Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator

TL;DR

This paper introduces HGNS, a hybrid graph neural network model that significantly accelerates large-scale 3D subsurface fluid flow simulations while maintaining high accuracy, enabling faster decision-making in petroleum engineering.

Contribution

The paper presents a novel hybrid graph neural network architecture combining SGNN and 3D-U-Net for scalable and accurate reservoir simulation at unprecedented grid sizes.

Findings

HGNS scales to millions of cells per time step.

HGNS reduces inference time by up to 18 times.

HGNS outperforms existing models with up to 21% lower prediction error.

Abstract

Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast and accurate models are needed for high-stake decision making, for example, for well placement optimization and field development planning. Classical finite difference numerical simulators require massive computational resources to model large-scale real-world reservoirs. Alternatively, streamline simulators and data-driven surrogate models are computationally more efficient by relying on approximate physics models, however they are insufficient to model complex reservoir dynamics at scale. Here we introduce Hybrid Graph Network Simulator (HGNS), which is a data-driven surrogate model for learning reservoir simulations of 3D subsurface fluid flows. To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. HGNS is able to scale to grids with millions of cells per time step, two orders of magnitude higher than previous surrogate models, and can accurately predict the fluid flow for tens of time steps (years into the future). Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators, and that it outperforms other learning-based models by reducing long-term prediction errors by up to 21%.