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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:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
Publication Date:
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:
Journal Article: 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.
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. doi:10.1109/BigData.2017.8258502.
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. doi:10.1109/BigData.2017.8258502.
@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}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on December 11, 2018
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