Speaker
Description
Pore-scale simulations are computationally expensive and the presence of non-unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics-based simulations. While training the data-driven model, we simultaneously generate initial conditions and perform physics-based simulations using these. 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 a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. 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.
| References | https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JH000293 |
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| Country | US |
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