# Artificial Neural Network Accelerated Flash Calculation for Compositional Simulations

- Authors
- Kun Wang (University of Calgary) | Jia Luo (University of Calgary) | Lin Yan (Exploration and Development Research Institute, PetroChina) | Yizheng Wei (Computer Modeling Group Ltd) | Keliu Wu (China University of Petroleum) | Jing Li (University of Calgary) | Fuli Chen (Exploration and Development Research Institute, PetroChina) | Xiaohu Dong (China University of Petroleum) | Zhangxin Chen (University of Calgary)
- DOI
- https://doi.org/10.2118/193896-MS
- Document ID
- SPE-193896-MS
- Publisher
- Society of Petroleum Engineers
- Source
- SPE Reservoir Simulation Conference, 10-11 April, Galveston, Texas, USA
- Publication Date
- 2019

- Document Type
- Conference Paper
- Language
- English
- ISBN
- 978-1-61399-634-8
- Copyright
- 2019. Society of Petroleum Engineers
- Disciplines
- 5.2 Fluid Characterization, 7 Management and Information, 6.1.5 Human Resources, Competence and Training, 6.1 HSSE & Social Responsibility Management, 6 Health, Safety, Security, Environment and Social Responsibility, 5.2.1 Phase Behavior and PVT Measurements, 7.6.7 Neural Networks, 7.6 Information Management and Systems, 5 Reservoir Desciption & Dynamics, 5.2.2 Fluid Modeling, Equations of State
- Keywords
- Phase Flash calculation, Split Calculation, Artificial Neural Network, Stability Test, Compositional Simulation
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- 8 in the last 30 days
- 110 since 2007

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EOS-based phase equilibrium calculations are usually used in compositional simulation to have accurate phase behaviour. Phase equilibrium calculations include two parts: phase stability tests and phase splitting calculations. Since the conventional methods for phase equilibrium calculations need to iteratively solve strongly nonlinear equations, the computational cost spent on the phase equilibrium calculations is huge, especially for the phase stability tests. In this work, we propose artificial neural network (ANN) models to accelerate the phase flash calculations in compositional simulations. For the phase stability tests, an ANN model is built to predict the saturation pressures at given temperature and compositions, and consequently the stability can be obtained by comparing the saturation pressure with the system pressure. The prediction accuracy is more than 99% according to our numerical results. For the phase splitting calculations, another ANN model is trained to provide initial guesses for the conventional methods. With these initial guesses, the nonlinear iterations can converge much faster. The numerical results show that 90% of the computation time spent on the phase flash calculations can be saved with the application of the ANN models.

File Size | 1 MB | Number of Pages | 13 |

### Supporting information

- SUPPLEMENTARY/SPE-193896-SUP.pdf

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Leven-berg, D. Man e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi’egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems, software available from tensorflow.org (2015). URL https://www.tensorflow.org/