Verification of a real-time ensemble-based method for updating earth model based on GAN

Kristian Fossum, Sergey Alyaev, Jan Tveranger, Ahmed H. Elsheikh

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Abstract

The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of geomodels, combined with Ensemble Randomized Maximum Likelihood (EnRML) for rapid updating of subsurface uncertainty. This real-time ensemble method is known to be approximate for non-linear forward models and might therefore produce inaccurate and/or biased posterior solutions when combined with a highly non-linear model arising from the neural-network modeling sequences. This paper illustrates the predictive ability of EnRML on several examples where we assimilate local extra-deep electromagnetic logs. Statistical verification with MCMC confirms that the proposed workflow can produce reliable results required for geosteering wells.
Original languageEnglish
Article number101876
JournalJournal of Computational Science
Volume65
Early online date18 Oct 2022
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Deep neural network
  • Ensemble randomized maximum likelihood
  • Generative Adversarial Network
  • Geosteering
  • Machine learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Modelling and Simulation

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