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Sang Shu-ping Coal Mine And The Mined-out Area Of CBM Resources/Reserves Calculation

Posted on:2017-02-01Degree:MasterType:Thesis
Country:ChinaCandidate:P LiFull Text:PDF
GTID:2311330509463679Subject:Mineral prospecting and exploration
Abstract/Summary:PDF Full Text Request
Gas in Hancheng has seriously affected the coal mine production safety, has been China's “color change” talk about gas mine, gas control is always a difficult problem to the industry. Based of the coal geological exploration conditions of Sang Shu-Ping mining area, Study the basic of coal mine gas occurrence's characteristics, analyzes the gas content associated with various geological factors and the size of gas occurrence, and between the various geological factors, using multiple regression analysis and BP neural network prediction of coal mine the gas content, and segment method to estimate CBM resource in the mine mining area, using the method of segment and resource estimates to estimate the mined-out area's resources respectively.By analyzing the research think Sang shu-ping coal mine main minable seam 3# and 11# coal seam influenced by the NNW to the parallel arrangement of the of the rolling anticline, in the change of coal thickness of parts and flexural damage to coal seam extrusion forming of tectonic soft coal area for gas enrichment advantageous position. Seam gas mining area by weathering significantly, showing zonality obviously. 3# coal seam gas content influenced by the buried depth of coal seam and the volatile content in cleaned coal, 11# coal seam gas content influenced by lithology and coal seam thickness, coal seam thickness and seam depth. According to the various factors affecting the coal seam gas content, using different methods to predict coalbed gas and think BP neural network analysis is more suitable for Sang shu-ping coal mine gas content prediction. Combining the known and predicted's coal seam gas content, estimated the mining area of CBM resources, and use the segment method and the resources composition method to estimate goaf's coal bed methane resources.
Keywords/Search Tags:Geological structure, Coal seam gas content, BP neural network, Resource estimates, Goaf
PDF Full Text Request
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