  We use borehole resistivity measurements to map the electrical properties of
the subsurface and to increase the productivity of a reservoir. When used for
geosteering purposes, it becomes essential to invert them in real time. In this
work, we explore the possibility of using Deep Neural Network (DNN) to perform
a rapid inversion of borehole resistivity measurements. Herein, we build a DNN
that approximates the following inverse problem: given a set of borehole
resistivity measurements, the DNN is designed to deliver a physically
meaningful and data-consistent piecewise one-dimensional layered model of the
surrounding subsurface. Once the DNN is built, we can perform the actual
inversion of the field measurements in real time. We illustrate the performance
of DNN of logging-while-drilling measurements acquired on high-angle wells via
synthetic data.
