Oil-Net: A Learning-Based Framework for Working Conditions Diagnosis of Oil Well Through Dynamometer Cards Identification

作者：Rong Ma, Haifeng Tian, Xiaochun Cheng, Yi Xiao, Qingxiao Xu, Xianchuan Yu · 发表于：IEEE Sensors Journal · 年份：2023 · DOI：10.1109/jsen.2023.3275933 · 被引用次数：10 · 研究领域：Oil and Gas Production Techniques、Reservoir Engineering and Simulation Methods、Fault Detection and Control Systems

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 article, we collect dynamometer cards from the famous Chinese Shengli Oil Field as research data. We first investigate properties of dynamometers under different working conditions and create a dynamometer card dataset. Furthermore, we introduce calibration/visualization preprocess methods and propose the Oil-Net 1-D/2-D identification models from the time-series and computer vision views, respectively. Experimental results indicate that compared with other machine learning and time-series classification methods, Oil-Net 1-D/2-D improves identification accuracy significantly. This study provides guidance for the design and implementation of learning-based oil well working conditions intelligent diagnosis system.