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Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator by Tailin Wu; Qinchen Wang; Yinan Zhang; Rex Ying; Kaidi Cao; Rok Sosic; Ridwan Jalali; Hassan Hamam; Marko Maucec; Jure Leskovec is a scholarly article available to read on EtoBox.

What is Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator about?

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 highstake 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 pr

Author

Tailin Wu; Qinchen Wang; Yinan Zhang; Rex Ying; Kaidi Cao; Rok Sosic; Ridwan Jalali; Hassan Hamam; Marko Maucec; Jure Leskovec

Publisher

ACM

Published

2022

Language

EN