TY - GEN
T1 - Passive super-low frequency remote sensing technique for monitoring coal-bed methane reservoirs
AU - Wang, Nan
AU - Qin, Qi Ming
AU - Chen, Li
AU - Bai, Yan Bing
AU - Zhao, Shan Shan
AU - Zhang, Cheng Ye
AU - Ren, Hua Zhong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/4
Y1 - 2014/11/4
N2 - Coal-bed methane (CBM), as an increasingly promising resource for the energy supply, deserves further exploration and accurate reservoir evaluation. It is also required to dynamically monitor the reservoirs (>200 m). Remote sensing methods in regular wavebands may fail in the depth sounding, with only imaging geo-objects shallower than 100 m. In contrast, the Super-Low Frequency (SLF) remote sensing technique has outstanding traits over others, including lower attenuation, all-weather and deeper penetration. In this paper, we have developed a non-imaging remote sensor to acquire electromagnetic signals in the Super-Low Frequency bands (i.e. SLF signals), which also enables us to fast and efficiently pre-process signals in a real-time display. In order to accurately identify producing CBM reservoirs, we mainly extract electromagnetic radiation (EMR) anomalies from processed SLF signals, and then dynamic analysis can be achieved. This technique has been validated by field experiments in Qin shui Basin, China.
AB - Coal-bed methane (CBM), as an increasingly promising resource for the energy supply, deserves further exploration and accurate reservoir evaluation. It is also required to dynamically monitor the reservoirs (>200 m). Remote sensing methods in regular wavebands may fail in the depth sounding, with only imaging geo-objects shallower than 100 m. In contrast, the Super-Low Frequency (SLF) remote sensing technique has outstanding traits over others, including lower attenuation, all-weather and deeper penetration. In this paper, we have developed a non-imaging remote sensor to acquire electromagnetic signals in the Super-Low Frequency bands (i.e. SLF signals), which also enables us to fast and efficiently pre-process signals in a real-time display. In order to accurately identify producing CBM reservoirs, we mainly extract electromagnetic radiation (EMR) anomalies from processed SLF signals, and then dynamic analysis can be achieved. This technique has been validated by field experiments in Qin shui Basin, China.
KW - Coal-bed methane
KW - Super-Low Frequency
KW - dynamic monitoring
KW - electromagnetic radiation
KW - reservoir identification
UR - http://www.scopus.com/inward/record.url?scp=84911364806&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2014.6946562
DO - 10.1109/IGARSS.2014.6946562
M3 - Conference Proceeding
AN - SCOPUS:84911364806
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 867
EP - 870
BT - International Geoscience and Remote Sensing Symposium (IGARSS)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Y2 - 13 July 2014 through 18 July 2014
ER -