In order to ensure the stable production of ESP oil wells and improve the operating time of ESP,the real-time parameter changes during the operation of ESP were studied.The data characteristics of real-time motor,production and pump conditions during gas lock,oil nozzle blockage,emulsification and sand production in the production process of ESP well in an oil field in Bohai Sea were analyzed,and the parameter characteristics of electric submersible pump were summarized.The parameters of abnormal production of ESP were collected as training samples and test samples,and the deep neural network method was used to simulate and learn these sample data.A deep neural network model for predicting abnormal production of ESP is obtained.This model can monitor and forecast the state of ESP running by analyzing the real-time data of ESP wells,and can also build a case library of ESP wells state by analyzing history data to maintain the steady running of ESP.
关键词
电潜泵/实时数据/深度神经网络/运行状态
Key words
electric submersible pump(ESP)/real-time data/deep neural networks/production status
维普
万方数据