摘要：Trips and failures are common occurrences in the Electric Submersible Pump (ESP) systems. Considering the high maintenance cost of the intervention and workovers post failures, ESPs connected to real-time monitoring and surveillance systems are used to fulfill proactive responses and early detection of incidents. Large amounts of dynamic and historical production data acquired from the surface and downhole sensors can help perform diagnostics and prognostics on ESPs. This study applies the Hotelling T-square statistic (T-2) and Squared Prediction Error (SPE) equations combined with Principal Component Analysis (PCA) method to construct a predictive model based on the collected ESP data. This predictive model provides a solution that enables to detect the developing ESP trips and failures in real time. A new input matrix corresponding to any period in the whole ESP life cycle is fed to the predictive model to obtain prediction results. The model validation analysis demonstrates that the predictive model can identify the stable operating regions of ESP and detect the potential ESP trips and failures before the actual failure time. This study concludes that this proposed diagnostic model can serve as a real-time platform to identify dynamic changes and therefore monitoring potential ESP trips and failures in real-time.

关键字：diagnostic model; electric submersible pump; health monitoring; principal component analysis; trip and failure prediction

ISSN号：1091-6466

卷、期、页：卷: 40期: 21页: 2669-2684

发表日期：2022-11-02

影响因子：1.267900

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,地学领域高质量科技期刊分级目录（国外T2）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：PETROLEUM SCIENCE AND TECHNOLOGY

参与作者：Pagou, Arnold Landjobo

通讯作者：彭龙

第一作者：韩国庆

论文类型：期刊论文

论文概要：彭龙,韩国庆,Pagou, Arnold Landjobo，Real-time anomaly detection methodology in the electric submersible pump systems，PETROLEUM SCIENCE AND TECHNOLOGY，2022，卷: 40期: 21页: 2669-2684

论文题目：Real-time anomaly detection methodology in the electric submersible pump systems