Multi-well dynamic liquid-level prediction method of pumping well based on dynamic and static information feature fusion neural network

Abstract

In a rod pumping system, accurate prediction of dynamic liquid level is a key to rational optimization of pumping system parameters. At present, it is difficult to use a uniform prediction model for dynamic liquid-level prediction in different rod pumping systems. To this end, a method for predicting the dynamic liquid level of multiple wells in a pumping well based on dynamic and static information feature fusion (DSIFF) neural network is proposed in this paper. According to the principle of dynamic liquid-level calculation, suspension displacement, suspension load, pumping rod parameters, formation crude oil density, surface crude oil density, oil pressure, and casing pressure are used as inputs. The subnetwork feature extraction method is proposed for the problem of large network structure caused by the high dimension of the important dynamic data of suspension displacement and suspension load. The Huber loss function is used as the loss function of the prediction model for the case of abnormal data in the large amount of data required for model training. Finally, the results of comparative analysis show that the proposed method solves the problem of unified establishment of dynamic liquid-level prediction model for multiple oil wells and has better robustness for abnormal data.