Fast and Robust Prediction of Multiphase Flow in Complex Fractured Reservoir Using a Fourier Neural Operator

Tie Kuang, Jianqiao Liu (), Zhilin Yin, Hongbin Jing, Yubo Lan, Zhengkai Lan and Huanquan Pan
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Tie Kuang: Exploration and Development Research Institute of Daqing Oilfield Company Ltd., Daqing 163000, China
Jianqiao Liu: Department of Petroleum Engineering, China University of Geosciences (Wuhan), Wuhan 430000, China
Zhilin Yin: Exploration and Development Research Institute of Daqing Oilfield Company Ltd., Daqing 163000, China
Hongbin Jing: Department of Petroleum Engineering, China University of Geosciences (Wuhan), Wuhan 430000, China
Yubo Lan: Exploration and Development Research Institute of Daqing Oilfield Company Ltd., Daqing 163000, China
Zhengkai Lan: Tracy Energy Technologies Co., Ltd., Houzhou 312399, China
Huanquan Pan: Department of Petroleum Engineering, China University of Geosciences (Wuhan), Wuhan 430000, China

Energies, 2023, vol. 16, issue 9, 1-18

Abstract: Predicting multiphase flow in complex fractured reservoirs is essential for developing unconventional resources, such as shale gas and oil. Traditional numerical methods are computationally expensive, and deep learning methods, as an alternative approach, have become an increasingly popular topic. Fourier neural operator (FNO) networks have been shown to be a hundred times faster than convolutional neural networks (CNNs) in predicting multiphase flow in conventional reservoirs. However, there are few relevant studies on applying FNO to predict multiphase flow in reservoirs with complex fractures. In the present study, FNO-net and U-net (CNN-based) were successfully applied to predict pressure and gas saturation fields for the 2D heterogeneous fractured reservoirs. The tested results show that FNO can accurately depict the influence of fine fractures, while the CNN-based method has relatively poor performance in the treatment of fracture systems, both in terms of accuracy and computational speed. In addition, by adding initial conditions and boundary conditions to the loss function of FNO, we prove the necessity of adding physical constraints to the data-driven model. This work contributes to improving the understanding of the applicability of FNO-net, and provides new insights into deep learning methods for predicting multiphase flow in complex fractured reservoirs.

Keywords: Fourier neural operator; multiphase flow; fractured reservoirs; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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