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Abstract

Summary

The ability to generate geologically plausible subsurface models is critical for many applications related to energy resources such as hydrocarbon and geothermal reservoirs and to geo-storage of carbon dioxide and hydrogen. Geostatistical methods have been the preferable tools to create numerical representations of the subsurface due to their ability to integrate data with different spatial resolutions while accounting for uncertainties. However, these methods might be computationally expensive and might have limitations to account for scenarios not sampled by the existing data. The recent advances in deep learning open new ways to overcome some of these limitations. We show herein, the applicability of a Variational Autoencoder (VAE) for the reconstruction of subsurface models in the presence and absence of direct observations. The VAE learn the spatial structure of the available data and allow generating multiple subsurface models. The application examples show the ability of VAE to predict the spatial distribution of electrical resistivity for the conditioning and unconditional cases with similar performance of geostatistical modelling tools.

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/content/papers/10.3997/2214-4609.202335041
2023-11-27
2025-04-30

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References

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/content/papers/10.3997/2214-4609.202335041
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Facies Model Generation with Deep Variational Autoencoders
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335041
/content/papers/10.3997/2214-4609.202335041
/content/papers/10.3997/2214-4609.202335041
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