APPLICATION OF REMOTE SENSING INFORMATION IN OF OIL AND GAS ACCUMULATION PROGNOSIS

doi:10.6046/gtzyyg.1992.02.05

Download: PDF(413 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Remote sensing information is economical in petroleum and natural gas survey. This paper introduces two remote sousing methods to look for hydrocarbon reservoir. 1. The hydrocarbon micro-seepage information was extracted from TMdata with the spectra and image processing to detect petroleum reservoir. The application results in North China were presented in this paper. 2. Application of residual index of linear bodies to detect the local structure of petroleum reservoir. This method is special to study the buried local structures which are deep and hard to find because of weak response. The residual index analysis method of geographic, geomorphic and geologic linear bodies was used to detect the local structures of petroleum reservoir. This paper introduced the application results in carbonatite area.

Keywords Mixed-pixel Weight Remote sensing image classification Abundance

Issue Date: 02 August 2011

	Service

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

	HE Hai-Qing
	LI Fa-Bin
	LI He-Chao
	WANG Yong-
	YANG Yun-Jian
	MI Xiao-Li
	SONG Xi-Lin
	HE Zhan-Xiang
	ZHANG Zhen-Heng

Cite this article:

HE Hai-Qing,LI Fa-Bin,LI He-Chao, et al. APPLICATION OF REMOTE SENSING INFORMATION IN OF OIL AND GAS ACCUMULATION PROGNOSIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 1992, 4(2): 24-30.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1992.02.05 OR https://www.gtzyyg.com/EN/Y1992/V4/I2/24

[1] 欧庆贤,中国南方海相碳酸盐岩区油气普查勘探方法技术攻关的新进展,中国南方油气勘查新领域探索论文集,第1辑,地质出版社,1988, 11.

[2] 方起东,遥感地质基础,地质出版社,1979年.

[3] ДАКУкущкин,Аналимегатрещиноватости,выцеияемойирицешифрироваиицистанционныхматериацов,цияпоисковлокалныхструктурвнефтегаэоносныхБассейнах,Иэв.в.у.э.Геол,раэ,1982 No.3.pp107-114.

[4] ДАКукушкиндр.Линеаментныйаналиэаккумулятивныхравнин,《Сов.Гео.》1985,2,pp.100-105.

[1]	JIANG Na, CHEN Chao, HAN Haifeng. An optimization method of DEM resolution for land type statistical model of coastal zones[J]. Remote Sensing for Natural Resources, 2022, 34(1): 34-42.
[2]	DU Yi, WANG Dayang, WANG Dagang. Spatial downscaling of GPM precipitation products: A case study of Guizhou Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 111-120.
[3]	XIAO Dongsheng, LIAN Hong. Population spatialization based on geographically weighted regression model considering spatial stability of parameters[J]. Remote Sensing for Natural Resources, 2021, 33(3): 164-172.
[4]	XU Rui, YU Xiaoyu, ZHANG Chi, YANG Jin, HUANG Yu, PAN Jun. Building change detection method combining Unet and IR-MAD[J]. Remote Sensing for Land & Resources, 2020, 32(4): 90-96.
[5]	CAI Zhiling, WENG Qian, YE Shaozhen, JIAN Cairen. Remote sensing image scene classification based on Inception-V3[J]. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
[6]	Yuling ZHAO. Study and application of analytic hierarchy process of mine geological environment: A case study in Hainan Island[J]. Remote Sensing for Land & Resources, 2020, 32(1): 148-153.
[7]	Dechao ZHAI, Yanan FAN, Yanan ZHOU. Multi-scale segmentation of satellite imagery by edge-incorporated weighted aggregation[J]. Remote Sensing for Land & Resources, 2019, 31(3): 36-42.
[8]	Ruhan A, Fang HE, Biaobiao WANG. Hyperspectral images classification via weighted spatial-spectral dimensionality reduction principle component analysis[J]. Remote Sensing for Land & Resources, 2019, 31(2): 17-23.
[9]	Yitian WU, Fu CHEN, Yong MA, Jianbo LIU, Xinpeng LI. Research on automatic extraction method for coastal aquaculture area using Landsat8 data[J]. Remote Sensing for Land & Resources, 2018, 30(3): 96-105.
[10]	Lingyu YIN, Xianlin QIN, Guifen SUN, Shuchao LIU, Xiaofeng ZU, Xiaozhong CHEN. The method for detecting forest cover change in GF-1images by using KPCA[J]. Remote Sensing for Land & Resources, 2018, 30(1): 95-101.
[11]	ZHANG Qi, LIU Fujiang, LI Chan, QIAO Le, GUO Zhenhui, CHAI Chunpeng. Fully constrained linear-unmixing for inversion of lunar mineral abundance in Sinus Iridum[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 7-14.
[12]	ZHANG Yuan, ZHANG Jielin, ZHAO Xuesheng, YUAN Bo. Extraction of mineral alteration information from core hyperspectral images based on weight of absorption peak[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 154-159.
[13]	LI Sha, NI Weiping, YAN Weidong, WU Junzheng, ZHANG Han. Change detection of multi-spectral images based on iterative estimation with weight selection and unsupervised classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 34-40.
[14]	LIANG Lingfei, ZHANG Chong, PING Ziliang. Remote sensing image fusion based on weighted filter empirical mode decomposition[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 61-66.
[15]	LIU Shanlei, SHI Shanqiu, ZHANG Lijing, ZHAO Yindi, WANG Guanghui. Selection of GCP in geometric correction of remote sensing images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 86-91.