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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells
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Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

机译：使用机器学习在吸盘杆泵浦井中的操作条件和传感器故障诊断

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摘要

In sucker-rod pumping wells, due to the lack of an early diagnosis of operating condition or sensor faults, several problems can go unnoticed. These problems can increase downtime and production loss. In these wells, the diagnosis of operation conditions is carried out through downhole dynamometer cards, via pre-established patterns, with human visual effort in the operation centers. Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification task and best practices for solving the problem. In the search for answers to these questions, this work carried out sixty tests with more than 50,000 dynamometer cards from 38 wells in the Mossoró, RN, Brazil. In addition, it presented test results for three algorithms (decision tree, random forest and XGBoost), three descriptors (Fourier, wavelet and card load values), as well as pipelines provided by automated machine learning. Tests with and without the tuning of hypermeters, different levels of dataset balancing and various evaluation metrics were evaluated. The research shows that it is possible to detect sensor failures from dynamometer cards. Of the results that will be presented, 75% of the tests had an accuracy above 92% and the maximum accuracy was 99.84%.
机译：在吸盘杆泵送井中，由于缺乏早期诊断操作条件或传感器故障，有几个问题可以忽略。这些问题可以增加停机时间和生产损失。在这些井中，通过井下测功率卡，通过预先建立的图案进行操作条件的诊断，并在操作中心的人类视觉努力中进行。从机器学习算法开始，已经发表了几篇论文，但仍然是有关测力计卡分类任务的难度水平和解决问题的最佳实践的疑虑。在寻找这些问题的答案中，这项工作进行了六十个测试，其中38个井中的38个井中的50,000张测力计卡，巴西。此外，它还呈现了三种算法（决策树，随机林和XGBoost）的测试结果，三个描述符（傅立叶，小波和卡负载值），以及由自动化机器学习提供的管道。在没有高度调整的情况下进行测试，评估不同程度的数据集平衡和各种评估度量。该研究表明，可以检测测功机卡的传感器故障。将呈现的结果，75％的测试的精度高于92％，最大精度为99.84％。

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