Deep learning based dynamic prediction model of ESP well production

The dynamic prediction of electric submersible pump (ESP) well production is of guiding significance to recognize supply-discharge coordination of oil well and working condition of ESP equipment and improve working system, production rate and energy saving. In this paper, the relationships between the factors influencing the operating performance of ESP were analyzed by means of the Pearson correlation coefficient analysis method, based on the static and production dynamic data of ESP well and the working condition data of lifting equipment. Then, the data dimension was reduced by means of the principal component analysis method (PCA) to determine the main control parameters. In addition, the time series prediction model of ESP well production was established by using the long short-term memory (LSTM) and considering the change trend and relevance of the working conditions of ESP equipment comprehensively. Finally, the liquid production was predicted based on the on-site actual data of one certain oilfield and compared with the prediction result of BP neural network. The results show that the ESP well production predicted by LSTM model is highly accordant with the on-site actual value, indicating the prediction model has better fitting results and higher prediction accuracy. It considers factors more comprehensively, its application is more convenient and its prediction result is more reliable. It provides a new dynamic prediction method of ESP production and a basis for the adjustment of ESP’s working system and the reasonable selection and design of ESP.