Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Advanced Computational Intelligence and Intelligent Informatics

DOI: 10.20965/jaciii.2025.p0820

Authors: Kanghui Zeng, Min Wu, Chengda Lu, Xiao Yang, Zhejiaqi Ma

Institution: China University of Geosciences

Language: English

Topic: Drilling and Well Engineering

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: National Natural Science Foundation of China, Higher Education Discipline Innovation Project