煤的绿色清洁高效利用是煤炭工业可持续发展的重要途径。陕西省煤炭资源丰富,主要含煤区分布于陕西北部的五大煤田,富油煤资源更是位居全国首位。通过收集以往地质勘探资料和针对性采集陕北榆神矿区某煤矿煤心进行化验测试分析,探讨陕西省富油煤分布特征及影响煤焦油产率的地质因素。结果显示,陕西省富油煤以陕北侏罗系煤田为最优,焦油产率分布于7.6%~15.3%;物质组成、生油潜力、热演化程度及成煤环境控制着煤的焦油产率的大小和分布。基质镜质体、挥发分产率、H 元素含量及H/C 原子比等与煤的焦油产率呈正相关关系,相关系数较高,岩石热解参数中烃指数与焦油产率的相关性高,可作为快速判别煤焦油产率的指标;煤焦油产率随着热演化程度的增加呈现先增加后减少的趋势,且在Ro 约0.9% 时,达到最大值;沉积环境水体微流动、强还原、浅覆水−微流动的泥炭沼泽相的煤焦油产率值越高。研究结果为富油煤的地质预测提供借鉴。

The green, clean and efficient utilization of coal is an important way for the sustainable development of coal industry. Shaanxiprovince is rich in coal resources, which are mainly distributed in the five coal fields in the north of Shaanxi province, and the oil-rich coalresources rank first in China. In this paper, the distribution characteristics of oil-rich coal in Shaanxi Province and the geological factors affectingthe tar yield of coal are discussed by collecting the previous geological exploration data and the coal core of a coal mine in Yushenmining area of northern Shaanxi Province. The results show that the Jurassic coal field in northern Shaanxi is the best oil-rich coal inShaanxi Province, and the tar yield is distributed in the range of 7.6%-15.3%. Material composition, oil generation potential, thermal evolutiondegree and coal-forming environment control the size and distribution of tar yield in coal. The desmocollinite content, volatile mattercontent, H element content and H/C atomic ratio were positively correlated with the tar yield of coal, and the correlation coefficient washigh. There is a high correlation between the hydrocarbon index and tar yield in rock pyrolysis parameters, which can be used as an indexto quickly identify tar yield in coal. The tar yield of coal increases first and then decreases with the increase of thermal evolution degree,and reaches the maximum value when Ro is about 0.9%. The higher the coal tar yield value is in the peat swamp facies with micro-flow,strong reduction and shallow overlying water-micro-flow in sedimentary environment. The research results provide reference for geologicalprediction of oil-rich coal.