.identifier.citationZhang, T., Li, Y., Sun, S., & Bai, H. (2020). Accelerating flash calculations in unconventional reservoirs considering capillary pressure using an optimized deep learning algorithm. Journal of Petroleum Science and Engineering, 195, 107886. doi:10.1016/j.petrol.2020.107886
dc.identifier.issn0920-4105
dc.identifier.doi10.1016/j.petrol.2020.107886
dc.identifier.urihttp://hdl.handle.net/10754/665295
dc.description.abstractAn increasing focus was placed in the past few decades on accelerating flash calculations and a variety of acceleration strategies have been developed to improve its efficiency without serious compromise in accuracy and reliability. Recently, as machine learning becomes a powerful tool to handle complicated and time-consuming problems, it is increasingly appealing to replace the iterative flash algorithm, due to the strong nonlinearity of flash problem, by a neural network model. In this study, an NVT flash calculation scheme is established with a thermodynamically stable evolution algorithm to generate training and testing data for the proposed deep neural network. With a modified network structure, the deep learning algorithm is optimized by carefully tuning neural network hyperparameters. Numerical tests indicate that the trained model is capable of accurately estimating phase compositions and states for complex reservoir fluids under a wide range of environmental conditions, while the effect of capillary pressure can be captured well. Thermodynamic rules are preserved well through our algorithm, and the trained model can be used for various fluid mixtures, which significantly accelerates flash calculations in unconventional reservoirs.
dc.description.sponsorshipThe authors thank for the support from the National Natural Science Foundation of China (No. 51874262,51936001) and the Research Funding from King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01-01.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0920410520309438
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, [195, , (2020-09-15)] DOI: 10.1016/j.petrol.2020.107886 . © 2020. 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.titleAccelerating flash calculations in unconventional reservoirs considering capillary pressure using an optimized deep learning algorithm
dc.typeArticle
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentComputational Transport Phenomena Laboratory (CTPL), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalJournal of Petroleum Science and Engineering
dc.rights.embargodate2022-09-18
dc.eprint.versionPost-print
dc.contributor.institutionPetrochina Beijing Oil and Gas Pipeline Control Center, 9 Dongzhimen North Street, Dongcheng District, Beijing, 100007, China
dc.identifier.volume195
dc.identifier.pages107886
kaust.personZhang, Tao
kaust.personLi, Yiteng
kaust.personSun, Shuyu
kaust.grant.numberBAS/1/1351-01-01
dc.date.accepted2020-09-02
dc.identifier.eid2-s2.0-85091114048
refterms.dateFOA2020-09-27T06:08:23Z
dc.date.published-online2020-09-15
dc.date.published-print2020-12


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