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    A self-adaptive deep learning algorithm for accelerating multi-component flash calculation

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    A self adaptive.pdf
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    Type
    Article
    Authors
    Zhang, Tao cc
    Li, Yu
    Li, Yiteng cc
    Sun, Shuyu cc
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computational Transport Phenomena Lab
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2020-06-11
    Online Publication Date
    2020-06-11
    Print Publication Date
    2020-09
    Embargo End Date
    2022-06-11
    Submitted Date
    2020-01-10
    Permanent link to this record
    http://hdl.handle.net/10754/663530
    
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    Abstract
    In this paper, the first self-adaptive deep learning algorithm is proposed in details to accelerate flash calculations, which can quantitatively predict the total number of phases in the mixture and related thermodynamic properties at equilibrium for realistic reservoir fluids with a large number of components under various environmental conditions. A thermodynamically consistent scheme for phase equilibrium calculation is adopted and implemented at specified moles, volume and temperature, and the flash results are used as the ground truth for training and testing the deep neural network. The critical properties of each component are considered as the input features of the neural network and the final output is the total number of phases at equilibrium and the molar compositions in each phase. Two network structures are well designed, one of which transforms the input of various numbers of components in the training and the objective fluid mixture into a unified space before entering the productive neural network. “Ghost components” are defined and introduced to process the data padding work in order to modify the dimension of input flash calculation data to meet the training and testing requirements of the target fluid mixture. Hyperparameters on both two neural networks are carefully tuned in order to ensure the physical correlations underneath the input parameters are preserved properly through the learning process. This combined structure can make our deep learning algorithm to be self-adaptive to the change of input components and dimensions. Furthermore, two Softmax functions are used in the last layer to enforce the constraint that the summation of mole fractions in each phase is equal to 1. An example is presented that the flash calculation results of a 8-component Eagle Ford oil is used as input to estimate the phase equilibrium state of a 14-component Eagle Ford oil. The results are satisfactory with very small estimation errors. The capability of the proposed deep learning algorithm is also verified that simultaneously completes phase stability test and phase splitting calculation. Remarks are concluded at the end to provide some guidance for further research in this direction, especially the potential application of newly developed neural network models.
    Citation
    Zhang, T., Li, Y., Li, Y., Sun, S., & Gao, X. (2020). A self-adaptive deep learning algorithm for accelerating multi-component flash calculation. Computer Methods in Applied Mechanics and Engineering, 369, 113207. doi:10.1016/j.cma.2020.113207
    Publisher
    Elsevier BV
    Journal
    Computer Methods in Applied Mechanics and Engineering
    DOI
    10.1016/j.cma.2020.113207
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0045782520303923
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.cma.2020.113207
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Structural and Functional Bioinformatics Group; Computer Science Program; Earth Science and Engineering Program; Computational Transport Phenomena Lab; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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