Predicting Rate of Penetration in Ultra-deep Wells Based on Deep Learning Method.

The accurate prediction of the rate of penetration (ROP) is crucial for optimizing drilling parameters and enhancing drilling efficiency in ultra-deep wells. However, this task is challenging due to the harsh geological conditions, complex drilling processes, voluminous drilling data, and nonlinear relationships between drilling parameters and rock-breaking. In this study, a comprehensive intelligent model is proposed that combines clustering and deep residual neural network to address these challenges. Specifically, relevant feature parameters are selected for ROP prediction and the Savitzky–Golay filter is employed to reduce noise in the field data. Formations with similar rock characteristics are clustered using well logging parameters, including sonic logging and natural gamma ray logging, which indicate the formation rock properties. A deep residual neural network is then used to develop the prediction model, with the clustering results and 13 mud logging parameters serving as inputs. The model is trained and tested using field data from an ultra-deep reservoir in northwest China, and its performance is evaluated. The impact of data noise reduction, formation clustering, and deep residual neural network on the prediction accuracy is analyzed through ablation experiments. The proposed model achieves high accuracy in predicting ROP, with relative errors ranging from 11.34 to 11.44% and R2 values from 0.92 to 0.94. Compared to traditional machine learning models, the approach demonstrates superior performance and is suitable for real-time drilling applications. This study provides a promising solution for accurate ROP prediction in ultra-deep wells, helping to optimize drilling parameters and improve drilling efficiency. [ABSTRACT FROM AUTHOR]