摘要
为了揭示煤相对煤储层含气量的控制作用,以常规测井曲线为基础,以沁水盆地中部沁源区块2号煤层为研究对象,通过测井曲线与煤的工业组分、煤的工业组分同煤岩成分、煤岩成分与宏观煤岩类型以及宏观煤岩类型与煤相的关系研究,结合主成分分析、多元回归分析、Logistic回归等方法,建立了煤相的测井解释模型。在单井和连井煤相特征分析的基础上,恢复了研究区2号煤层煤相空间展布特征,揭示了煤相与煤储层含气量的相关关系。结果表明:深覆水森林沼泽分布区域煤储层含气量最高,主要分布在研究区的东南部;干燥森林沼泽相分布区域煤储层含气量最低,主要分布在研究区的西北部;覆水和潮湿森林沼泽相分布区域煤储层含气量介于上述两者之间,主要分布在研究区的中部和东部。
In order to reveal the control role of the coal facies to the gas content of the coal reservoir,based on the conventional logging curve,taking No. 2 seam in Qinyuan Block at the central part of Qingshui Basin as the study object,with a study on the logging curve and the coal industrial components,the coal industrial components and lithotype,the lithotype and macro lithotypic type as well as the relationship between the macro lithotypic type and coal facies,the combination of the principal component analysis,multiple regression analysis,Logistic regression and other methods was applied to establish the logging interpretation model of the coal facies. Based on the coal facies features analysis on the single well and linked wells,the distribution features of the coal facies space in No. 2 seam of the study block were recovered and the relevant relationship between the coal facies and the coal reservoir gas content was revealed. The results showed that the gas content of the coal reservoir in the deep water covered forest-swamp distribution area was highest and the main distribution was located in the southeast of the study block. The gas content of the coal reservoir in the dry forest swamp facies distribution area was lowest and the main distribution was located in the northwest of the study block. The gas content of the coal reservoir in the water covered and wet forest-swamp distribution area was between above two gas contents and the main distribution was located in the central and east of the study block.
出处
《煤炭科学技术》
CAS
北大核心
2017年第4期117-122,共6页
Coal Science and Technology
基金
国家科技重大专项资助项目(2011ZX05060
2016ZX05066)
关键词
煤相
主成分分析
多元回归
LOGISTIC回归
测井解释
含气量
coal facies
principal components analysis
multiple regression
Logistic regression
logging interpretation
gas content