LAUSR

Abstract Application of artificial intelligence in the accurate prediction of the rate of penetration (ROP), an important measure of drilling performance, has lately gained significant interest in oil and gas well drilling operations. Consequently, several computational intelligence techniques (CITs) for the prediction of ROP have been explored in the literature. This study explores the predictive capabilities of four commonly used CITs in the prediction of ROP and experimentally compare their predictive performance. The CIT algorithm utilizes predictors which are easily accessible continuous drilling data that have physical but complex relationship with ROP based on hydro-mechanical specific energy ROP model. The four CITs compared are the artificial neural network (ANN), extreme learning machine, support vector Regression and least-square support vector regression (LS-SVR). Two experiments were carried out; the first experiment investigates the comparative performance of the CITs while the second investigates the effect of reduced number of predictors on the performance of the models. The results show that all the CITs perform within acceptable accuracy with testing root mean square error range (RMSE) of 18.27–28.84 and testing correlation coefficient (CC) range of 0.71–0.94. LS-SVR has the best predictive performance in terms of accuracy with RMSE of 18.27 and CC of 0.94 while ANN has the best testing execution time at 0.03 s. Also utilizing the specific energy concept in chosen drilling parameters to be included among the predictors shows improved performance with five drilling parameters showing an improvement of 3%–9% in RMSE for LS-SVR in the two well studied. The utilization of the specific energy concept in the selection of the predictors in this study has demonstrated that the easily accessible drilling parameters have immense value to provide acceptable performance in the development of ROP model with CITs.