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arXiv · 2207.03596

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 combined with a highly non-linear model arising from neural-network modeling sequences might produce inaccurate and/or biased posterior solutions. 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.

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Kristian Fossum, Sergey Alyaev, Jan Tveranger, Ahmed H. Elsheikh. 2022-07-07. Verification of a real-time ensemble-based method for updating earth model based on GAN. https://doi.org/10.1016/j.jocs.2022.101876

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