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History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions

arXiv 2507.10201 ↗First posted 14 Jul 2025stat.AP cs.LG physics.data-ancited by 0

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Abstract (arXiv)

The graph-based variational autoencoder represents an architecture that can handle the uncertainty of different geological scenarios, such as depositional or structural, through the concept of a lowerdimensional latent space. The main difference from recent studies is utilisation of a graph-based approach in reservoir modelling instead of the more traditional lattice-based deep learning methods. We provide a solution to implicitly control the geological realism through the latent variables of a generative model and Geodesic metrics. Our experiments of AHM with synthetic dataset that consists of 3D realisations of channelised geological representations with two distinct scenarios with one and two channels shows the viability of the approach. We offer in-depth analysis of the latent space using tools such as PCA, t-SNE, and TDA to illustrate its structure.

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References · 15

  1. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 881
  2. 2017Wasserstein Auto-Encoderscited by 373
  3. 2017Latent Space Oddity: on the Curvature of Deep Generative Modelscited by 174
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  5. 2018On Characterizing the Capacity of Neural Networks using Algebraic Topologycited by 38
  6. 2017Training-image based geostatistical inversion using a spatial generative adversarial neural networkcited by 32
  7. 2017Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural networkcited by 24
  8. 2018On the Latent Space of Wasserstein Auto-Encoderscited by 22
  9. 2018Parametric generation of conditional geological realizations using generative neural networkscited by 19
  10. 2018Towards a Robust Parameterization for Conditioning Facies Models Using Deep Variational Autoencoders and Ensemble Smoothercited by 16
  11. 2018Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networkscited by 12
  12. 2017Parametrization and generation of geological models with generative adversarial networkscited by 9
3 more
  1. 2020Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matchingcited by 8
  2. 2019DeepFlow: History Matching in the Space of Deep Generative Modelscited by 4
  3. 2018Exemplar-based synthesis of geology using kernel discrepancies and generative neural networkscited by 1

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