Peng Long, Han Guoqing, Sui Xianfu, Pagou Arnold Landjobo, Zhu Liying, Shu Jin
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China.
Shaanxi Key Laboratory of Carbon Dioxide Sequestration and Enhanced Oil Recovery, Xi'an 710075, China.
ACS Omega. 2021 Mar 16;6(12):8104-8111. doi: 10.1021/acsomega.0c05808. eCollection 2021 Mar 30.
It has been a great challenge for the oil and gas industry to timely identify any electrical submersible pump (ESP) abnormal performance to avoid ESP failure. Given the high cost of the ESP failure, more and more real-time surveillance systems are applied to monitor ESP performance to generate alarms in the case of failures. This paper presents a robust principal component analysis (PCA) model to perform fault detection for ESP systems continuously. A three-dimensional plot of scores of principal components was used to observe different patterns during the stable and failure periods. 47 cases of actual failure events and 40 cases of stable operating events were tested on the robust PCA model to generate prediction results. The testing results demonstrate that the robust PCA model has managed to identify 20 failure events before the actual failure time out of the 47 failure cases and has successfully distinguished all the 40 stable operating wells. This study has concluded that PCA has the potential to be used as a monitoring platform to recognize dynamic change and therefore to predict the developing failures in the ESP system.
对于石油和天然气行业来说，及时识别任何潜油电泵（ESP）的异常性能以避免ESP故障一直是一项巨大的挑战。鉴于ESP故障成本高昂，越来越多的实时监测系统被应用于监测ESP性能，以便在出现故障时发出警报。本文提出了一种稳健主成分分析（PCA）模型，用于持续对ESP系统进行故障检测。主成分得分的三维图用于观察稳定期和故障期的不同模式。在稳健PCA模型上对47例实际故障事件和40例稳定运行事件进行了测试，以生成预测结果。测试结果表明，稳健PCA模型在47个故障案例中成功在实际故障时间之前识别出20个故障事件，并成功区分了所有40口稳定运行的油井。本研究得出结论，PCA有潜力用作监测平台，以识别动态变化，从而预测ESP系统中不断发展的故障。
