Semi-supervised Failure Prediction for Oil Production Wells. Liu, Y., Yao, K., Liu, S., Raghavendra, C. S., Balogun, O., & Olabinjo, L. In 2011 IEEE 11th International Conference on Data Mining Workshops, pages 434–441, December, 2011. ISSN: 2375-9259doi abstract bibtex In the petroleum industry, multivariate time series data is commonly used to monitor the performance of their assets, in which wells artificial lift systems are among the key assets that bring oil up to the surface. Failures frequently occur among these artificial lift systems, and they can greatly increase the operational expense due to loss of production and cost of repairs (also known as workovers). Predicting these failures before they occur can dramatically improve operational performance, such as by adjusting operating parameters to forestall failures or by scheduling maintenance to reduce unplanned repairs and to minimize downtime. Artificial lift failure prediction problem poses interesting challenges to data mining algorithms, because of the many real-world data issues, such as noise, missing data, delay of failure event logs, and large variability among normally functioning well artificial lift units. This paper presents the Smart Engineering Apprentice (SEA) framework that incorporates robust feature extraction algorithm, clustering and semi-supervised learning techniques, to enable learning of failure/normal patterns from noisy and poorly labeled multivariate time series, while achieving a high recall and precision for failures for real-world dataset.
@inproceedings{liu_semi-supervised_2011,
title = {Semi-supervised {Failure} {Prediction} for {Oil} {Production} {Wells}},
doi = {10.1109/ICDMW.2011.151},
abstract = {In the petroleum industry, multivariate time series data is commonly used to monitor the performance of their assets, in which wells artificial lift systems are among the key assets that bring oil up to the surface. Failures frequently occur among these artificial lift systems, and they can greatly increase the operational expense due to loss of production and cost of repairs (also known as workovers). Predicting these failures before they occur can dramatically improve operational performance, such as by adjusting operating parameters to forestall failures or by scheduling maintenance to reduce unplanned repairs and to minimize downtime. Artificial lift failure prediction problem poses interesting challenges to data mining algorithms, because of the many real-world data issues, such as noise, missing data, delay of failure event logs, and large variability among normally functioning well artificial lift units. This paper presents the Smart Engineering Apprentice (SEA) framework that incorporates robust feature extraction algorithm, clustering and semi-supervised learning techniques, to enable learning of failure/normal patterns from noisy and poorly labeled multivariate time series, while achieving a high recall and precision for failures for real-world dataset.},
booktitle = {2011 {IEEE} 11th {International} {Conference} on {Data} {Mining} {Workshops}},
author = {Liu, Yintao and Yao, Ke-Thia and Liu, Shuping and Raghavendra, Cauligi S. and Balogun, Oluwafemi and Olabinjo, Lanre},
month = dec,
year = {2011},
note = {ISSN: 2375-9259},
keywords = {Feature extraction, feature extraction, Hidden Markov models, scheduling, semi-supervised learning, artificial lift systems, asset performance monitoring, clustering, condition monitoring, data mining, Data mining, data mining algorithms, downtime minimization, failure analysis, failure prediction, hydrocarbon reservoirs, Labeling, lifts, maintenance engineering, Maintenance engineering, maintenance scheduling, multiple multivariate time series, multivariate time series data, oil production wells, petroleum, petroleum industry, Production, robust feature extraction algorithm, semi supervised failure prediction, smart engineering apprentice framework, time series, Time series analysis, unplanned repair reduction},
pages = {434--441},
file = {IEEE Xplore Abstract Record:C\:\\Users\\ktyao\\Zotero\\storage\\ZRZVC4RD\\6137412.html:text/html},
}
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Predicting these failures before they occur can dramatically improve operational performance, such as by adjusting operating parameters to forestall failures or by scheduling maintenance to reduce unplanned repairs and to minimize downtime. Artificial lift failure prediction problem poses interesting challenges to data mining algorithms, because of the many real-world data issues, such as noise, missing data, delay of failure event logs, and large variability among normally functioning well artificial lift units. 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