Fault detection and classification in oil wells and production/service lines using random forest
| Autor: | Thiago de M. Prego, Eduardo A. B. da Silva, Sergio L. Netto, Ricardo Emanuel Vaz Vargas, Bettina D 'Avila Barros, Marcello L. R. de Campos, Matheus Araújo Marins, Amaro A. de Lima, Daniel C. Barrionuevo, Ismael Santos |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Service (systems architecture)
Computer science 02 engineering and technology 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology computer.software_genre Fault (power engineering) 01 natural sciences Fault detection and isolation Random forest Fuel Technology 020401 chemical engineering Production (economics) Transient (computer programming) Data mining 0204 chemical engineering computer 0105 earth and related environmental sciences |
| Zdroj: | Journal of Petroleum Science and Engineering. 197:107879 |
| ISSN: | 0920-4105 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect