A MgNO Method for Multiphase Flow in Porous Media

TL;DR

This paper introduces MgNO, a neural operator architecture inspired by multigrid methods, tailored for efficient and accurate simulation of multiphase flow in porous media, extending to time-dependent problems and comparing with FNO.

Contribution

The study extends MgNO to handle time-dependent multiphase flow problems and provides a comprehensive comparison with FNO regarding prediction accuracy and stability.

Findings

MgNO effectively simulates multiphase flow in porous media.

MgNO shows improved long-term prediction stability over FNO.

Significant time savings over traditional simulation methods.

Abstract

This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications.