Distributed wireless sensing for fugitive methane leak detection

Klein, Levente J.; van Kessel, Theodore; Nair, Dhruv; Muralindar, Ramachandran; Hinds, Nigel; Hamann, Hendrik; Sosa, Norma

doi:10.1109/BigData.2017.8258502

Title: Distributed wireless sensing for fugitive methane leak detection

Abstract

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

Authors:

Klein, Levente J. ^[1]; van Kessel, Theodore ^[1]; Nair, Dhruv ^[1]; Muralindar, Ramachandran ^[1]; Hinds, Nigel ^[1]; Hamann, Hendrik ^[1]; Sosa, Norma ^[1]

IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

Publication Date:: Mon Dec 11 00:00:00 EST 2017

Research Org.:: IBM, Yorktown Heights, NY (United States)

Sponsoring Org.:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Contributing Org.:: METEC, Colorado State University

OSTI Identifier:: 1409489

Grant/Contract Number:: AR0000540

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Proceedings for IEEE Big Data 2017

Additional Journal Information:: Conference: IEEE Big Data, 2017 , Boston, MA (United States), 11 Dec 2017

Country of Publication:: United States

Language:: English

Subject:: 04 OIL SHALES AND TAR SANDS; wireless sensor network; fugitive methane gas; cloud analytics; computation at edge; data fusion

Citation Formats


                    Klein, Levente J., van Kessel, Theodore, Nair, Dhruv, Muralindar, Ramachandran, Hinds, Nigel, Hamann, Hendrik, and Sosa, Norma. Distributed wireless sensing for fugitive methane leak detection.  United States: N. p., 2017. 
Web.  doi:10.1109/BigData.2017.8258502.

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                    Klein, Levente J., van Kessel, Theodore, Nair, Dhruv, Muralindar, Ramachandran, Hinds, Nigel, Hamann, Hendrik, & Sosa, Norma. Distributed wireless sensing for fugitive methane leak detection.  United States.  https://doi.org/10.1109/BigData.2017.8258502

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                    Klein, Levente J., van Kessel, Theodore, Nair, Dhruv, Muralindar, Ramachandran, Hinds, Nigel, Hamann, Hendrik, and Sosa, Norma. Mon .  
"Distributed wireless sensing for fugitive methane leak detection".  United States.  https://doi.org/10.1109/BigData.2017.8258502.  https://www.osti.gov/servlets/purl/1409489.

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@article{osti_1409489,

  title        = {Distributed wireless sensing for fugitive methane leak detection},

  author       = {Klein, Levente J. and van Kessel, Theodore and Nair, Dhruv and Muralindar, Ramachandran and Hinds, Nigel and Hamann, Hendrik and Sosa, Norma},

  abstractNote = {Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.},

  doi          = {10.1109/BigData.2017.8258502},

  journal      = {IEEE Proceedings for IEEE Big Data 2017},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Dec 11 00:00:00 EST 2017},

  month        = {Mon Dec 11 00:00:00 EST 2017}

}

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Journal Article:

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Figures / Tables:

Fig 1: (A) The performance of various communication protocols and (B) the power and data rate specifications main communication technologies used in this study.

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