Rate of Penetration (ROP) prediction is an important aspect of drilling in the Oil & Gas Industry. Several studies have
been carried out to predict ROP. Primarily, Artificial Neural Networks (ANN) has been used. In this paper, the objective is to
explore a new approach to predict ROP using K-means and Ensemble of Gradient Boosting Model (GBM) technique. Nine input
parameters are used for ROP prediction- True vertical depth, weight on bit, standpipe pressure, flow-rate, torque, equivalent
circulating density and RPM