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基于深度学习的抽油机井工况诊断方法
信息技术与网络安全
肖 翔
(中国科学技术大学 信息科学技术学院,安徽 合肥230026)
摘要:基于示功图对抽油机井下工况进行自动诊断是数字油田不可或缺的环节。通常通过人工提取示功图的特征向量,然后输入机器学习算法分类器识别井下工况。然而,特征的选择需要借助经验和先验知识,并且直接影响后续分类器的最终性能。而人工提取特征易受知识干扰,且在特征提取的过程中存在信息丢失,这决定了识别结果的上限。为此,受深度神经网络自动特征提取的启发,提出基于卷积神经网络的示功图的离线训练与在线诊断的方法。首先将挑选后的信号数据转换为图像数据,然后将图像二值化降低计算复杂度,最后基于改进的LeNet-5网络探究最适合模型的网络结构。最终通过实验与目前先进的算法进行对比,验证了本方法的有效性和可行性。
基于示功图对抽油机井下工况进行自动诊断是数字油田不可或缺的环节。通常通过人工提取示功图的特征向量,然后输入机器学习算法分类器识别井下工况。然而,特征的选择需要借助经验和先验知识,并且直接影响后续分类器的最终性能。而人工提取特征易受知识干扰,且在特征提取的过程中存在信息丢失,这决定了识别结果的上限。为此,受深度神经网络自动特征提取的启发,提出基于卷积神经网络的示功图的离线训练与在线诊断的方法。首先将挑选后的信号数据转换为图像数据,然后将图像二值化降低计算复杂度,最后基于改进的LeNet-5网络探究最适合模型的网络结构。最终通过实验与目前先进的算法进行对比,验证了本方法的有效性和可行性。
Research on diagnostic method for working conditions of pumping unit wells based on deep learning
Xiao Xiang
(School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China)
Abstract:It is an indispensable link to automatically diagnose downhole working conditions of pumping unit wells on dynamometer cards in digital oilfields. The feature vector of the dynamometer card is usually extracted manually and then identified downhole working conditions by the machine learning algorithm classifier as input. However, the selection of features requires experience and prior knowledge, then it directly affects the final performance of subsequent classifier. Features extracted manually are easily disturbed by knowledge, lead to the loss of key information, which determines the upper limit of the recognition result. Therefore, inspired by the automatic feature extraction of deep neural networks, this paper proposes the method of offline training and online diagnosis of dynamometer cards based on convolutional neural networks. Firstly the selected signal data is converted to image data. Then the image is binarized to reduce the complexity of computation. Finally based on the improved LeNet-5 network, we explore the network structure that is most suitable for the model. Compared with the current advanced algorithm, the validity and feasibility of this method are verified.
Key words :dynamometer card;downhole working conditions;automatic diagnosis;convolutional neural networks

0 引言

基于游梁式抽油机井的人工举升法[1]是目前主流的采油法。人工举升法以其结构简单、成本低廉、适应性强等优点著称。据不完全统计[2],全球超过90%的油田以及国内超过85%的油田都是采用这种方式进行原油开采。在油田开采的过程中,地下深井作业的环境较为复杂,容易引起井下抽油泵从正常的状态转变为故障的状态。若抽油泵长期处于故障状态,抽油泵设备会加速磨损使其生命周期骤缩,进一步影响油田的开采效率。由实时监测的数据快速、准确地诊断识别出抽油机井的工作状态,会给实际的开采提供有价值的信息,实现高效开采的同时将损耗降至最小。


本文详细内容请下载:http://www.chinaaet.com/resource/share/2000003682




作者信息:

肖 翔

(中国科学技术大学 信息科学技术学院,安徽 合肥230026)


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