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Directional oil well drilling requires high precision of the wellborepositioning inside the productive area. However, due to specifics ofengineering design, sensors that explicitly determine the type of the drilledrock are located farther than 15m from the drilling bit. As a result, thetarget 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 expensivere-boring operation.  We present a novel approach for identifying rock type at the drilling bitbased on machine learning classification methods and data mining on sensorsreadings. We compare various machine-learning algorithms, examine extrafeatures coming from mathematical modeling of drilling mechanics, and show thatthe real-time rock type classification error can be reduced from 13.5 % to 9 %.The approach is applicable for precise directional drilling in relatively thintarget intervals of complex shapes and generalizes appropriately to new wellsthat are different from the ones used for training the machine learning model.

Data-driven model for the identification of the rock type at a drilling bit