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Distributed wireless sensing for fugitive methane leak detection

Journal Article · · IEEE Proceedings for IEEE Big Data 2017
 [1];  [2];  [2];  [2];  [2];  [2];  [2]
  1. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; IBM TJ Watson Research Center
  2. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
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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.
Research Organization:
IBM, Yorktown Heights, NY (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
Contributing Organization:
METEC, Colorado State University
Grant/Contract Number:
AR0000540
OSTI ID:
1409489
Journal Information:
IEEE Proceedings for IEEE Big Data 2017, Journal Name: IEEE Proceedings for IEEE Big Data 2017
Country of Publication:
United States
Language:
English

Figures / Tables (9)

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Related Subjects

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