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U-net generative adversarial network for subsurface facies modeling

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Abstract

Subsurface models are central pieces of information in different earth-related disciplines such as groundwater management and hydrocarbon reservoir characterization. These models are normally obtained using geostatistical simulation methods. Recently, methods based on deep learning algorithms have been applied as subsurface model generators. However, there are still challenges on how to include conditioning data and ensure model variability within a set of realizations. We illustrate the potential of Generative Adversarial Networks (GANs) to create unconditional and conditional facies models. Based on a synthetic facies dataset, we first train a Deep Convolution GAN (DCGAN) to produce unconditional facies models. Then, we show how image-to-image translation based on a U-Net GAN framework, including noise-layers, content loss function and diversity loss function, is used to model conditioning geological facies. Results show that GANs are powerful models to capture complex geological facies patterns and to generate facies realizations indistinguishable from the ones comprising the training dataset. The U-Net GAN framework performs well in providing variable models while honoring conditioning data in several scenarios. The results shown herein are expected to spark a new generation of methods for subsurface geological facies with fragmentary measurements.

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Acknowledgements

The authors gratefully acknowledge the support of the CERENA (strategic project FCT-UIDB/04028/2020) and the National Key Research and Development Project of China (project number 2019YFA0708300). The authors also thank the student exchange program between China University of Petroleum (Beijing) and Instituto Superior Técnico. We acknowledge the two anonymous reviewers that contributed to increase the quality of the original version of this work.

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Authors and Affiliations

  1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, 102249, China

    Chengkai Zhang & Xianzhi Song

  2. CERENA, DECivil, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Leonardo Azevedo

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  1. Chengkai Zhang
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  2. Xianzhi Song
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  3. Leonardo Azevedo
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Correspondence to Xianzhi Song or Leonardo Azevedo.

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The U-NET GAN code used in this work can be accessed using the following link: ttps://github.com/kyle4git/U-Net-GAN-for-subsurface-facies-modeling.git

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Zhang, C., Song, X. & Azevedo, L. U-net generative adversarial network for subsurface facies modeling. Comput Geosci 25, 553–573 (2021). https://doi.org/10.1007/s10596-020-10027-w

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