Weight on Bit and Hook Load Prediction in Drilling Based on the GA-RF

Abstract Weight on bit (WOB) and hook load are important parameters in the drilling process, and the accurate application of WOB and hook load can improve drilling efficiency. The traditional mathematical model needs to consider many relevant factors, the model is difficult to establish, and the prediction accuracy of the model is poor. In order to overcome these problems, this study established a genetic algorithm-optimized random forest (GA-RF) prediction model for WOB and hook load based on the real-time drilling data of a block in the Sichuan Basin, China. First, the data are filtered and denoised, and the correlation analysis of drilling parameters is carried out. It is found that the linear correlation between the WOB and other drilling parameters was not high, while the linear correlation between the hook load and the standpipe pressure is up to 0.82. Then, a random forest (RF) optimized by a genetic algorithm (GA) is established according to the data. The prediction model transmits the number of decision trees and the maximum tree depth of GA to the RF. The RF model is used to deal with the nonlinear relationship of drilling parameters to WOB and hook load, and is compared with other models. The research results indicate that the GA-RF algorithm outperforms other algorithms in predicting WOB and hook load, with an R2 as high as 0.976 for predicting WOB and up to 0.998 for predicting hook load. A comparison of various prediction models reveals that the GA-RF model demonstrates high predictive accuracy for both WOB and hook load.