  Directional oil well drilling requires high precision of the wellbore
positioning inside the productive area. However, due to specifics of
engineering design, sensors that explicitly determine the type of the drilled
rock are located farther than 15m from the drilling bit. As a result, the
target area runaways can be detected only after this distance, which in turn,
leads to a loss in well productivity and the risk of the need for an expensive
re-boring operation.
  We present a novel approach for identifying rock type at the drilling bit
based on machine learning classification methods and data mining on sensors
readings. We compare various machine-learning algorithms, examine extra
features coming from mathematical modeling of drilling mechanics, and show that
the real-time rock type classification error can be reduced from 13.5 % to 9 %.
The approach is applicable for precise directional drilling in relatively thin
target intervals of complex shapes and generalizes appropriately to new wells
that are different from the ones used for training the machine learning model.
