试验装置
计算机科学
人工神经网络
潜水泵
卷积神经网络
试验数据
人工智能
混淆矩阵
数据集
均方误差
集合(抽象数据类型)
模式识别(心理学)
工程类
统计
数学
石油工程
程序设计语言
作者
DongJiang Liu,Guiliang Feng,Guoyong Feng,Lijie Xie
出处
期刊:Spe Journal
[Society of Petroleum Engineers]
日期:2024-01-01
卷期号:: 1-18
摘要
Electric submersible pump (ESP) is one of the common artificial lift technologies in offshore production systems. ESP failures are the main cause of the decline in the production efficiency of oil wells. Early warning and diagnosis of ESP failures are crucial to improve well production efficiency. In this study, a hybrid model of long short-term memory neural network and convolutional neural network (LSTM-CNN) for accurate early warning and diagnosis of ESP faults is proposed, based on electrical data as the basis of analysis. Using hyper-parameters to optimize the LSTM neural network structure and highly fit the field electrical data so that it can be applied to anomaly prediction before ESP faults, the results show that the optimized LSTM model with R2 (test set) = 0.79, root mean square error (RMSE) (test set) = 0.89, which can predict the future electrical data more accurately, and the predicted data are plotted in the polar coordinate system to simulate the ammeter card as the validation set. The ammeter card data set is simulated by collecting electrical data from different working conditions, which is expanded using data expansion, and different CNN models are trained to fine-tune the parameters using transfer learning. The results show that the GoogLeNet model has a significant diagnostic accuracy of 97%, which is 2% and 18% better than VGG16 and ResNet34, respectively. The model evaluation shows that the GoogLeNet model has good recall, F1-Score, and confusion matrix. The trained GoogLeNet model was applied to the validation set, and the hybrid LSTM-GoogLeNet model was found to be well-suited for ESP warning and diagnosis.
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