Abdalla Ramez, Samara Hanin, Perozo Nelson, Carvajal Carlos Paz, Jaeger Philip
Clausthal University of Technology, Institute of Subsurface Energy Systems, Agricolastrasse 10, 38678 Clausthal-Zellerfeld, Germany.
ACS Omega. 2022 May 19;7(21):17641-17651. doi: 10.1021/acsomega.1c05881. eCollection 2022 May 31.
Electrical submersible pumps (ESPs) are considered the second-most widely used artificial lift method in the petroleum industry. As with any pumping artificial lift method, ESPs exhibit failures. The maintenance of ESPs expends a lot of resources, and manpower and is usually triggered and accompanied by the reactive process monitoring of multivariate sensor data. This paper presents a methodology to deploy the principal component analysis and extreme gradient boosting trees (XGBoosting) in predictive maintenance in order to analyze real-time sensor data to predict failures in ESPs. The system contributes to an efficiency increase by reducing the time required to dismantle the pumping system, inspect it, and perform failure analysis. This objective is achieved by applying the principal component analysis as an unsupervised technique; then, its output is pipelined with an XGBoosting model for further prediction of the system status. In comparison to traditional approaches that have been utilized for the diagnosis of ESPs, the proposed model is able to identify deeper functional relationships and longer-term trends inferred from historical data. The novel workflow with the predictive model can provide signals 7 days before the actual failure event, with an F1-score more than 0.71 on the test set. Increasing production efficiencies through the proactive identification of failure events and the avoidance of deferment losses can be accomplished by means of the real-time alarming system presented in this work.
潜油电泵（ESPs）被认为是石油工业中第二广泛使用的人工举升方法。与任何泵送人工举升方法一样，潜油电泵也会出现故障。潜油电泵的维护耗费大量资源和人力，并且通常由多变量传感器数据的反应式过程监测触发并伴随。本文提出一种在预测性维护中部署主成分分析和极端梯度提升树（XGBoosting）的方法，以分析实时传感器数据来预测潜油电泵的故障。该系统通过减少拆解泵送系统、检查系统以及进行故障分析所需的时间，提高了效率。这一目标是通过将主成分分析作为一种无监督技术来实现的；然后，将其输出与XGBoosting模型进行流水线处理，以进一步预测系统状态。与用于潜油电泵诊断的传统方法相比，所提出的模型能够识别从历史数据推断出的更深层次的功能关系和长期趋势。带有预测模型的新型工作流程可以在实际故障事件发生前7天提供信号，在测试集上的F1分数超过0.71。通过主动识别故障事件和避免延期损失来提高生产效率，可以通过本文提出的实时警报系统来实现。
