Correlation-Optimized Modeling Algorithm for Rate of Penetration

Aiming at the problems of low prediction accuracy of rate of penetration (ROP) during drilling in complex formations and strong non-monotonic, nonlinear coupling among parameters: traditional correlation coefficients such as Pearson and Spearman mainly measure linear or monotonic relationships, which cannot effectively characterize such complex dependencies and tend to result in underestimation of key features, this paper aims to develop a dynamic optimal correlation modeling algorithm. This approach is designed to enhance both the accuracy and engineering applicability of ROP prediction. Using 21, 912 data sets from ten oil and gas wells, we performed data preprocessing including median imputation for missing values, IQR-based outlier removal, and Savitzky-Golay smoothing. By comparing four correlation theories: Pearson, Spearman, Kendall, and Chatterjee: key parameters affecting ROP (such as mud density, solid content, and well depth) were identified. A sliding window technique (window size: 400, step size: 100) was introduced to achieve local dynamic modeling, with a Random Forest model serving as the core for regression prediction. Experimental results demonstrate that the Chatterjee algorithm performed best in screening the top 5 features. The architecture combining the sliding window technique with the Random Forest model achieved an average R² of 0.985 on the window test set of the 10th test well, representing a significant improvement over global static modeling (R²=0.96). The resulting optimal correlation ROP modeling algorithm can adapt to abrupt formation changes and effectively capture local correlations between parameters. This provides a high-precision solution for real-time ROP optimization and it provides effective technical support for the transformation of drilling engineering from empirical trial-and-error to data-driven decision-making.