Research on the Prediction of Drilling Rate in Geological Core Drilling Based on the BP Neural Network

Abstract

The development of automation and intelligence in geological core drilling is not yet mature. The selection and improvement of drilling parameters rely mainly on experience, and adjustments are often made after drilling by evaluating the core, which introduces a lag and reduces the drilling efficiency. Therefore, this study first establishes a geological core drilling experiment platform to collect drilling data. Through the constructed geological core drilling experimental platform, the practical data at the drill bit can be directly obtained, solving the problem of the data from the surface equipment in the practical drilling differing from the practical data. Second, the back propagation (BP) algorithm is used to perform the ROP prediction, with weight on bit (WOB), torque (TOR), flow rate (Q), and rotation speed (RPM) as input parameters, and rate of penetration (ROP) as the output. Subsequently, correlation analysis is used to perform the feature parameter optimization, and the effects of bit wear and bit cutting depth on the experiment are considered. Finally, comparison with algorithms such as ridge regression, SVM and KNN shows that the ROP prediction model using the BP neural network has the highest prediction accuracy of 94.1%. The results provide a reference for ROP prediction and the automation of geological core drilling rigs.