首页|Gas content evaluation in deep coal seam with an improved method and its geological controls
Gas content evaluation in deep coal seam with an improved method and its geological controls
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
An improved evaluation method for estimating gas content during the inversion process of deep-burial coal was established based on the on-site natural desorption curves.The accuracy of the US Bureau of Mines(USBM),Polynomial fitting,Amoco,and the improved evaluation methods in the predicting of lost gas volume in deep seams in the Mabidong Block of the Qinshui Basin were then compared.Furthermore,the calculation errors of these different methods in simulating lost gas content based on coring time were compared.A newly established nonlinear equation was developed to estimate the minimum error value,by controlling the lost time within 16 min,the related errors can be reduced.The improved evaluation was shown to accurately and rapidly predict the gas content in deep seams.The results show that the deep coal bed methane accumulation is influenced by various factors,including geological structure,hydrodynamic conditions,roof lithology,and coalification.Reverse faults and weak groundwater runoff can hinder the escape of methane,and these factors should be considered in the future exploration and development of coalbed methane.
deep coalbed methaneMabidong Blocklost gasgeological controls
Haiqi LI、Shida CHEN、Dazhen TANG、Shuling TANG、Jiaosheng YANG
展开 >
School of Energy Resource,China University of Geosciences(Beijing),Beijing 100083,China
Coal Reservoir Laboratory of National Engineering Research Center of Coalbed Methane Development and Utilization,Beijing 100083,China
Beijing Key Laboratory of Unconventional Natural Gas Geological Evaluation and Development Engineering,Beijing 100083,China
China Petroleum Exploration and Development Research Institute,Langfang 065000,China
展开 >
National Natural Science Foundation of ChinaPetroChina Company Limited"14th Five Year Plan"Science and Technology Major Project
NETL
NSTL
万方数据
