  Modern geosteering is heavily dependent on real-time interpretation of deep
electromagnetic (EM) measurements. We present a methodology to construct a deep
neural network (DNN) model trained to reproduce a full set of extra-deep EM
logs consisting of 22 measurements per logging position. The model is trained
in a 1D layered environment consisting of up to seven layers with different
resistivity values. A commercial simulator provided by a tool vendor is used to
generate a training dataset. The dataset size is limited because the simulator
provided by the vendor is optimized for sequential execution. Therefore, we
design a training dataset that embraces the geological rules and geosteering
specifics supported by the forward model. We use this dataset to produce an EM
simulator based on a DNN without access to the proprietary information about
the EM tool configuration or the original simulator source code. Despite
employing a relatively small training set size, the resulting DNN forward model
is quite accurate for the considered examples: a multi-layer synthetic case and
a section of a published historical operation from the Goliat Field. The
observed average evaluation time of 0.15 ms per logging position makes it also
suitable for future use as part of evaluation-hungry statistical and/or
Monte-Carlo inversion algorithms within geosteering workflows.
