Oil-Net: A learning-based framework for working conditions diagnosis of oil well through dynamometer cards identification

Working conditions identification of oil well relies on high-cost and slow-speed artificial inspection in past decades. Working conditions diagnosis through dynamometer cards identification based on machine learning has significant economic value and environmental meaning for oil production. In this...

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Bibliographic Details
Published in:IEEE sensors journal 2023-07, Vol.23 (13), p.1-1
Main Authors: Ma, Rong, Tian, Haifeng, Cheng, Xiaochun, Xiao, Yi, Xu, Qingxiao, Yu, Xianchuan
Format: Article
Language:English
Subjects:
Cards
Computer vision
Convolutional Neural Network
Diagnosis
Dynamometer Card
Dynamometers
Employee welfare
Inspection
Machine learning
Oil fields
Oil Well
Oil wells
Oils
Production
Pumps
Shape
Time series
Two dimensional models
Working Conditions Diagnosis
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Summary:Working conditions identification of oil well relies on high-cost and slow-speed artificial inspection in past decades. Working conditions diagnosis through dynamometer cards identification based on machine learning has significant economic value and environmental meaning for oil production. In this paper, we collect dynamometer cards from the famous Chinese Shengli Oil Field as research data. We firstly investigate properties of dynamometers under different working conditions and create a dynamometer card dataset. Further we introduce calibration/visualization preprocess methods, and propose the Oil-Net 1D/2D identification models from the time-series and computer vision views respectively. Experiment results indicate that comparing with other machine learning and time series classification methods, Oil-Net 1D/2D improve identification accuracy significantly. This study provides guidance for the design and implementation of learning-based oil well working conditions intelligent diagnosis system.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3275933