Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2000, Vol. 12 Issue (3) : 25-30     DOI: 10.6046/gtzyyg.2000.03.04
Technology Application |
EVALUATION TECHNIQUE OF INTEGRATING GEOCHEMICAL PROSPECTING WITH REMOTE SENSING IN PETROLEUM EXPLORATION
YAO Jun-mei1, XIA Xiang-hua1, ZHANG You-yan2
1. Petroleum Geochemistry Center, CNSPC, Hefei, 230022, China;
2. Research Institute of Petroleum Exploration and Development, CNPC, Beijing 100083, China
Download: PDF(386 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Oil/gas geochemical prospecting is closely related to oil/gas remote sensing in exploration prinsiple, although they are two different techniques. Both of them are based upon the theory of hydrocarbon microseeping and supplemented to each other in oil/gas exploration. Surface geochemical anomalies coincide with remote sensing image anomalies in spatial distribution. The integration of the two makes evaluation of oil/gas indicating significance of anomalies more reliable and can raise oil/gas prospecting efficiency.

Keywords Hydrocarbon micro-seepage      Remote sensing      Detection      Progress     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
SHEN Jin-Li
DING Shu-Bai
QI Xiao-Ping
XiNG Hua-Wen
ZHANG De-En
XIE Qiang-Feng
HE Zhen
NING Yong
Cite this article:   
SHEN Jin-Li,DING Shu-Bai,QI Xiao-Ping, et al. EVALUATION TECHNIQUE OF INTEGRATING GEOCHEMICAL PROSPECTING WITH REMOTE SENSING IN PETROLEUM EXPLORATION[J]. REMOTE SENSING FOR LAND & RESOURCES, 2000, 12(3): 25-30.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2000.03.04     OR     https://www.gtzyyg.com/EN/Y2000/V12/I3/25


[1] 姜洪训.综合地质物探化探多参数直接探测油气理论方法与效果【M】.西安:陕西出版社,1995.

[2] 朱振海.油气遥感勘探评价研究【M】.北京:中国科学技术出版社,1991.

[3] 郭德方,叶和飞.油气资源遥感【M】.杭州:浙江大学出版社,1995.

[4] 中科院航空遥感中心地理研究所.石油遥感【M】.北京:能源出版社,1989.

[5] 代联善,程同锦.第四届全国油气化探学术会议论文集
[C].武汉:中国地质大学出版社,1997.

[6] Steven A Tedesco. Surface Geochemisty In Petroleum Exploration【M】. International Thompson Publishing Inc.,1995.

[1] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2] 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.
[3] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[5] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[6] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[7] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[8] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[9] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[10] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[11] PAN Jianping, XU Yongjie, LI Mingming, HU Yong, WANG Chunxiao. Research and development of automatic detection technologies for changes in vegetation regions based on correlation coefficients and feature analysis[J]. Remote Sensing for Natural Resources, 2022, 34(1): 67-75.
[12] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[13] YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[14] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[15] 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.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech