A highly accurate and robust prediction framework for drilling rate of penetration based on machine learning ensemble algorithm

• Published in: Geoenergy Science and Engineering Vol. 244; p. 213423
• Main Authors: Yang, Yuxiang, Cen, Xiao, Ni, Haocheng, Liu, Yibin, Chen, Zhangxing John, Yang, Jin, Hong, Bingyuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2025
• Subjects: Ensemble algorithm; Machine learning; Rate of penetration; Robustness; Ensemble algorithm; Robustness; Rate of penetration; Machine learning
• ISSN: 2949-8910; 2949-8910
• DOI: 10.1016/j.geoen.2024.213423

The rate of penetration (ROP) is a key indicator of drilling efficiency. Many researchers have explored the application of machine learning in ROP prediction. However, few studies have focused on the robustness of the constructed models, and developing a ROP prediction model that can achieve both high accuracy and strong robustness remains a challenge. This paper introduces a novel machine learning approach to constructing a ROP prediction model through ensemble learning algorithms. The model is based on field data from oilfields, incorporating 16 input parameters that influence ROP. First, the feasibility of the collected dataset is verified using correlation analysis. Then, ROP prediction models are developed based on various machine learning algorithms, including Decision Tree Regression (DTR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). By comparing the performance of these models under noise levels of 0%, 1.7%, and 5.1%, RF, LGBM, XGB, and SVR are selected as base learners. These base learners are then combined to construct multiple ensemble models, and the performance of the optimal ensemble model is evaluated under varying noise levels. The results show that the prediction error of the optimal model remains within 10%, and R2 is greater than 0.96. Finally, the Shapley Additive Explanations (SHAP) method is applied to perform interpretability analysis on the optimal ROP prediction model, examining the impact of different input factors on the model's predictive performance. Compared to single models and other ensemble models, the proposed ensemble model not only achieves higher accuracy but also demonstrates strong robustness and generalization capability. •Base learners are selected for ensemble models by analyzing the performance of single models.•Compared to other models, the proposed model achieves better performance.•Introduced SHAP method to explore the effect of various factors on model performance.•An ensemble predictive model for ROP is proposed, showing both high accuracy and strong robustness.