School of Mechanics Science & Engineering, Northeast Petroleum University, Daqing, Heilongjiang, China.
The Second Oil Production Plant of Daqing Oilfield Co., Ltd., Daqing, Heilongjiang, China.
PLoS One. 2020 Nov 16;15(11):e0242458. doi: 10.1371/journal.pone.0242458. eCollection 2020.
The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.
直接确定潜油螺杆泵的失效模式较为困难，这将缩短系统的使用寿命和油井的正常生产。本文旨在准确、高效地识别潜油螺杆泵的故障形式，提出了一种基于随机森林的潜油螺杆泵故障诊断方法。在 Hadoop 大数据处理平台上建立了 HDFS 存储系统和 MapReduce 处理系统；采用 Bagging 算法收集训练集数据。同时，本文采用 CART 方法建立样本库和随机森林模型的决策树。使用六组连续变量、四组分类变量和潜油螺杆泵采油系统的故障类别对决策树进行训练。由于几个决策树构成了一个随机森林模型，因此将待测试的参数输入到随机森林模型中，然后使用各种类型的决策树来确定潜油螺杆泵中的故障类别。验证结果表明，故障诊断的准确率为 92.86%。本文可为及时发现井下机组故障原因、减少油井产量损失、加快潜油螺杆泵在油田的推广应用提供一些有意义的指导。
