TY - GEN
T1 - Distributed wireless sensing for fugitive methane leak detection
AU - Klein, Levente J.
AU - Van Kessel, Theodore
AU - Nair, Dhruv
AU - Muralidhar, Ramachandran
AU - Hinds, Nigel
AU - Hamann, Hendrik
AU - Sosa, Norma
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - cloud analytics
KW - computation at edge
KW - data fusion
KW - wireless sensor network
UR - https://www.scopus.com/pages/publications/85047773228
U2 - 10.1109/BigData.2017.8258502
DO - 10.1109/BigData.2017.8258502
M3 - Conference contribution
AN - SCOPUS:85047773228
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 4583
EP - 4591
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
Y2 - 11 December 2017 through 14 December 2017
ER -