随机森林
分类器(UML)
故障检测与隔离
断层(地质)
计算机科学
数据挖掘
人工智能
地质学
地震学
执行机构
作者
Matheus Araújo Marins,Bettina D. Barros,Ismael Santos,Daniel Centurion Barrionuevo,Ricardo Emanuel Vaz Vargas,Thiago de M. Prego,Amaro A. de Lima,Marcello L. R. de Campos,Eduardo A. B. da Silva,Sérgio L. Netto
标识
DOI:10.1016/j.petrol.2020.107879
摘要
This papers deals with the automatic detection and classification of faulty events during the practical operation of oil and gas wells and lines. The events considered here are part of the publicly available 3W database developed by Petrobras, the Brazilian oil holding. Seven fault classes are considered, with distinct dynamics and patterns, as well as several instances of normal operation. A random forest classifier is employed with different statistical measures to identify each fault type. Three experiments are devised in order to evaluate the system performance in distinct classification scenarios. An accuracy rate of 94% indicates a successful performance for the proposed system in detecting real events. Also, the system’s time of detection was on average 12% of the transient period that precedes the fault steady-state.
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