Comparison of popular Generative Adversarial Network flavours for fluvial reservoir modelling

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This paper presents a commparative study of popular generative adversarial network flavours for fluvial reservoir modelling. Fluvial reservoir contains complex sedimentary architecture and facies heterogeneity. To better represent the real complexity of fluvial reservoir, we used a process-based model, FLUMY, to generated a low NTG fluvial dataset. Low NTG systems have less amalgamation and more complex sand-body connectivity. We simplify this dataset by reducing the number of facies. Thus, the training dataset for testing different GANs is composed of three-facies process-based 2D realizations. Candidates in this study include DCGAN, WGAN, WGAN-gp and PatchGAN. Visual comparison of the GANs generations found that some GANs have difficulties in learning certain key geolgocial features from this training dataset. PatchGAN outperforms in the aspect of channel geometry and facies placement. However, there are still some geologically unrealistic featues remaning. For example, the 'closed channel' pattern. Future efforts in GAN-based modelling would benefit from tackling complex geological systems.

Original languageEnglish
Title of host publication82nd EAGE Conference and Exhibition 2021
PublisherEAGE Publishing BV
Number of pages5
ISBN (Electronic)9781713841449
Publication statusPublished - 2021
Event82nd EAGE Conference and Exhibition 2021 - Amsterdam, Virtual, Netherlands
Duration: 18 Oct 202121 Oct 2021


Conference82nd EAGE Conference and Exhibition 2021
Abbreviated titleEAGE 2021
CityAmsterdam, Virtual

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geology
  • Geophysics
  • Geotechnical Engineering and Engineering Geology


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