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:

针对钻进过程中的钻速预测与优化问题，提出了一种基于相关性分析的建模算法。对来自10口油气井的21912组数据进行分析。首先对数据进行了预处理，包括去除无关参数、填补缺失值、处理离群值、数据平滑和归一化等步骤，确保了数据质量的可靠性。随后，基于4种相关性计算方法（Pearson/Spearman/ Kendall/Chatterjee），分析了多个钻井参数与钻速之间的相关性，发现钻井液密度、固相含量、泵量等参数对钻速影响显著。在建模方法上，采用了多层感知机（MLP）和随机森林（RF）两种回归模型，结合不同数量的相关性参数进行训练和验证。对比不同相关性算法与特征数量对模型性能的影响，Pearson算法在多层感知机模型中表现最佳，而在随机森林模型中，所有算法的表现均较为稳定，R2值高达0.96，同时，随机森林较多层感知机模型表现更为优秀。结果表明，相关性分析不仅能够有效提取影响钻速的关键参数，还能优化模型的建模过程。结合机器学习和特征选择技术，本研究为钻速预测与优化提供了一种智能化的新途径，并为实际钻井工程提供了理论支持。

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.