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博士生吕孝孝的论文被国际知名期刊“Journal of Petroleum Science and Engineering”(SCI二区 Top)录用

日期2021/4/12 16:20:43

近日,由王旱祥教授指导的博士生吕孝孝撰写的论文《An evolutional SVM method based on incremental algorithm and simulated indicator diagrams for fault diagnosis in sucker rod pumping systems》被“Journal of Petroleum Science and Engineering”SCI2 Top)录用。JPSE期刊涉及石油(天然气)的成因和聚集, 石油(天然气)地球化学,储层工程;,岩石力学 / 岩石物理学, 测井、测试和评价,数学模型,提高油气采收率,石油(天然气)地质学,石油(天然气)经济学,钻井和钻井液,多孔介质的热力学和相态行为、流体力学和多相流动,油层模拟法,生产工程,地层评价,勘探方法等方面的研究。IF:3.706

Fault diagnosis of the sucker rod pumping system (SRPS) is a challenging problem in the oil industry. Recently, the computer-aided indicator diagram (ID) recognition techniques are becoming useful measurements to help engineers monitor the wells. However, the slow variation of SRPS state makes it difficult for a well to experience all the fault conditions. Using the IDs collected from other oil wells as training data will lead to a large difference between the distribution of training data and target data because of the interference information including noise and system operation characteristics in IDs. Moreover, the different occurrence probability of each fault leads to the imbalance of training data, therefore the fault tolerance of the diagnosis models will result in lower accuracy in identifying the fault IDs with less training samples. In addition, due to one-off training, the parameters of diagnostic model are always fixed resulting in lack of adaptive ability. To address this issue, an evolutional support vector machine (SVM) method for SRPS diagnosis is proposed. First, to obtain balanced training data with similar distribution to the target data, a novel model describing the working process of SRPS under various fault conditions is established. Subsequently, the static apparent stiffness feature and its extraction algorithm is proposed to fully retain the fault information of SRPS in IDs. Then, the incremental SVM is used to construct the diagnosis model based on generated IDs. At last, the proposed method is verified experimentally through the system parameters and IDs of many wells collected from oilfields, and then some conventional techniques are employed in the comparison studies. The obtained results show that the accuracy of the diagnosis model trained by simulated IDs is 11.2% higher than that trained by collected IDs. Besides, the parameters of proposed diagnosis model can be continuously evolved to improve the diagnosis accuracy and generalization ability. Furthermore, the proposed diagnosis model also has advantages in training efficiency and memory consumption.