Abstract This main aim of this study is to generate an intensive artificial neural network model (ANN) based on FORTRAN language to develop a physical equation for oil rate prediction in wells lifted by ESP pumps. The backpropagation algorithm (BP) is selected in this study as a learning algorithm with its sigmoid curve based on the comparison performed against scaled conjugate gradient (SCG) and one-step secant (OSS) algorithms. 300 data points are collected from 2 fields in Gulf of Suez Egypt used in the ANN model. The results show that the optimum distribution for the collected data is of 70% and 30% for training and testing processes, respectively. This distribution yields the highest R2 of 0.988 and lowest mean square error of 0.025. Furthermore, based on the statistical analysis presented in this study, it has been found that the optimum number of hidden layers and neuron are one layer and two neuros, respectively. The newly ANN and correlation can predict the oil rate at the surface with accuracy exceeding 96% and that is extremely efficient. A comparison is conducted between the presented correlation in this study and other published correlations (Gilbert and Ros correlations) based on R2 value and mean square error. The results show that the new correlation has the highest R2 value with the lowest mean square error.

中文翻译：

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使用严格的人工神经网络技术预测人工流动井的多相流速

摘要 本研究的主要目的是生成基于 FORTRAN 语言的密集人工神经网络模型 (ANN)，以开发用于 ESP 泵提升的井中油率预测的物理方程。在本研究中选择反向传播算法 (BP) 作为一种学习算法，其 sigmoid 曲线基于对缩放共轭梯度 (SCG) 和一步割线 (OSS) 算法进行的比较。从 ANN 模型中使用的埃及苏伊士湾的 2 个油田收集了 300 个数据点。结果表明，对于训练和测试过程，所收集数据的最佳分布分别为 70% 和 30%。这种分布产生 0.988 的最高 R2 和 0.025 的最低均方误差。此外，根据本研究中的统计分析，已经发现隐藏层和神经元的最佳数量分别是一层和两个神经元。新的人工神经网络和相关性可以预测地表含油率，准确度超过 96%，非常有效。基于 R2 值和均方误差，在本研究中呈现的相关性与其他已发表的相关性（吉尔伯特和 Ros 相关性）之间进行了比较。结果表明，新的相关性具有最高的 R2 值和最低的均方误差。基于 R2 值和均方误差，在本研究中呈现的相关性与其他已发表的相关性（吉尔伯特和 Ros 相关性）之间进行了比较。结果表明，新的相关性具有最高的 R2 值和最低的均方误差。基于 R2 值和均方误差，在本研究中呈现的相关性与其他已发表的相关性（吉尔伯特和 Ros 相关性）之间进行了比较。结果表明，新的相关性具有最高的 R2 值和最低的均方误差。