  During a geosteering operation the well path is intentionally adjusted in
response to the new data acquired while drilling. To achieve consistent
high-quality decisions, especially when drilling in complex environments,
decision support systems can help cope with high volumes of data and
interpretation complexities. They can assimilate the real-time measurements
into a probabilistic earth model and use the updated model for decision
recommendations.
  Recently, machine learning (ML) techniques have enabled a wide range of
methods that redistribute computational cost from on-line to off-line
calculations. In this paper, we introduce two ML techniques into the
geosteering decision support framework. Firstly, a complex earth model
representation is generated using a Generative Adversarial Network (GAN).
Secondly, a commercial extra-deep electromagnetic simulator is represented
using a Forward Deep Neural Network (FDNN).
  The numerical experiments demonstrate that the combination of the GAN and the
FDNN in an ensemble randomized maximum likelihood data assimilation scheme
provides real-time estimates of complex geological uncertainty. This yields
reduction of geological uncertainty ahead of the drill-bit from the
measurements gathered behind and around the well bore.
