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Oil And Gas Platforms Detection And Oil And Gas Resources Security Situation Evaluate Based On Multi-Source Data In The South China Sea

Posted on:2017-05-16Degree:MasterType:Thesis
Country:ChinaCandidate:Q LiFull Text:PDF
GTID:2311330488987699Subject:Cartography and Geographic Information System
Abstract/Summary:PDF Full Text Request
As one of the main equipments of offshore oil and gas exploration and development,the quantity and spatial distribution of Oil and gas platform reflect the development status of oil and gas resources in a region.For the limitation of data source in the previous researches,VIIRS night light data are selected to realize the extraction of oil and gas platform by a critical value method of convolution operation.In order to compare extraction effect between light data and radar data,this paper used the current mainstream dual-parameter CFAR maritime targets extraction algorithm to realize the extraction of oil and gas platforms data from the RadarSat-2 as an example.Finally,artificial interpretation on GF-1 of the study area is done which not only validates the accuracy of extraction by light data and radar data but also explores the conditions of the extraction of oil and gas platforms based on optical image.The evaluation of oil and gas resources Security situation is of great significance on the exploration,development and management of the oil and gas resources in the South China Sea.Currently,most of the researches regarded oil and gas basin as the evaluation unit which lack of oil and gas resources development data in the South China Sea.This kind of evaluation often lack of rigor.On the basis of the platform of oil gas extraction technology research,we obtain the data of development situation of oil and gas resources in the South China Sea proposing a SAVEE spatial difference model based on Grid according to the results of previous studies which realize evaluation of oil and gas resources security situation in the South China Sea.This paper provides new ideas and methods for the research of oil and gas resources in the South China Sea both in technology and results providing decisions to maintain the security of oil and gas resources in China.The main conclusions of this paper include the following points:(1)The accuracy of extraction by radar data is higher but the method of extraction by light data is better.As precision is concerned,the accuracy was 85.4% in VIIRS and 95% in RadarSat-2,whose accuracy is improved by10% higher than VIIRS.As missing rate is concerned,the leakage rate of RadarSat-2 data is 5% while VIIRS is 14.6%.So the accuracy of extraction by radar data is higher.As the extraction method is concerned,geometric correction,filtering and other processing are need in the extraction by RadarSat-2.In the process of the platform of oil and gas extraction based on VIIRS data,there are two threshold selection,and data RadarSat-2 has four times.Therefore,using radar data extraction of oil and gas platforms joins more subjective consciousness.(2)The development value of the oil and gas resources is "strong outside and weak inner ",and the accessibility is “weak south and strong north”Affected by the oil and gas resources reserves,depth of water and oil and number of gas platform,the value of exploitation of oil and gas resources in the South China Sea showed a trend of “strong outside and weak inner ".Development value of Western and central Pearl River Mouth basin,the north of Beibu Gulf,the Mekong basin,central Zengmu basin,and the inside of Brunei Sabah basin is weak.Affected by the location of the South China Sea,the overall accessibility of our nation in oil and gas resources in the South China Sea showed a trend of " weak south and strong north " while a trend of " weak external strength internal " in the western and southern.Compared regularity of the development value and accessibility,the development situation of oil and gas resources is more dispersed and the development varies obviously.
Keywords/Search Tags:oil and gas platforms, neighborhood analysis, security situation, SAVEE spatial difference model based on Grid
PDF Full Text Request
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