富油煤作为一种煤基油气资源，广泛分布于我国西北部，对确保国内油气供给安全、提高煤炭综合利用效能具有巨大潜力。由于早期的勘探资料中富油煤的唯一指标焦油产率数据极为有限，无法满足目前对富油煤精细评价的需求。地球物理参数在评价煤的物理化学特性方面展现出优势，但对煤焦油产率的响应研究仍显不足，且响应机理尚不明确。本文以鄂尔多斯盆地延安组、太原组以及三塘湖盆地八道湾组煤为主要研究对象，基于室内实验开展煤焦油产率的地球物理性质响应研究，并结合红外光谱、X-射线衍射等方法揭示其内在响应机理。

研究发现：（1）具有高焦油产率的富油煤表现为低密度、高电阻率、低纵横波速度以及低自然伽马的特点；富油煤的地球物理参数分布区间为：真密度小于1.40 g/cm³，电阻率值大于14000 Ω/m，纵波速度小于1900 m/s，自然伽马值小于40 API。（2）基于体积模型法和多元线性回归法分别对焦油产率进行预测，其中多元线性回归法的地球物理四参数（DEN、RT、V_p、GR）预测模型效果最好，但煤体的各向异性对上述模型预测焦油产率具有影响。（3）孔隙水对煤焦油产率的地球物理参数响应敏感性具有显著干扰，根据密度测井原理计算出煤骨架密度进而建立与焦油产率关系可以提高判识精度。纵横波速、电阻率虽在一定程度上仍能对饱和水状态煤焦油产率有所响应，但预测精度较低、适用性差。核磁共振T₁-T₂谱能够排除孔隙水干扰，对不同焦油产率煤具有良好的信号强度响应差异。（4）上述富油煤的地球物理性质是有机质与无机矿物的共同表现，有机组分是影响煤焦油产率的主要控制因素，灰分对于焦油产率的负向影响较小，真密度与之相反；含钙镁的矿物对真密度的正向影响最为显著，也对焦油产率产生了最大的负向影响；脂肪族官能团（特别是桥键）越多，会导致芳香层间距变宽，进而使得大分子结构的层排列较为疏松，煤的含油性越高，干燥无灰基真密度越低。（5）采用多元线性回归法和人工神经网络法对煤焦油产率进行预测，最佳拟合系数分别可达0.884和0.8986。

As a kind of coal-based oil and gas resources, tar-rich coal is widely distributed in the northwestern part of China, which has great potential to ensure the security of domestic oil and gas supply and improve the comprehensive utilization efficiency of coal. Since the tar yield data, the only indicator of tar-rich coal in the early exploration data, are extremely limited, they cannot meet the current demand for fine evaluation of tar-rich coal. Geophysical parameters show advantages in evaluating the physicochemical properties of coal, but the response study of coal tar yield is still insufficient, and the response mechanism is not clear. In this paper, we take the coals of Yan'an Formation and Taiyuan Formation in the Ordos Basin and Badawan Formation in the Santanghu Basin as the main research objects, and carry out the geophysical property response study of coal tar yield based on indoor experiments, and reveal the intrinsic response mechanism by combining infrared spectroscopy, X-ray diffraction and other methods.

It is found that: (1) the tar-rich coal with high tar yield is characterized by low density, high resistivity, low longitudinal and transverse wave velocity, and low natural gamma; the geophysical parameters of the tar-rich coal are distributed as follows: the true density is less than 1.40 g/cm³, the resistivity value is more than 14,000 Ω/m, the longitudinal wave velocity is less than 1,900 m/s, and the natural gamma value is less than 40 API. (2) The prediction of tar yield was performed by the volumetric modeling method and the multiple linear regression method were used to predict the tar yield respectively, in which the geophysical four-parameter (DEN, RT, V_p, GR) prediction model of the multiple linear regression method was the most effective, but the anisotropy of the coal body had an effect on the prediction of tar yield by the above model.(3) Pore water significantly interferes with the sensitivity of geophysical parameter response to coal tar yield, and the relationship between coal skeleton density and tar yield can be improved by calculating coal skeleton density according to the principle of density logging. Although the longitudinal and transverse wave velocity and resistivity can still respond to the saturated water state coal tar yield to a certain extent, the prediction accuracy is low and the applicability is poor. The NMR T₁-T₂ spectra can exclude the interference of pore water, and have good signal intensity response differences for coals with different tar yield. (4) The geophysical properties of the above tar-rich coals are a combination of organic matter and inorganic minerals; the organic fraction is the main controlling factor affecting the coal tar yield; the ash fraction has a smaller negative effect on the tar yield, and the true density is opposite to it; minerals containing calcium and magnesium have the most significant positive effect on the true density, and also have the largest negative effect on the tar yield; the more aliphatic functional groups (especially the bridge bonds) will lead to the the wider spacing of aromatic layers, which in turn makes the layer arrangement of macromolecular structure more loose; the higher the oil content of coal, the lower the true density of dry ash-free base. (5) The multiple linear regression method and artificial neural network method were used to predict the coal tar yield, and the best fitting coefficients were up to 0.884 and 0.8986, respectively.

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