ESN Neural Network-Based Electrical Submersible Pump Model

Chuan Wang; Zhen Long

doi:10.54097/q19nxs69

Authors

Chuan Wang
Zhen Long

DOI:

https://doi.org/10.54097/q19nxs69

Keywords:

ESN-RLS network; PSO optimization parameters; Kalman filter method.

Abstract

This paper introduces an Electrical Submersible Pump (ESP) model based on the Echo State Network (ESN) neural network. ESPs are commonly used in oil extraction, and their performance is influenced by a variety of factors, making accurate modeling crucial for optimizing operations and maintenance. ESN is a type of recurrent neural network with unique dynamic memory capabilities, making it suitable for handling time-series data and modeling nonlinear dynamic systems. The article details the ESN network structure and its application in the ESP model, including how to use ESN to predict performance parameters and diagnose faults in ESPs. Compared with traditional models, the ESN model shows significant advantages in prediction accuracy and computational efficiency. The results indicate that the ESN-based ESP model can effectively simulate the dynamic behavior of ESPs, providing a powerful tool for real-time monitoring and fault prevention of ESPs.

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