[1]李克文,杜苁聪,黄宗超,等.CBiA-PSL抽油井异常工况预警模型[J].智能系统学报,2022,17(2):295-302.[doi:10.11992/tis.202106007]
LI Kewen,DU Congcong,HUANG Zongchao,et al.Early warning model for abnormal workingconditions of CBiA-PSL pumping wells[J].CAAI Transactions on Intelligent Systems,2022,17(2):295-302.[doi:10.11992/tis.202106007]
点击复制
LI Kewen,DU Congcong,HUANG Zongchao,et al.Early warning model for abnormal workingconditions of CBiA-PSL pumping wells[J].CAAI Transactions on Intelligent Systems,2022,17(2):295-302.[doi:10.11992/tis.202106007]
CBiA-PSL抽油井异常工况预警模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第2期
页码:
295-302
栏目:
学术论文—机器感知与模式识别
出版日期:
2022-03-05
- Title:
- Early warning model for abnormal workingconditions of CBiA-PSL pumping wells
- Keywords:
- convolutional neural network; bidirectional gated recurrent unit; attention; positive sharing loss; loss function; identification of abnormal working conditions; working condition diagnosis and early warning; data imbalance
- 分类号:
- TP18
- 摘要:
- 油田生产过程中，油井受各种因素的影响容易发生泵漏、管漏等异常工况，会降低油井产出甚至导致躺井，对异常工况预警是油田智能化管理的重要任务。基于CNN-BiGRU联合网络，提出一种改进的网络结构CBiA-PSL模型（CNN BiGRU attention-positive sharing loss），用于油井异常工况预警。模型利用CNN学习工况样本灰度图像的深度特征，BiGRU有效避免信息损失并加强CNN池化层特征的联系，注意力机制对隐藏状态加权计算以完成有效特征筛选。针对工况数据集不平衡的问题，提出正共享损失函数PSL，将异常数据（正类）划分为子类，每个子类都共享整个正类的损失，且给样本少的正类更高的权重。实验结果表明，CBiA-PSL模型预测效果更佳，对于异常类和整体的预测都有较高的精度。
- Abstract:
- In the process of oilfield production, which is affected by various factors, oil wells are prone to abnormal working conditions, such as pump and pipe leakages, which will reduce the output of oil wells and even lead to lying in wells. Early warning of abnormal working conditions is an important task of intelligent oilfield management. Based on the convolutional neural network–bidirectional gated recurrent unit (CNN–BiGRU) joint network, an improved network structure CNN–BiGRU attention-positive sharing loss (CBiA-PSL) model is proposed for early warning of abnormal oil well conditions. The model uses the CNN to learn the depth features of the gray image of the sample, BiGRU to effectively prevent information loss and strengthen the connection between the features of the CNN pool layer, and attention mechanism to weigh the hidden state to complete effective feature screening. To address the imbalance of the working condition dataset, a PSL function, which divides the abnormal data (positive class) into subclasses, is proposed. Each subclass shares the loss of the entire positive class and provides a high weight to the positive class with only a few samples. The experimental results show that the CBiA-PSL model has a better prediction effect and a higher accuracy for anomaly and overall prediction than other models.
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备注/Memo
收稿日期:2021-06-03。
基金项目:国家自然科学基金重大项目（51991365）;国家自然科学基金面上项目(61673396)
作者简介:李克文，教授，博士生导师，博士，CCF会员，主要研究方向为人工智能、软件工程、数据挖掘、深度学习。发表学术论文100余篇;杜苁聪，硕士研究生，主要研究方向为深度学习、故障检测
通讯作者:李克文.E-mail:likw@upc.edu.cn
更新日期/Last Update:
1900-01-01