Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Underground resources, particularly hydrocarbons, are critical assets that promote economic development on a global scale. Drilling activities are necessary for the extraction and recovery of subsurface energy resources, and the rate of penetration (ROP) is one of the most important drilling parameters. This study forecasts the ROP using drilling data from three Iranian wells and hybrid LSSVM-GA/PSO algorithms. These algorithms were chosen due to their ability to reduce noise and increase accuracy despite the high level of noise present in the data. The study results revealed that the LSSVM-PSO method has an accuracy of roughly 97% and is more precise than the LSSVM-GA technique. The LSSVM-PSO algorithm also demonstrated improved accuracy in test data, with RMSE = 1.92 and R-2 = 0.9516. Furthermore, it was observed that the accuracy of the LSSVM-PSO model improves and degrades after the 50th iteration, whereas the accuracy of the LSSVM-GA algorithm remains constant after the 10th iteration. Notably, these algorithms are advantageous in decreasing data noise for drilling data.

Automated summary

Underground resources, especially hydrocarbons, play a crucial role in global economic development. This study focuses on the rate of penetration (ROP), an important drilling parameter, and predicts it using drilling data from Iranian wells and hybrid LSSVM-GA/PSO algorithms. The results show that the LSSVM-PSO method achieves higher accuracy and effectively reduces data noise in drilling data.