U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow

• URL: http://arxiv.org/abs/2109.03697v1
• Date: Fri, 3 Sep 2021 17:52:25 GMT
• Title: U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow
• Authors: Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson
• Abstract summary: We present U-FNO, an enhanced Fourier neural operator for solving the multiphase flow problem. We show that the U-FNO architecture has the advantages of both traditional CNN and original FNO, providing significantly more accurate and efficient performance. The trained U-FNO provides gas saturation and pressure buildup predictions with a 10,000 times speedup compared to traditional numerical simulators.
• Score: 43.572675744374415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. However, due to the multi-physics, non-linear, and multi-scale problem nature, these simulations are very expensive at desirable grid resolutions, and the computational cost often impedes rigorous engineering decision-making. Machine learning methods provide faster alternatives to traditional simulators by training neural network models with numerical simulation data mappings. Traditional convolutional neural network (CNN)-based models are accurate yet data-intensive and are prone to overfitting. Here we present a new architecture, U-FNO, an enhanced Fourier neural operator for solving the multiphase flow problem. The U-FNO is designed based on the Fourier neural operator (FNO) that learns an integral kernel in the Fourier space. Through a systematic comparison among a CNN benchmark and three types of FNO variations on a CO2-water multiphase problem in the context of CO2 geological storage, we show that the U-FNO architecture has the advantages of both traditional CNN and original FNO, providing significantly more accurate and efficient performance than previous architectures. The trained U-FNO provides gas saturation and pressure buildup predictions with a 10,000 times speedup compared to traditional numerical simulators while maintaining similar accuracy.

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