Using a data mining CRISP-DM methodology for rate of penetration (ROP) prediction in oil well drilling

This work describes an implementation of a oil drilling data mining project approach based on the CRISP-DM methodology. Recent real-world data were collected from a from historical data of an actual oil drilling process in Hassi Terfa field, situated in South of Algeria. During the modelling process. The goal was to predict the rate of penetration (ROP) based on input parameters that are commonly used at the oil drilling process (weight on bit, rotation per minute, mud density , spp, ucs). At the data preparation stage, the data were cleaned and variables were selected and transformed. Next, at the modeling stage, a regression approach was adopted, where three learning methods were compared : Artificial Neural Network, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of correlation. The results of the experiment show that the proposed approach is able to effectively use the engineering data to provide effective prediction ROP, the ROP prediction allows the drilling engineer to select the best combination of the input parameters to have a better advancement.