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dc.contributor.authorLi, Yiteng
dc.contributor.authorZhang, Tao
dc.contributor.authorSun, Shuyu
dc.date.accessioned2019-12-18T07:31:24Z
dc.date.available2019-12-18T07:31:24Z
dc.date.issued2019-01-27
dc.identifier.urihttp://hdl.handle.net/10754/660651
dc.description.abstractPhase equilibrium calculation, also known as flash calculation, has been extensively applied in petroleum engineering, not only as a standalone application for separation process but also an integral component of compositional reservoir simulation. It is of vital importance to accelerate flash calculation without much compromise in accuracy and reliability, turning it into an active research topic in the last two decades. In this study, we establish a deep neural network model to approximate the iterative NVT-flash calculation. A dynamic model designed for NVT flash problems is iteratively solved to produce data for training the neural network. In order to test the model's capacity to handle complex fluid mixtures, three real reservoir fluids are investigated, including one Bakken oil and two Eagle Ford oils. Compared to previous studies that follow the conventional flash framework in which stability testing precedes phase splitting calculation, we incorporate stability test and phase split calculation together and accomplish both two steps by a single deep learning model. The trained model is able to identify the single vapor, single liquid and vapor-liquid state under the subcritical region of the investigated fluids. A number of examples are presented to show the accuracy and efficiency of the proposed deep neural network. It is found that the trained model makes predictions at most 244 times faster than the iterative flash calculation under the given cases. Even though training a multi-level network model does take a large amount of time that is comparable to the computational time of flash calculations, the one-time offline training process gives the deep learning model great potential to speed up compositional reservoir simulation.
dc.description.sponsorshipThe authors greatly thank for the Research Funding from King Abdullah University of Science and Technology (KAUST) through the grant BAS/1/1351- 01-01 and the support from the National Natural Science Foundation of China (No. 51874262).
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1901.09380
dc.rightsArchived with thanks to arXiv
dc.titleAcceleration of the NVT-flash calculation for multicomponent mixtures using deep neural network models
dc.typePreprint
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentPhysical Science and Engineering Division (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
dc.eprint.versionPre-print
dc.contributor.institutionInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, P.R. China.
dc.identifier.arxivid1901.09380
kaust.personLi, Yiteng
kaust.personZhang, Tao
kaust.personSun, Shuyu
dc.versionv1
refterms.dateFOA2019-12-18T07:32:25Z


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