Hyperspectral Remote Sensing Technology in Oil and Gas Exploration
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摘要: 高光谱遥感探测技术已成为探测油气藏的前沿新技术之一.研究以油气微渗漏地表共生异常理论为基础, 采用基于小波主成份分析(principal component analysis, PCA)最大似然分类、端元提取分类、光谱库典型蚀变光谱分类和植被指数决策树分类方法, 对榆林典型稀疏植被地区的进行油气勘探, 提取了与烃异常相关的粘土、碳酸盐、植被异常等相关的专题信息产品, 得出综合异常区图.对照分析已知气井与油气异常区分布, 证明了油气微渗漏信息的提取与识别方法的有效性.Abstract: Hyperspectral remote sensing technology is new in oil and gas exploration. Based on the theory of abnormal surface symbiosis with oil and gas microseepage, four typical classifications, maximum likelihood classification in the wavelet-based principal component analysis, endmember extraction, typical alteration classification with spectral libraries and decision tree classification based on vegetation indices are chosen to carry a case study of Hyperion images in Yulin, to obtain the related thematic maps such as clay, carbonate, vegetation and determine six comprehensive anomalous areas. A comprehensive analysis of the distribution of existing gas well and oil-gas anomalous areas show that the information extraction method of oil and gas microseepage is valid.
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图 2 基于小波PCA分类填图
a.细分结果；b.精分结果；c.各类均值曲线；d.各类标准差曲线
Fig. 2. Mineral mapping by wavelet-based principal components analysis classifications
图 4 图像端元光谱(a)与USGS标准光谱(b)
Fig. 4. End members spectrum (a) and standard spectrum in USGS (b)
图 5 包络线去除后的图像端元光谱与USGS标准光谱
a.端元1与蒙脱石；b.端元2与伊利石；c.端元3与方解石
Fig. 5. End members spectrum and standard spectrum in USGS after the continuum-removal
图 6 基于端元提取矿物填图
a.蒙脱石；b.伊利石；c.方解石
Fig. 6. Mineral mapping by endmember extraction classifications
图 8 基于光谱库典型蚀变光谱分类填图
a.蒙脱石；b.伊利石；c.方解石
Fig. 8. Mineral mapping by typical alteration classification with spectral libraries
图 9 研究区植被分类
a.ML植被区域；b.NDVI植被区域；c.决策树植被异常区
Fig. 9. Alteration mineral vegetation classification
图 10 蚀变矿物异常与植被异常综合分析
a.选区1真彩色图像；b.选区1分类结果；c.选区1异常点分类；d.选区2真彩色图像；e.选区2分类结果；f.选区2异常点分类
Fig. 10. Distribution of vegetation abnormal area and exposed surface abnormal area
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[1] Cloutis, E.A., 1996. Hyperspectral Geological Remote Sensing: Evaluation of Analytical Techniques. International Journal of Remote Sensing, 17(12): 2215-2242. doi: 10.1080/01431169608948770 [2] Everett, J.R., Staskowski, R.J., Jengo, C., 2002. Remote Sensing and GIS Enable Future Exploration Success. World Oil, 223(11): 59-65. http://search.ebscohost.com/login.aspx?direct=trueu0026db=buhu0026AN=8653515u0026site=ehost-live [3] Gan, F.P., Wang, R.S., Ma, A.N., et al., 2003. Alteration Extracting Based on Spectral Match Filter(SMF). Journal of Image and Graphics, 8(2): 147-150(in Chinese with English abstract). doi: 10.11834/jig.20030261 [4] Goetz, A.F.H., 2009. Three Decades of Hyperspectral Remote Sensing of the Earth: A Personal View. Remote Sensing of Environment, 113(1): 5-16. doi: 10.1016/j.rse.2007.12.014 [5] Khan, S.D., Jacobson, S., 2008. Remote Sensing and Geochemistry for Detecting Hydrocarbon Microseepages. Geological Society of America Bulletin, 120(1-2): 96-105. doi: 10.1130/0016-7606(2008)120[96:rsagfd]2.0.co;2 [6] Li, Q.Q., Chen, X.M., Liu, X., et al., 2013. Quantitative Analysis of Content and Spectrum of Altered Mineral in the Oil and Gas Microseepage Area. Spectroscopy and Spectral Analysis, 33(12): 3318-3320 (in Chinese with English abstract). doi: 10.3964/j.issn.1000-0593(2013)12-3318-03 [7] Ni, G.Q., Shen, Y.T., Xu, D.Q., 2007. Wavelet-Based Principal Components Analysis Feature Extraction Method for Hyperion Images. Transactions of Beijing Institute of Technology, 27(7): 621-624 (in Chinese with English abstract). doi: 10.3969/j.issn.1001-0645.2007.07.014 [8] Noomen, M.F., Skidmore, A.K., van der Meer, F., 2003. Detecting the Influence of Gas Seepage on Vegetation, Using Hyperspectral Remote Sensing. Proceedings of 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 252-256. [9] Pan, C., Du, P.J., Luo, Y., et al., 2009. Decision Tree Classification of Remote Sensing Images Based on Vegetation Indices. Journal of Computer Applications, 29(3): 777-780 (in Chinese with English abstract). doi: 10.3724/SP.J.1087.2009.00777 [10] Schaepman, M.E., Ustin, S.L., Plaza, A.J., et al., 2009. Earth System Science Related Imaging Spectroscopy—An Assessment. Remote Sensing of Environment, (113): S123-S137. doi: 10.1016/j.rse.2009.03.001 [11] Tong, Q.X., Xue, Y.Q., Zhang, L.F., 2014. Progress in Hyperspectral Remote Sensing Science and Technology in China over the Past Three Decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 70-91. doi: 10.1109/jstars.2013.2267204 [12] van der Meer, F.D., Harald, M.A., Hecker, C.A., et al., 2012. Multi- and Hyperspectral Geologic Remote Sensing: A Review. International Journal of Applied Earth Observation and Geoinformation, 14(1): 112-128. doi: 10.1016/j.jag.2011.08.002 [13] Wang, X.L., Du, P.J., Tan, K., et al., 2010. An Automatic Endmember Extraction Algorithm from Hyperspectral Image. Remote Sensing Information, (4): 8-12(in Chinese with English abstract). doi: 10.3969/j.issn.1000-3177.2010.04.002 [14] Wang, Y., 2010. Hydrocarbon Microseepage Information Extracting through Remote Sensing Technology in Front Range of Longmenshan. Coal Geology of China, 22(10): 10-16 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGMT201010006.htm [15] Yang, Y.J., Zhao, Y.J., 2011. The Hyperspectral Research Status at Home and Abroad in the Oil Exploration. Science Technology and Engineering, 11(6): 1290-1299 (in Chinese with English abstract). http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-KXJS201106031.htm [16] 甘甫平, 王润生, 马蔼乃, 等, 2003. 基于光谱匹配滤波的蚀变信息提取. 中国图像图形学报, 8(2): 147-150. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200302004.htm [17] 李倩倩, 陈小梅, 刘幸, 等, 2013. 油气微渗漏区蚀变矿物含量与光谱之间的定量分析. 光谱学与光谱分析, 33(12): 3318-3320. doi: 10.3964/j.issn.1000-0593(2013)12-3318-03 [18] 倪国强, 沈渊婷, 徐大琦, 2007. 一种基于小波PCA的高光谱图像特征提取新方法. 北京理工大学学报, 27(7): 621-624. doi: 10.3969/j.issn.1001-0645.2007.07.014 [19] 潘琛, 杜培军, 罗艳, 等, 2009. 一种基于植被指数的遥感影像决策树分类方法. 计算机应用, 29(3): 777-780. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY200903044.htm [20] 王晓玲, 杜培军, 谭琨, 等, 2010. 一种高光谱遥感影像端元自动提取方法. 遥感信息, 4: 147-150. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX201004004.htm [21] 王永, 2010. 基于遥感技术的龙门山前山带烃类微渗漏信息提取. 中国煤炭地质, 22(10): 10-16. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMT201010006.htm [22] 杨燕杰, 赵英俊, 2011. 高光谱在油气勘探中的国内外研究现状. 科学技术与工程, 11(6): 1290-1299. doi: 10.3969/j.issn.1671-1815.2011.06.029 -
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