A Novel Rate of Penetration Model Based on Support Vector Regression and Modified Bat Algorithm

Abstract

In the geological drilling process, predicting the rate of penetration (ROP) is significantly important for improving drilling efficiency and reducing non-drilling time. However, due to the drilling data pollution and the complex nonlinearity in the geological drilling process, a reliable and highly accurate ROP prediction model is not easy to construct, and the model accuracy is affected by the value of model hyperparameters. In order to overcome the difficulties in modeling, a novel ROP model is developed to handle abnormal data and achieve nonlinear fitting. First, a local outlier factor is introduced to automatically detect the abnormal data, and then replace it with the nearest normal data. Then, the support vector regression (SVR) method is applied to construct nonlinear prediction model for ROP, and a modified bat algorithm (MBA) is developed to solve the non-convex problem in determining optimal value of hyperparameters for SVR-based ROP model. The MBA has six modifications to improve the global search ability, which achieves better performance in global search ability compared with other nine algorithms based on the experiments of IEEE 2005 benchmark functions. The developed ROP prediction model that combines SVR and MBA methods is tested based on actual drilling data and a semi-physical system, and the simulation and application results show that the developed modeling method has higher ROP prediction accuracy compared with the other modeling methods.