Oil and gas extraction is vital for meeting the energy needs of a growing global population. Artificial lift (AL) systems play a crucial role in oilfields, especially when reservoir pressure is low. Among these systems, sucker rod pumps (SRPs) are extensively employed for onshore hydrocarbon recovery operations. However, SRPs are susceptible to mechanical failures and operational challenges arising from evolving reservoir conditions. This study proposes an approach for fault diagnosis in SRPs using real-time data from sensors and machine learning (ML) algorithms to detect anomalies and patterns associated with potential problems. The proposed method involves deep learning (DL) for automated feature extraction from dynamometer cards and error-correcting output codes (ECOC) model-based supervised machine learners for efficiently assessing the operational state of the SRP. By forecasting potential failures, preemptive measures can be taken to minimize downtime and reduce maintenance costs. The application of ML in the analysis of SRP provides a potential tool for diagnosing and predicting failures, improving productivity efficiency, and reducing downtime. Also, the accuracy of Convolutional Neural Networks (CNN) models was compared with that of CNN-ECOC model-based learners in this study, and the results demonstrated that integrating algorithms such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) with the network caused significant improvements in the accuracy; however, Decision Tree (DT) and Naive Bayes (NB) did not perform well in the pattern classification task. Specifically, the GoogLeNet model achieved improved accuracy of 96.40 % and 99.40 % when SVM and k-NN were integrated, respectively. Conversely, with DT and NB, the accuracies dropped to 66.30 % and 75.10 %, respectively.

中文翻译：

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使用 googlenet 集成机器学习分类器通过迁移学习实现抽油杆泵故障自动诊断


石油和天然气开采对于满足不断增长的全球人口的能源需求至关重要。人工举升 （AL） 系统在油田中发挥着至关重要的作用，尤其是在油藏压力较低时。在这些系统中，抽油杆泵 （SRP） 广泛用于陆上碳氢化合物回收作业。然而，SRP 容易受到机械故障和不断变化的储层条件引起的操作挑战的影响。本研究提出了一种使用来自传感器的实时数据和机器学习 （ML） 算法来检测与潜在问题相关的异常和模式的 SRP 故障诊断方法。所提出的方法涉及深度学习 （DL） 用于从测功机卡中自动提取特征，以及基于纠错输出代码 （ECOC） 模型的监督机器学习，用于有效评估 SRP 的运行状态。通过预测潜在故障，可以采取先发制人的措施来最大限度地减少停机时间并降低维护成本。ML 在 SRP 分析中的应用为诊断和预测故障、提高生产力效率和减少停机时间提供了一种潜在的工具。此外，在本研究中，将卷积神经网络 （CNN） 模型的准确性与基于 CNN-ECOC 模型的学习者的准确性进行了比较，结果表明，将支持向量机 （SVM） 和 k 最近邻 （k-NN） 等算法与网络集成导致准确性的显著提高;然而，决策树 （DT） 和 Naive Bayes （NB） 在模式分类任务中表现不佳。具体来说，当 SVM 和 k-NN 集成时，GoogLeNet 模型的准确率分别提高了 96.40% 和 99.40%。相反，使用 DT 和 NB 时，准确率下降到 66。分别为 30 % 和 75.10 %。