Rate of penetration (ROP) represents drilling speed and its productive time during drilling operations in oil and gas wells. A predictive model that links ROP to its influential parameters is essential to optimize ROP for minimizing drilling costs. This study implements a comprehensive data mining approach utilizing Python toolboxes to improve ROP prediction in directional wells, which has not been addressed as much as vertical wells with respect to the downhole weight on the bit (WOB) and cutting transport. To do so, seven functions, including influential parameters, were identified to impact ROP in directional drilling. Drilling data of seven directional wells from an offshore rig in a gas field was compiled to set up the input dataset. The data preprocessing methods, consisting of the modified Z-score and Savitzky–Golay (SG) smoothing filter, were utilized to remove outliers and reduce the noises in the input dataset. Multilayer perceptron (MLP) neural network and random forest regression models were employed comparatively to predict ROP, and their architectures were designed by tuning hyperparameters of the models. The models' accuracy was statistically and graphically assessed by using the K-fold cross-validation and statistical metrics. The random forest model was demonstrated to be superior to the MLP neural network model in terms of accuracy and speed. The results represent that using calculated downhole WOB instead of measured surface WOB in the input dataset reinforces the models’ accuracy in the prediction of ROP. Statistical investigations such as partial correlation, mutual information, and permutation feature importance revealed that the cutting transport function can affect ROP in directional drilling as significantly as other influential parameters, which has not been mainly accounted for in the literature when establishing models for ROP prediction.

钻速 (ROP) 表示油气井钻井作业期间的钻井速度及其生产时间。将 ROP 与其影响参数相关联的预测模型对于优化 ROP 以最大限度地降低钻井成本至关重要。本研究实施了一种利用 Python 工具箱的综合数据挖掘方法，以改进定向井的 ROP 预测，而定向井在井下钻压 (WOB) 和岩屑运移方面的研究不如直井多。为此，确定了影响定向钻井 ROP 的七个函数，包括有影响力的参数。编译了气田海上钻井平台的七个定向井的钻井数据，以建立输入数据集。数据预处理方法，包括修改后的 Z 分数和 Savitzky–Golay (SG) 平滑滤波器，用于去除异常值并减少输入数据集中的噪声。比较采用多层感知器 (MLP) 神经网络和随机森林回归模型来预测 ROP，并通过调整模型的超参数来设计它们的体系结构。使用 K 折交叉验证和统计指标对模型的准确性进行统计和图形评估。随机森林模型被证明在准确性和速度方面优于 MLP 神经网络模型。结果表明，在输入数据集中使用计算的井下 WOB 而不是测量的表面 WOB 增强了模型预测 ROP 的准确性。偏相关、互信息等统计调查，