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    Accelerating flash calculations in unconventional reservoirs considering capillary pressure using an optimized deep learning algorithm

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    samplepaper.pdf
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    1.072Mb
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    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2022-09-18
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    Type
    Article
    Authors
    Zhang, Tao cc
    Li, Yiteng cc
    Sun, Shuyu cc
    Bai, Hua
    KAUST Department
    Computational Transport Phenomena Lab
    Computational Transport Phenomena Laboratory (CTPL), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    BAS/1/1351-01-01
    Date
    2020-09-15
    Online Publication Date
    2020-09-15
    Print Publication Date
    2020-12
    Embargo End Date
    2022-09-18
    Submitted Date
    2019-12-29
    Permanent link to this record
    http://hdl.handle.net/10754/665295
    
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    Abstract
    An 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.
    Citation
    Zhang, 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
    Sponsors
    The 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.
    Publisher
    Elsevier BV
    Journal
    Journal of Petroleum Science and Engineering
    DOI
    10.1016/j.petrol.2020.107886
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0920410520309438
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.petrol.2020.107886
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computational Transport Phenomena Lab

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