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Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series. / Wilson, R.E.; Eckley, I.A.; Nunes, M.A. et al.
In: Data Mining and Knowledge Discovery, Vol. 33, No. 3, 15.05.2019, p. 748-772.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wilson, RE, Eckley, IA, Nunes, MA & Park, T 2019, 'Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series', Data Mining and Knowledge Discovery, vol. 33, no. 3, pp. 748-772. https://doi.org/10.1007/s10618-018-00608-w

APA

Wilson, R. E., Eckley, I. A., Nunes, M. A., & Park, T. (2019). Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series. Data Mining and Knowledge Discovery, 33(3), 748-772. https://doi.org/10.1007/s10618-018-00608-w

Vancouver

Wilson RE, Eckley IA, Nunes MA, Park T. Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series. Data Mining and Knowledge Discovery. 2019 May 15;33(3):748-772. Epub 2019 Feb 20. doi: 10.1007/s10618-018-00608-w

Author

Wilson, R.E. ; Eckley, I.A. ; Nunes, M.A. et al. / Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series. In: Data Mining and Knowledge Discovery. 2019 ; Vol. 33, No. 3. pp. 748-772.

Bibtex

@article{6faf1d3ff69f4d708f0bccc39c7d11d2,
title = "Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series",
abstract = "Distributed acoustic sensing technology is increasingly being used to support production and well management within the oil and gas sector, for example to improve flow monitoring and production profiling. This sensing technology is capable of recording substantial data volumes at multiple depths within an oil well, giving unprecedented insights into production behaviour. However the technology is also prone to recording periods of anomalous behaviour, where the same physical features are concurrently observed at multiple depths. Such features are called {\textquoteleft}stripes{\textquoteright} and are undesirable, detrimentally affecting well performance modelling. This paper focuses on the important challenge of developing a principled approach to identifying such anomalous periods within distributed acoustic signals. We extend recent work on classifying locally stationary wavelet time series to an online setting and, in so doing, introduce a computationally-efficient online procedure capable of accurately identifying anomalous regions within multivariate time series. {\textcopyright} 2019, The Author(s).",
keywords = "Coherence, Distributed acoustic sensing, Dynamic classification, Locally stationary time series, Stripe detection, Wavelets, Coherent light, Gas industry, Oil well production, Oil wells, Acoustic sensing, Computationally efficient, Multivariate time series, On-line procedures, Sensing technology, Stationary time series, Time series",
author = "R.E. Wilson and I.A. Eckley and M.A. Nunes and T. Park",
year = "2019",
month = may,
day = "15",
doi = "10.1007/s10618-018-00608-w",
language = "English",
volume = "33",
pages = "748--772",
journal = "Data Mining and Knowledge Discovery",
issn = "1384-5810",
publisher = "Springer New York LLC",
number = "3",

}

RIS

TY - JOUR

T1 - Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series

AU - Wilson, R.E.

AU - Eckley, I.A.

AU - Nunes, M.A.

AU - Park, T.

PY - 2019/5/15

Y1 - 2019/5/15

N2 - Distributed acoustic sensing technology is increasingly being used to support production and well management within the oil and gas sector, for example to improve flow monitoring and production profiling. This sensing technology is capable of recording substantial data volumes at multiple depths within an oil well, giving unprecedented insights into production behaviour. However the technology is also prone to recording periods of anomalous behaviour, where the same physical features are concurrently observed at multiple depths. Such features are called ‘stripes’ and are undesirable, detrimentally affecting well performance modelling. This paper focuses on the important challenge of developing a principled approach to identifying such anomalous periods within distributed acoustic signals. We extend recent work on classifying locally stationary wavelet time series to an online setting and, in so doing, introduce a computationally-efficient online procedure capable of accurately identifying anomalous regions within multivariate time series. © 2019, The Author(s).

AB - Distributed acoustic sensing technology is increasingly being used to support production and well management within the oil and gas sector, for example to improve flow monitoring and production profiling. This sensing technology is capable of recording substantial data volumes at multiple depths within an oil well, giving unprecedented insights into production behaviour. However the technology is also prone to recording periods of anomalous behaviour, where the same physical features are concurrently observed at multiple depths. Such features are called ‘stripes’ and are undesirable, detrimentally affecting well performance modelling. This paper focuses on the important challenge of developing a principled approach to identifying such anomalous periods within distributed acoustic signals. We extend recent work on classifying locally stationary wavelet time series to an online setting and, in so doing, introduce a computationally-efficient online procedure capable of accurately identifying anomalous regions within multivariate time series. © 2019, The Author(s).

KW - Coherence

KW - Distributed acoustic sensing

KW - Dynamic classification

KW - Locally stationary time series

KW - Stripe detection

KW - Wavelets

KW - Coherent light

KW - Gas industry

KW - Oil well production

KW - Oil wells

KW - Acoustic sensing

KW - Computationally efficient

KW - Multivariate time series

KW - On-line procedures

KW - Sensing technology

KW - Stationary time series

KW - Time series

U2 - 10.1007/s10618-018-00608-w

DO - 10.1007/s10618-018-00608-w

M3 - Journal article

VL - 33

SP - 748

EP - 772

JO - Data Mining and Knowledge Discovery

JF - Data Mining and Knowledge Discovery

SN - 1384-5810

IS - 3

ER -