Anomalies in oil-producing wells can have detrimental financial implications, leading to production disruptions and increased maintenance costs. Machine learning techniques offer a promising solution for detecting and preventing such anomalies, minimizing these disruptions and expenses. In this study, we focused on detecting faults in naturally flowing offshore oil and subsea gas-producing wells, utilizing the publicly available 3W dataset comprising multivariate time series data. We conducted a comparison of different anomaly detection methods, specifically one-class classifiers, including Isolation Forest, One-class Support Vector Machine (OCSVM), Local Outlier Factor (LOF), Elliptical Envelope, and Autoencoder with feedforward and LSTM architectures. Our evaluation encompassed two variations: one with feature extraction and the other without, each assessed in both simulated and real data scenarios. Across all scenarios, the LOF classifier consistently outperformed its counterparts. In real instances, the LOF classifier achieved an F1-measure of 87.0% with feature extraction and 85.9% without. In simulated instances, the LOF classifier demonstrated superior performance, attaining F1 measures of 91.5% with feature extraction and 92.0% without. These results show an improvement over the benchmark established by the 3W dataset. Considering the more challenging nature of real data, the inclusion of feature extraction is recommended to improve the effectiveness of anomaly detection in offshore wells. The superior performance of the LOF classifier suggests that the boundaries of normal cases as a single class may be ill-defined, with normal cases better represented by multiple clusters. The statistical analysis conducted further reinforces the reliability and robustness of these findings, instilling confidence in their generalizability to a larger population. The utilization of individual classifiers per instance allows for tailored hyperparameter configurations, accommodating the specific characteristics of each offshore well.

产油井的异常可能会产生不利的财务影响，导致生产中断和维护成本增加。机器学习技术为检测和预防此类异常现象提供了一种有前景的解决方案，从而最大限度地减少这些干扰和费用。在本研究中，我们利用包含多元时间序列数据的公开 3W 数据集，重点检测自然流动的海上石油和海底产气井的故障。我们对不同的异常检测方法进行了比较，特别是一类分类器，包括隔离森林、一类支持向量机 (OCSVM)、局部离群因子 (LOF)、椭圆包络以及具有前馈和 LSTM 架构的自动编码器。我们的评估包括两种变化：一种有特征提取，另一种没有特征提取，每种都在模拟和真实数据场景中进行评估。在所有场景中，LOF 分类器的性能始终优于同类分类器。在实际情况中，LOF 分类器在使用特征提取的情况下实现了 87.0% 的 F1 测量，在不提取特征的情况下实现了 85.9% 的 F1 测量。在模拟实例中，LOF 分类器表现出卓越的性能，在使用特征提取时达到 91.5% 的 F1 测量值，在不使用特征提取时达到 92.0%。这些结果表明比 3W 数据集建立的基准有所改进。考虑到实际数据更具挑战性，建议纳入特征提取，以提高海上油井异常检测的有效性。LOF 分类器的卓越性能表明，作为单个类别的正常情况的边界可能是不明确的，而通过多个聚类更好地表示正常情况。进行的统计分析进一步增强了这些发现的可靠性和稳健性，增强了人们对其适用于更多人群的信心。每个实例使用单独的分类器可以进行定制的超参数配置，以适应每个海上油井的具体特征。