【作者】 李娟娟；

【导师】 崔若飞； 陈圣恩；

【作者基本信息】 中国矿业大学 ， 地质资源与地质工程， 2013， 博士

【摘要】 煤与瓦斯突出灾害已经成为导致中国煤矿特大恶性事故的“头号杀手”，“瓦斯不治，矿无宁日”。中国煤系地层构造十分复杂且地应力大，采掘时极易发生瓦斯突出现象。面对如此严峻的煤矿安全形势，深入研究矿井和瓦斯地质赋存情况，为矿井安全生产提供可靠的地质保障和科学依据刻不容缓。构造煤作为煤与瓦斯突出的高危煤体是发生突出的必要条件，预测煤层中构造煤发育程度是评价瓦斯突出危险可能性的重要依据之一。论文利用岩性地震反演方法(包括概率神经网络反演方法、弹性波阻抗反演方法和同步反演方法)，研究了阳煤集团新景煤矿佛洼区15#煤层构造煤发育情况，以煤层中构造煤分布的角度评价来完成煤层瓦斯突出危险性预测。第一，从构造煤岩石地球物理特征出发，分析了构造煤与原生煤在物理性质和化学性质方面的显著差异，总结归纳出几种常见岩石物理量的相互转化经验公式，为识别煤层中构造煤提供理论依据。第二，从地震属性技术和基于模型反演理论入手，分析了地震属性反演的各种方法，研究了基于概率神经网络的叠后地震反演方法。第三，从叠前地震反演的理论基础Zeoppritz方程出发，研究了基于Zeoppritz方程近似公式的弹性波阻抗反演理论，以及另一种叠前地震反演方法同步反演。第四，完成阳煤集团新景煤矿佛洼区三维地震资料处理和地震岩性反演计算。利用概率神经网络反演方法获得孔隙度数据体；利用声波阻抗反演方法获得声波阻抗数据体；利用弹性波阻抗反演方法获得弹性波阻抗数据体；利用同步反演方法获得λ*ρ和μ*ρ岩性指示因子数据体。第五，利用各种岩性数据体对15#煤层中构造煤分布进行预测。将孔隙度数据作为定性解释依据；弹性波阻抗(≤0.17)和声波阻抗(≤0.2)交会区域，λ*ρ值(≤15)和值(≤10)交会区域作为定量解释依据联合解释。最后，提出了综合评价因子X的概念，实质是各类岩性数据体给定权重的线性组合。通过综合评价因子将15#煤层划分为5个岩性区域，即无构造煤分布区域、几乎无构造煤分布区域、构造煤分布范围较小区域、构造煤分布范围较大区域和构造煤分布区域。利用综合岩性指标预测了15#煤层瓦斯突出危险的可能性。更多还原

【Abstract】 Coal and gas outburst accidents considered as "the first killer" of the coal mine,has resulted in huge loss in China. With complex structure and largeground stress,coaland gas outburst easily occur so it is urgent to deeply study the geology of the mineand the gas occurrence to provide the reliable geological protection and scientificbasis. Deformed coal is necessary condition for coal and gas outburst and as ahigh-risk coal it is the significant basis to evaluate the gas accidents risk.In this paper, the development of deformed coal in15#coal layer has studied bythe lithologic seismic inversion methods which include PNN, elastic and simultaneousinversion methods, then gas outburst risk can be assessed and predicted from the pointof view the deformed coal’s distribution.Firstly, the significant differences of the physical and chemical propertiesbetween the deformedcoal and bituminous coal are described as well as petrophysicalparameters characteristic of the deformed coal. Petrophysical conversion formulasabout common physical quantities are summarized and ordered and it also providesthe basic theoretical basis to evaluate deformed coal.Secondly, the probabilistic neural network inversion technique is chosen to applyto complete the porosity inversion which avoids the inherent limitations of theconventional inversion. The theory of attributes inversion methods is analyzed fromthe introduction of attribute technology and model-based inversion theory and finallythe chapter focuses on the theory of probabilistic neural network inversion.Thirdly, the theory foundation of pre-stack inversion is Zeoppritz equation andapproximate formula based on the equations. Based the approximate formulas, elasticimpedance inversion is elaboratedas well as simultaneous inversion which is the otherindependent pre-stack inversion method.Fourthly, the risk evaluation of coal and gas outburst hazard has completed bythe lithologic seismic inversion method in Yang Quan coal mine. The pre-stack anglegathers and common post stack gathers are prepared after the fidelity processing forinversion and PNN inversion，acoustic inversion，elastic inversion and simultaneousinversion methods are applied to obtain porosity,AI, EI, ZP, Zs, and μ*ρ volumesin order to interpret the deformed coal distribution.Fifthly, the porosity volume is used as the qualitative explanation, and the crossplot between EI values(no more than0.17) and AI values(no more than0.2) as well as the cross plot between λ*ρvalues(no more than15) and μ*ρvalues(no more than10) is considered as quantitative results to evaluate the distribution of the deformedcoal.Finally, the author has proposed the new concept—comprehensive evaluationfactor X, which is the linear combination of all kinds of lithologic data volumes, todivide the15#coal into five types: no deformed coal area, almost no deformed coalarea, less likelydeformed coalarea, likelydeformed coal area, high-risk deformed coalarea. Based on the information of comprehensive evaluation factorX, the occurrenceof deformed coal is predicted and it is the reliable basis for the evaluation of gasoutburst risk.更多还原