Accelerating Multiphase Flow Simulations with Denoising Diffusion Model Driven Initializations

TL;DR

This paper presents a hybrid diffusion model and physics-based simulation approach that accelerates multiphase flow simulations, reducing computational costs while maintaining physical accuracy, demonstrated on sandstone fracture data.

Contribution

The study introduces a novel hybrid method coupling generative diffusion models with physics simulations for faster flow initializations in complex geometries.

Findings

Up to 4.4 times faster initializations compared to traditional methods

Effective generation of variable fluid saturations within the same geometry

Real-time feedback enables efficient training and evaluation

Abstract

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics-based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. These simulations enhance our understanding of applications such as assessing hydrogen and CO$_2$ storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of nonunique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a hybrid method that couples generative diffusion models and physics-based modeling. We introduce a system to condition the diffusion model with a geometry of interest, allowing to produce variable fluid saturations in the same geometry. While training the model, we simultaneously generate initial conditions and perform physics-based simulations using these conditions. This integrated approach enables us to receive real-time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within one compute node, we can continuously evaluate performance and stop training when the desired criteria are met. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.