基于深度时序网络的固井泵压回归预测研究及应用  

Research and Application of Cementing Pump Pressure Regression Prediction Based on Depth Time Series Network

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作  者:郭磊鑫 李毛毛 莫峦奇 GUO Lei-xin;LI Mao-mao;MO Luan-qi(Shool of Computer Science and Software Engineering,Southwest Petroleum University,Chengdu 610000,China)

机构地区:[1]西南石油大学计算机与软件学院,四川成都610000

出  处:《电脑与信息技术》2024年第4期51-53,共3页Computer and Information Technology

基  金:西南石油大学大学生创新创业训练计划项目(项目编号:S202210615127)。

摘  要:在固井作业中,泵压大小代表了目前固井作业的质量和施工状态,提前预测泵压有利于及时调整固井作业参数,提高施工效率,以及预防事故的发生。文章旨在通过固井作业现场的实时数据采集,对泵压进行精准的回归预测。利用现场实地采集的特征数据,以及依据作业机理和流程构建的计算特征,构建出与时间序列紧密相关的数据集。在此基础上,进一步提炼与泵压变化相关的特征数据,搭建深度时序模型。此模型旨在学习并捕捉具有时序特性的数据与泵压之间的复杂映射关系,从而实现对泵压的有效预测。In cementing operations,the size of pump pressure represents the quality and construction status of the current cementing operation.Predicting pump pressure in advance is conducive to timely adjusting cementing operation parameters,improving construction efficiency,and preventing accidents.This paper aims at accurate regression prediction of pump pressure through real-time data acquisition in cementing field.By using the feature data collected on the spot and the calculation features constructed according to the operation mechanism and process,the data set closely related to the time series is constructed.On this basis,the characteristic data related to pump pressure changes are further extracted and the depth time series model is built.This model is designed to learn and capture the complex mapping relationship between data with time series characteristics and pump pressure,so as to achieve effective prediction of pump pressure.

关 键 词:石油固井 时序网络 回归预测 泵压预测 

分 类 号:TP18[自动化与计算机技术—控制理论与控制工程] TE256[自动化与计算机技术—控制科学与工程]

 

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