Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters

Abstract

Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF), gradient boosting modeling (GBM), extreme gradient boosting (XGBoost), and adaptive boosting (Adaboost) models to generate ROP predictions. The models use well data from a 3200-m segment across the stratigraphic column (Dibdibba to Zubair formations) of the large West Qurna oil field in Southern Iraq, penetrating 19 formations and four oil reservoirs. The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies. The ROP predictive models were developed using 14 operational parameters: TVD, weight on bit (WOB), torque, effective circulating density (ECD), drilling rotation per minute (RPM), flow rate, standpipe pressure (SPP), bit size, total RPM, D exponent, gamma ray (GR), density, neutron, caliper, and discrete lithology distribution. Training and validation of the ROP models involves data compiled from three development wells. Applying Random subsampling, the compiled dataset was split into 85 % for training and 15 % for validation and testing. The test subgroup's measured and predicted ROP mismatch was assessed using root mean square error (RMSE) and coefficient of correlation (R²). The RF, GBM, and XGBoost models provide ROP predictions versus depth with low errors. Models with cross-validation that integrate data from three wells deliver more accurate ROP predictions than datasets from single well. The input variables' influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.