A self-adaptive deep learning algorithm for accelerating multi-component flash calculation
Type
ArticleAuthors
Zhang, Tao
Li, Yu
Li, Yiteng

Sun, Shuyu

Gao, Xin

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
2020-06-11Online Publication Date
2020-06-11Print Publication Date
2020-09Embargo End Date
2022-06-11Submitted Date
2020-01-10Permanent link to this record
http://hdl.handle.net/10754/663530
Metadata
Show full item recordAbstract
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.2020.113207Publisher
Elsevier BVAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S0045782520303923ae974a485f413a2113503eed53cd6c53
10.1016/j.cma.2020.113207