Open AccessResearch on Intelligent Energy-saving Algorithm of Oil Pumping Based on BP Neural Network
Author(s) -
Tianshi Liu,
Mengdi Shi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/4/042023
Subject(s) - electricity , artificial neural network , oil field , production (economics) , computer science , energy consumption , process engineering , engineering , petroleum engineering , automotive engineering , artificial intelligence , electrical engineering , economics , macroeconomics
In order to enhance the international competitiveness of China’s oilfields, this paper reduces the phenomenon of high energy consumption in low-permeability oilfields, reduce electricity costs, increase oil production, and improve oilfield efficiency. In this paper, an intelligent energy-saving algorithm based on BP neural network is proposed for continuous oil recovery. This algorithm analyzes the main influencing factors of oil production and collects sample data from two aspects of oil production and electricity consumption cost. The BP neural network model with single hidden layer is constructed. The number of hidden layer neurons is determined. The output of oil and the unit speed of oil pumping are predicted, and combined with the time-sharing electricity price to adjust the times of oil pumping unit time to reduce the cost of electricity. The experimental results show that the proposed intelligent pumping energy-saving algorithm effectively improves the pumping efficiency, can save the electricity cost to a certain extent, realizes the intelligent control of pumping unit, and makes the oil field achieve high production and efficiency.