Modified UNet with attention gate and dense skip connection for flow field information prediction with porous media

Abstract

Porous media flow phenomena are commonly found in nature and industry. Understanding the patterns can improve industrial production and efficiency. Traditional numerical methods can predict flow field information within porous media exactly, even on pore scale. However, once the pore structures of porous media are changed, the traditional numerical methods have to start over again, which implies poor extension and low efficiency for realistic application. To address the challenge, we propose a new deep learning method for image segmentation based on convolutional neural networks that can quickly predict the saturation variation and flow velocity profile of multiphase flow directly from the structure of porous media. In the present method, the UNet network structure is improved by replacing parallel splicing with different levels of dense skip connection to extract flow information at different depths. When processing flow topological information, the introduction of attention mechanism allows effective focus on adjusting the flow field edges and front shapes to improve accuracy. Depending on the pore structures information, the saturation of the porous media also can be evaluated. This result shows that the flow field information (saturation and velocity) of porous media can be quickly predicted by deep learning methods. Compared with the computational fluid dynamics (CFD) method, this approach has a better prediction efficiency for porous media. The machine learning method is a good prediction tool for porous media with different porosities and pore structures.