Drill bit deterioration estimation with the Random Forest Regressor

Abstract

Blastholes drilling performance is crucial for ensuring good performance of the whole excavation process, the correctness of which demands ‘healthy’ drill bit and appropriate behavior of an operator. Given the large volume of non-linear parameters describing the process, it appears reasonable to employ supervised learning methods to obtain drilling performance insights. Random Forest Regressor model has been trained on the dataset corresponding to correct performance of blastholes drilling and its hyperparameters have been tuned to obtain the highest possible accuracy. It has been later tested on three datasets corresponding to a good performance of drilling, and two cases of its non-optimal execution. Estimation errors are proposed to be used as bit technical state condition indicators (or more generally - process performance indicators). Root Mean Squared Error has been proven to differ significantly when compared estimation based on datasets corresponding to execution of drilling with ‘healthy’ drill bit, and its execution with worn-off one, however, it has been not sufficient to distinguish non-optimal drilling when additional feed pressure has been exerted by an operator to compensate the reduced pace of drilling. It has been, however, possible when the mean of absolute estimation errors has been used.

Data optimization for the knowledge bases in the oil and gas Monitoring-While-Drilling (MWD) Systems

“…The drilling process is characterized by high dynamics with shocks and vibrations (axial bit bounce, torsional stickslip chatter and lateral whirling), which is typical in other industries for the long shafts with small stiffness [19], [20]. The overall performance of drilling process is very sensitive to the condition of drill bits [21]- [24], tubes couplings [25] and geological properties (hardness, compaction etc.) [26].…”