U-FNO-An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Abstract
Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical simulation data can provide a faster alternative to traditional simulators. Here we present U-FNO, a novel neural network architecture for solving multiphase flow problems with superior accuracy, speed, and data efficiency. U-FNO is designed based on the newly proposed Fourier neural operator (FNO), which has shown excellent performance in single-phase flows. We extend the FNO-based architecture to a highly complex CO2-water multiphase problem with wide ranges of permeability and porosity heterogeneity, anisotropy, reservoir conditions, injection configurations, flow rates, and multiphase flow properties. The U-FNO architecture is more accurate in gas saturation and pressure buildup predictions than the original FNO and a state-of-the-art convolutional neural network (CNN) benchmark. Meanwhile, it has superior data utilization efficiency, requiring only a third of the training data to achieve the equivalent accuracy as CNN. U-FNO provides superior performance in highly heterogeneous geological formations and critically important applications such as gas saturation and pressure buildup "fronts" determination. The trained model can serve as a general-purpose alternative to routine numerical simulations of 2D-radial CO2 injection problems with significant speed-ups than traditional simulators.
- Publication:
-
Advances in Water Resources
- Pub Date:
- May 2022
- DOI:
- 10.1016/j.advwatres.2022.104180
- arXiv:
- arXiv:2109.03697
- Bibcode:
- 2022AdWR..16304180W
- Keywords:
-
- Multiphase flow;
- Fourier neural operator;
- Convolutional neural network;
- Carbon capture and storage;
- Deep learning;
- Physics - Geophysics;
- Computer Science - Machine Learning
- E-Print:
- doi:10.1016/j.advwatres.2022.104180