In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the R², MAE, RMSE, RAE, and RRSE were (0.9616, 3.9008, 5.8698, 24.5259%, and 27.4406%) and (0.8964, 6.937, 10.318, 43.3841%, and 45.6581%), respectively. The obtained results, in analyzing the training and testing datasets, proved that DT is the best predictive network to predict the percentage of drag reduction.

在石油工业中，减阻剂已被用于减少流体的湍流摩擦。这项研究的主要工作是检查四种新颖的机器学习模型（多层感知器，M5Rules，决策表（DT）和树M5P）的可行性，以估计减阻百分比。然后，利用上述方法来识别原油管道系统的输入和输出参数之间的关系。减阻的参数百分比被视为基本输出。相反，输入参数选择了油的流速，聚合物浓度，聚合物种类，温度以及管道直径和粗糙度。根据几种已知的统计指标，即确定系数（R ²），平均绝对误差（MAE），均方根误差（RMSE），相对绝对误差（RAE）和均方根误差（RRSE）以及色彩强度评级和总评级方法的新型评级系统。R ²，MAE，RMSE，RAE和RRSE的DT学习方法的训练和测试结果分别为（0.9616、3.9008、5.8698、24.5259％和27.4406％）和（0.8964、6.937、10.318、43.3841％和45.6581％）。在分析训练和测试数据集时获得的结果证明，DT是预测减阻百分比的最佳预测网络。