Measuring fluid flow rate passing through pipelines is a basic strategy for developing the infrastructure of fluid-dependent industries. It is a challenging issue for trade, transportation, and reservoir management purposes. Predicting the flow rate of fluid is also regarded as one of the crucial steps for the development of oil fields. In this study, a novel deep machine learning model, convolutional neural network (CNN), was developed to predict oil flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (T_f), upstream pressure (Pu), root differential pressure (√ΔP), percentage of base sediment and water (BS&W%), oil specific gravity (SG), kinematic viscosity (ν), and beta ratio (β, the ratio of pipe diameter to orifice diameter). Due to the absence of accurate and credible methods for determining Qo, deep learning can be a useful alternative to traditional machine learning methods. Justifying the promising performance of the developed CNN model over conventional machine learning models, three different machine learning algorithms, including radial basis function (RBF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were also developed and their prediction performance was compared with that of the CNN model. A sensitivity analysis was also performed on the influence degree of each input variable on the output variable (Qo). The study outcomes indicate that the CNN model provided the highest Qo prediction accuracy among all the four models developed by presenting a root mean squared error (RMSE) of 0.0341 m3/s and a coefficient of determination (R²) of 0.9999, when applied to the dataset of 3303 data records compiled from oil fields around Iran. The Spearman correlation coefficient analysis results display that √ΔP, Pu, and Tf were the most influential variables on the oil flow rate in respect of the large dataset evaluated.

测量通过管道的流体流速是开发流体相关行业基础设施的基本策略。对于贸易、运输和水库管理而言，这是一个具有挑战性的问题。预测流体的流速也被认为是油田开发的关键步骤之一。在这项研究中，开发了一种新颖的深度机器学习模型，即卷积神经网络 (CNN)，从七个输入变量（包括流体温度 (T _f)、上游压力(Pu)、根部压差(√ΔP)、底泥和水的百分比(BS&W%)、油比重(SG)、运动粘度(ν)和β比(β，管道的比值直径到孔口直径）。由于缺乏准确可靠的 Qo 确定方法，深度学习可以成为传统机器学习方法的有用替代方案。证明开发的 CNN 模型优于传统机器学习模型的有希望的性能，还开发了三种不同的机器学习算法，包括径向基函数 (RBF)、最小绝对收缩和选择算子 (LASSO) 和支持向量机 (SVM)并将它们的预测性能与CNN模型的预测性能进行了比较。还对每个输入变量对输出变量（Qo）的影响程度进行了敏感性分析。研究结果表明，CNN 模型在通过呈现 0.0341 m3/s 的均方根误差 (RMSE) 和决定系数 (R² ) 为 0.9999，当应用于伊朗周围油田汇编的 3303 条数据记录的数据集时。Spearman 相关系数分析结果表明，就评估的大数据集而言，√ΔP、Pu 和 Tf 是对油流量影响最大的变量。