Fault early warning of electric submersible pump based on long short-term memory neural network

Key words:  electric submersible pump  long short-term memory(LSTM) neural network  one-class support vector machine  fault early warning

Abstract:

Using the operating current of the electric submersible pump unit as the main criterion, this paper proposes to apply the long and short--term memory neural network to the prediction of the operating state of the electric submersible pump. For the fault types with unobvious characteristics, the operating voltage, operating current, power, oil pressure, wellhead temperature and instantaneous flow data are combined to predict the current value at the next moment. The one-class support vector machine model is used to predict the operating status of the electric submersible pump unit to achieve the early warning of the fault of the electric submersible pump. Finally, Actual production data are used to verify the model. The results show that the prediction accuracy of the method proposed in this paper is high, and the alarm time can be advanced by one hour, and finally the early warning and diagnosis of fault can be achieved.