Multiphase flow through both vertical and horizontal tubulars is getting higher interest in the oil and gas industry. Prediction of wellhead pressure through vertical wells is a very critical point that has a great influence on different applications. In this research, an artificial neural network with backpropagation technique (ANN-BP) was used to predict the wellhead pressure (WHP) for multiphase flow for vertical well systems. This permits the calculation of the pressure drop across the vertical well section by knowing the bottom hole flowing pressure (BHP). More than 150 data sets from different wells in the Middle East with different conditions were used to build the model. About 80% of the data were used to train the model while the rest unseen 20% were used to test and validate the model. The network structure, including the training function, the transfer function, the number of hidden layers, and the number of neurons in each layer, was highly optimized by trying different combinations of each parameter. The developed ANN model yielded high accuracy in predicting the WHP with an average absolute percentage error (AAPE) for both training and testing which are 0.61% and 1.13%, respectively. The optimized model comprised a single hidden layer with 20 neurons activated with the transfer function “tansig.” The correlation coefficient between the actual and predicted values for both training and testing was 0.98. A new empirical equation was then developed to mimic the developed ANN model by extracting the network weights and biases. The developed ANN-based correlation outweighs the previously established correlations in the literature upon comparison using unseen dataset.

通过垂直管和水平管的多相流在石油和天然气工业中受到越来越多的关注。通过垂直井的井口压力预测是非常关键的一点，对不同的应用有很大的影响。在这项研究中，使用带有反向传播技术的人工神经网络（ANN-BP）来预测垂直井系统多相流的井口压力（WHP）。这样就可以通过了解井底流动压力（BHP）来计算垂直井段的压降。使用来自中东不同井，条件不同的150多个数据集来构建模型。大约80％的数据用于训练模型，而其余未见的20％的数据用于测试和验证模型。网络结构 通过尝试每个参数的不同组合，高度优化了训练函数，传递函数，隐藏层数以及每层神经元数（包括训练函数）。所开发的ANN模型在预测WHP时具有很高的准确性，训练和测试的平均绝对百分比误差（AAPE）分别为0.61％和1.13％。优化的模型包括一个单独的隐藏层，其中包含20个神经元，这些神经元通过传递函数“ tansig”激活。训练和测试的实际值和预测值之间的相关系数是0.98。然后，通过提取网络权重和偏差，开发了一个新的经验公式来模拟已开发的ANN模型。