基于3D注意力残差的井场偷油行为识别算法  

Algorithm for Identifying Oil Stealing Behavior in Wellsite Based on 3D Attention Residual

作　　者：张岩[1] 肖坤 汪靖哲 张林军 ZHANG Yan;XIAO Kun;WANG Jingzhe;ZHANG Linjun(School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318

出　　处：《吉林大学学报(信息科学版)》2024年第6期1090-1099,共10页Journal of Jilin University(Information Science Edition)

基　　金：东北石油大学特色科研团队“智慧油田信息处理创新团队”基金资助项目(2023TSTD-04)。

摘　　要：由于井场偷油现象是影响油田的安全生产与稳定运营的重要问题,而目前行为识别方法较少关注井场偷油检测的需求,且在对井场偷油目标进行特征识别过程中通常存在局限。为此,提出一种基于3D注意力残差的井场偷油行为识别算法。该网络由多个三维注意力残差块组成,通过在每个残差块中嵌入通道和时空注意力模块,获取更多的特征判别信息,以增强模型对重要特征的关注。在井场偷油行为数据集上对算法的有效性进行验证,实验结果表明,相较同类算法,该方法具有更高的识别准确率,可为油田井场偷油行为自动检测的实际应用提供参考。The phenomenon of oil theft at well sites is an important issue that affects the safe production and stable operation of oil fields.The current behavior recognition methods pay less attention to the need for detecting oil theft in well pads,and there are often limitations in the application of the oil theft target feature recognition process.An algorithm for identifying oil theft behavior at well sites is proposed based on 3D attention residuals.This network consists of multiple three-dimensional attention residual blocks,which embed channels and spatiotemporal attention modules in each residual block to obtain more feature discrimination information and enhance the model's attention to important features.The effectiveness of the algorithm is varified on the dataset of oil theft behavior at the well site.The experimental results indicate that,compared to similar algorithms,this method has higher recognition accuracy.It can provide a reference for the practical application of automatic detection of oil theft behavior in oilfield well sites.

关 键 词：井场偷油 三维卷积 行为识别 残差模块 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]