The Progress of Remote Sensing Technology in the Detection of Hydrocarbon Micro-seepage

doi:10.6046/gtzyyg.2010.03.02

Abstract
References
Related Articles
Metrics

Download: PDF(805 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Hydrocarbon micro-seepage is a common phenomenon over oil and gas reservoirs. Remote sensing data are important sources in extracting seepage information for the exploration of oil and gas resources. This paper presents the theory of hydrocarbon micro-seepage and describes different manifestations of hydrocarbon micro-seepage at continental surface, in offshore area and on sea surface. An overall discussion is given in this paper concerning the research progress and the development trend of the remote sensing technology in China.

Keywords Principal component analysis(PCA) Information fusion TM SAR

TP 79

Issue Date: 20 September 2010

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	YANG Cun-jian
	XU Jun
	ZHANG Zeng-xiang

Cite this article:

YANG Cun-jian,XU Jun,ZHANG Zeng-xiang. The Progress of Remote Sensing Technology in the Detection of Hydrocarbon Micro-seepage[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 7-11.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.03.02 OR https://www.gtzyyg.com/EN/Y2010/V22/I3/7

［1］Everett J R,Jengo C J,Staskowski R J.Remote Sensing and GIS Enable Future Exploration Success［J］.World Oil,2002,223(11):59-65.

［2］谢青云，丁树柏.油气资源遥感技术若干问题的探讨［J］.国土资源遥感，1994（2）：56-62.

［3］陶国强，冯建辉，熊介凡.遥感油气勘探对伊利盆地的评价［J］.现代地质，1997，11（4）：529-535.

［4］朱振海.油气藏烃类微渗漏理论及遥感油气资源探测的机理研究［J］.遥感技术与应用，1994，9（1）：1-10.

［5］郭德方.遥感技术直接找油［J］.石油学报，1995，4（16）：9-16.

［6］赵欣梅．基于烃类微渗漏理论的高光谱遥感油气异常探测方法研究［D］.北京：中国地质大学（北京），2007.

［7］管仲，田庆久.油气光学遥感研究综述［J］.天然气工业，2006，26(11)：44-48.

［8］Roger M M. Detecting Hydrocarbon Microseepage Using Hydrocarbon Absorption Bands of Reflectance ［J］.Oil and Gas Journal, 2001, 99(22)：40-45.

［9］李岩.烃类微渗漏的地物波谱探测远离与方法研究［J］.干旱区地理,1995,18(1)：76-82.

［10］Crawford M F. Preliminary Evaluation of Remote Sensing Data for Detection of Vegetation Stress Related to Hydrocarbon Microseepage［C］//Proceedings of the Fifth Thematic Conference on Remote Sensing for Exploration Geology, American Nevada：Environmental Research Institute of Michigan, 1986：161-177.

［11］Yang H,Zhang J,Van Der Meer F,et al.Geochemistry and Field Spectrometry for Detecting Hydrocarbon Microseepage［J］.Terra Nova,1998,10:231-235.

［12］王津义.地植物异常判别油气微渗漏的实例［J］. 石油与天然气地质,1998,19(1)：80-82.

［13］王云鹏，耿安松，刘德汉.鄂尔多斯盆地地表烃类的遥感探测研究［J］. 天然气工业,1999,19(6)：17-20.

［14］Roberts H H, Aharon P, Carney R，et al. Sea Floor Responses to Hydrocarbon Seeps, Louisiana Continental Slope［J］. Geo-Marine Letters,1990,10(4)：232-243.

［15］Nicolas Pinet, Mathieu Duchesne, Denis Lavoie，et al. Surface and Subsurface Signatures of Gas Seepage in the St. Lawrence Estuary (Canada)：Significance to Hydrocarbon Exploration［J］. Marine and Petroleum Geology ，2008，25：271-288.

［16］Jean Whelan, Lorraine Eglinton, Lawrence Cathles III，et al. Surface and Subsurface Manifestations of Gas Movement Through a N-S Transect of the Gulf of Mexico［J］.Marine and Petroleum Geology,2005,22：479-497.

［17］Barthold M Schroot, Gerard T Klaver, Ruud T E. Schuttenhelm,Surface and Subsurface Expressions of Gas Seepage to the Seabed—Examples from the Southern North Sea［J］. Marine and Petroleum Geology,2005,22：499-515.

［18］Kruglyakova R P, Byakov Y A, Kruglyakova M V, et al. Natural Oil and Gas Seeps on the Black Sea Floor［J］. Geo-Mar Lett，2004,24：150-162.

［19］Halliday E J, Barrie J V, Chapman N R，et al. Structurally Controlled Hydrocarbon Seeps on a Glaciated Continental Margin, Hecate Strait, Offshore British Columbia［J］.Marine Geology,2008,252：193-206.

［20］Sophie Magdalena DE Beukelaer. Remote Sensing Analysis of Natural Oil and Gas Seeps on the Continental Slope of the Northern Gulf of Mexico ［D］. Texas：A&M University, 2003.

［21］Struckmeyer H I M,Williams A K,Cowley R, et al. Evaluation of Hydrocarbon Seepage in the Great Australian Bight［J］. Appea Journal,2002：371-386.

[1]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[2]	YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.
[3]	ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[4]	LI Mengmeng, FAN Xueting, CHEN Chao, LI Qiannan, YANG Jin. Monitoring and interpretation of land subsidence in mining areas in Xuzhou City during 2016—2018[J]. Remote Sensing for Natural Resources, 2021, 33(4): 43-54.
[5]	SHI Min, GONG Huili, CHEN Beibei, GAO Mingliang, ZHANG Shunkang. Monitoring of land subsidence in Beijing-Tianjin-Hebei plain during 2016—2018 based on InSAR and Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 55-63.
[6]	LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[7]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[8]	YU Bing, TAN Qingxue, LIU Guoxiang, LIU Fuzhen, ZHOU Zhiwei, HE Zhiyong. Land subsidence monitoring based on differential interferometry using time series of high-resolution TerraSAR-X images and monitoring precision verification[J]. Remote Sensing for Natural Resources, 2021, 33(4): 26-33.
[9]	SHA Yonglian, WANG Xiaowen, LIU Guoxiang, ZHANG Rui, ZHANG Bo. SBAS-InSAR-based monitoring and inversion of surface subsidence of the Shadunzi Coal Mine in Hami City, Xinjiang[J]. Remote Sensing for Natural Resources, 2021, 33(3): 194-201.
[10]	ZHANG Teng, XIE Shuai, HUANG Bo, FAN Jinghui, CHEN Jianping, TONG Liqiang. Detection of active landslides in central Maoxian County using Sentinel-1 and ALOS-2 data[J]. Remote Sensing for Land & Resources, 2021, 33(2): 213-219.
[11]	HE Haiying, CHEN Caifen, CHEN Fulong, TANG Panpan. Deformation monitoring along the landscape corridor of Zhangjiakou Ming Great Wall using Sentinel-1 SBAS-InSAR approach[J]. Remote Sensing for Land & Resources, 2021, 33(1): 205-213.
[12]	TIAN Lei, FU Wenxue, SUN Yanwu, JING Linhai, QIU Yubao, LI Xinwu. Research on spatial change of the boreal forest cover in Siberia over the past 30 years based on TM images[J]. Remote Sensing for Land & Resources, 2021, 33(1): 214-220.
[13]	DONG Tiancheng, YANG Xiao, LI Hui, ZHANG Zhi, QI Rui. The extraction of plateau lakes from SAR images based on Faster R-CNN and MorphACWE model[J]. Remote Sensing for Land & Resources, 2021, 33(1): 129-137.
[14]	JIANG Shan, WANG Chun, SONG Hongli, LIU Yufeng. A study of crop planting type recognition based on SAR and optical remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 105-110.
[15]	SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou[J]. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.

Viewed

Full text

Abstract

Cited

Shared

Discussed