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- W4387358976 abstract "Abstract Numerical simulation is an important tool for CO2 flooding and storage simulation, which allows to obtain global approximate solutions of governing equation. However, the simulations often suffer from significant computational costs and convergence problems, especially considering the pseudo-component and CO2 storage mechanisms. This makes the scheme optimization tedious. Therefore, we propose a deep learning-based surrogate model to efficiently implement numerical simulation of CO2-flooding and storage. Proposed method consists of automatic encoder and prediction part. The auto-encoder consists of VQ-VAE model, which projects the reservoir's 3D properties into 2D space. The prediction part consists of ConvLSTM models, which accepts reservoir variables. Finally, the surrogate model outputs the dynamic characteristics of production and different CO2 storage forms. The results show that the original reservoir properties can be restored with high fidelity after autoencoder training. The correlation coefficient between the decoded attribute and the original attribute is greater than 0.98. For prediction part, ConvLSTM can accurately predict the dynamic characteristics of production and different CO2 storage forms. The average relative errors of the predictions in the training and validation sets were 4.37% as well as 8.91%. In addition, for computational efficiency, the surrogate model is two orders of magnitude faster than the numerical model. It is proved that the surrogate model can effectively replace the numerical simulation model and greatly improve the computational efficiency." @default.
- W4387358976 created "2023-10-06" @default.
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- W4387358976 date "2023-10-06" @default.
- W4387358976 modified "2023-10-06" @default.
- W4387358976 title "A Surrogate Model for Numerical Reservoir Simulation of CO2 Flooding and Storage Based on Deep Learning" @default.
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- W4387358976 doi "https://doi.org/10.2118/215253-ms" @default.
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