  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.
