Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Study on Computer Monitoring System for Oilfield Joint Stations Based on the BP Neural Network PID Control Algorithm

Version 1 : Received: 18 May 2021 / Approved: 20 May 2021 / Online: 20 May 2021 (09:07:15 CEST)

How to cite: Xu, J.; qiao, Y. Study on Computer Monitoring System for Oilfield Joint Stations Based on the BP Neural Network PID Control Algorithm. Preprints 2021, 2021050458 (doi: 10.20944/preprints202105.0458.v1). Xu, J.; qiao, Y. Study on Computer Monitoring System for Oilfield Joint Stations Based on the BP Neural Network PID Control Algorithm. Preprints 2021, 2021050458 (doi: 10.20944/preprints202105.0458.v1).

Abstract

Abstract: The Hanqu Joint Station of the Dingbian Oil Production Plant of Yanchang Oilfield Co., Ltd is located at the edge of the desert in northern China. the bad field conditions and strong sandstorm, the hardware of computer monitoring system of Joint Station is often damaged. At the same time, the core equipment of the joint station three-phase separator oil chamber liquid level is hard to achieve high precision constant value control, the general control algorithm is difficult to meet the control requirements.This paper proposed a design scheme of a oilfield joint station computer monitoring system based on the Siemens S7-300 PLC, the hardware of the monitoring system adopts the redundancy scheme of dual monitoring computers, dual programmable logic controllers (PLCs), and dual industrial Ethernet. The BP neural network PID control algorithm was used to realize constant value control of the oil chamber liquid level of the three-phase separator of the core equipment of the joint station,and realized high control precision. The monitoring system could well adapt to the harsh environment of the scene, and showed high reliability and efficiency.

Subject Areas

Monitoring system; joint station; BP neural network; PID

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