Prediction of Rate of Penetration in Drilling Process with Bayesian-Optimized Gaussian Process Regression

Predicting the rate of penetration (ROP) is essential for improving drilling efficiency by optimizing the operational parameters. Accurate ROP prediction facilitates better decision-making, reduces drilling costs, and helps obtain optimal operational parameters. This paper proposes a new prediction model that combines Gaussian process regression and Bayesian optimization methods. First, the interquartile range and Savitzky-Golay filtering methods are used to denoise the data. To reduce model redundancy, appropriate input variables are identified based on Spearman correlation analysis. Second, a Gaussian process regression model tuned using Bayesian optimization is established to predict the ROP. Finally, public data sourced from the UTAH FORGE Well 58-32 dataset are used to validate the proposed model. The results indicate that the proposed model offers reliable prediction accuracy and enhances the ROP during drilling.