钻探工程2025，Vol.52Issue(z1)：112-118,7.DOI:10.12143/j.ztgc.2025.S1.017
基于不同相关性的钻速预测模型对比
Comparison of ROP prediction models based on different correlation measures
摘要
Abstract
A correlation-based modeling algorithm is proposed to address the prediction and optimization of the rate of penetration(ROP)in the drilling process.By analyzing 21912 data points from ten oil and gas wells,the data were preprocessed,including the removal of irrelevant parameters,filling missing values,handling outliers,data smoothing,and normalization to ensure data quality and reliability.Subsequently,four correlation calculation methods(Pearson/Spearman/Kendall/Chatterjee)were used to analyze the relationships between various drilling parameters and ROP,revealing that parameters such as drilling fluid density,solid-phase content,and pump rate significantly impact ROP.In the modeling approach,two regression models,Multi-Layer Perceptron(MLP)and Random Forest(RF),were employed,and different quantities of correlation parameters were used for training and validation.Pearson's method performs best in the MLP model,while the performance of all algorithms in the RF model remains stable with an R2 value reaching 0.96.Additionally,the RF model outperforms the MLP model.The findings indicate that correlation analysis not only effectively extracts key parameters affecting ROP but also optimizes the modeling process.By combining machine learning and feature selection techniques,this study provides an intelligent new approach for ROP prediction and optimization and offers theoretical support for practical drilling engineering.关键词
钻速预测/相关性分析/机器学习/特征选择/数据预处理Key words
rate of penetration prediction/correlation analysis/machine learning/feature selection/data preprocessing分类
天文与地球科学引用本文复制引用
魏思维,李谦,何俊杰,李同意,姜杰..基于不同相关性的钻速预测模型对比[J].钻探工程,2025,52(z1):112-118,7.基金项目
四川省自然科学基金青年科学基金项目"基于数字孪生的动态时变钻进工况自适应迁移模型研究"(编号:2024NSFSC0817) （编号:2024NSFSC0817）
