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Geological realism in Fluvial facies modelling with GAN under variable depositional conditions

Computational Geosciences volume 27, pages 203–221 (2023)Cite this article

Abstract

This study investigates generative adversarial networks (GANs)’ capacity to model multi-facies distributions of meandering systems. Earlier works showed that GANs outperform geostatistical methods in reproducing complex geometry, like the shapes of fluvial channels. However, the reproduction of geological complexity and geological realism remains an issue when modelling fluvial depositional systems. Meandering systems deposit multiple facies and change facies shape following the migration of rivers. Sand accretes at the inner bank of channels, forming the point bar and erodes the plain at the outer bank to create sediments. Channel fills with mud or sand at the bottom after abandonment due to avulsions or meander cut-offs. Those sedimentary processes yield complex geological patterns. This paper proposes further developing a GAN model, Fluvial GAN, to learn complex multi-facies fluvial patterns across depositional variability. We create a set of meandering facies models by a process-based model, FLUMYTM, for training a GAN and assessing how well it can learn fluvial facies distributions representing sedimentary processes. Fluvial GAN has three distinct enhancements: (i) a One-Hot Encoder for better handling of multi-facies distribution, (ii) a Hybrid-discriminator for better learning geological patterns, and (iii) an improved loss function to prevent mode collapse. We compare Fluvial GAN performance with two more standard configurations using qualitative and quantitative geological features assessments. Fluvial GAN vastly reduces the occurrence of a typical unrealistic feature, channels forming isolated loops, which we called ‘closed channel’ in this study. We analyse the diversity of Fluvial GAN generations via a dimensionality reduction algorithm, UMAP, that plots the training dataset and Fluvial GAN generations together in a 2D space. Fluvial GAN provides good coverage of the uncertainty space represented by the training dataset.

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Data Availability

The training dataset used in this study is available at https://geodatascience.hw.ac.uk/gan-river-i/ [43]. Supporting data of the results are available at https://github.com/GeoDataScienceUQ/Fluvial_GAN.

Code Availability

Codes are available at https://github.com/GeoDataScienceUQ/Fluvial_GAN.

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Acknowledgements

The work contained in this paper contains work conducted during a PhD study undertaken as part of the Natural Environment Research Council (NERC) Centre for Doctoral Training (CDT) in Oil & Gas [grant number NEM00578X/1]. This project is sponsored by Heriot-Watt University via their James Watt Scholarship Scheme whose support is gratefully acknowledged. We would like to thank MINES ParisTech for their software FLUMYTM, Process-based channelized reservoir models. CopyrightⒸMINES PARIS-PSL / ARMINES. Free download from http://cg.ensmp.fr/flumy. We would like to thank Dr Andy Gardiner for his help on sedimentary geology, thank Prof. Ahmed ElSheikh for his suggestions on result evaluation, and thank Dr Zeyun Jiang, Dr Guillaume Rongier and two anonymous reviewers for their reviews that helped improve the paper.

Funding

The work contained in this paper contains work conducted during a PhD study undertaken as part of the Natural Environment Research Council (NERC) Centre for Doctoral Training (CDT) in Oil & Gas [grant number NEM00578X/1]. This project is sponsored by Heriot-Watt University via their James Watt Scholarship Scheme.

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    Authors and Affiliations

    1. Institute of Geoenergy Engineering, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK

      Chao Sun, Vasily Demyanov & Daniel Arnold

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    1. Chao Sun
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    2. Vasily Demyanov
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    3. Daniel Arnold
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    Corresponding author

    Correspondence to Chao Sun.

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    Additional information

    Supplementary information

    Our codes and results are available at https://github.com/GeoDataScienceUQ/Fluvial_GAN. The training dataset used in this study is available at https://geodatascience.hw.ac.uk/gan-river-i/ [43].

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    Vasily Demyanov and Daniel Arnold contributed equally to this work.

    Appendices

    Appendix A: Details of Fluvial GAN model

    1.1 A.1 Generator

    Table 6 Generator architecture
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    Table 7 NN Block architecture
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    1.2 A.2 Hybrid-discriminator

    Table 8 CNN-based PatchGAN discriminator
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    Table 9 Dilated CNN-based PatchGAN discriminator
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    1.3 A.3 Training

    We use a benchmark GPU, RTX3090, to test our Fluvial GAN model’s training and generation speed. Training 200 epochs take around 23 hours for a single RTX3090 and the pre-trained Fluvial GAN can simulate about 16000 realisations per minute. Figure 18 shows the loss curves during training.

    Fig. 18
    figure 18

    Loss curves of Fluvial GAN during training

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    Appendix B: Details of making training dataset

    Table 10 FLUMYTM parameters to create 3D simulations
    Full size table

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    Sun, C., Demyanov, V. & Arnold, D. Geological realism in Fluvial facies modelling with GAN under variable depositional conditions. Comput Geosci 27, 203–221 (2023). https://doi.org/10.1007/s10596-023-10190-w

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