本文關(guān)鍵詞： 煤與瓦斯突出預測 構(gòu)造煤 PNN反演 彈性波阻抗反演 同步反演　出處：《中國礦業(yè)大學》2013年博士論文　論文類型：學位論文

【摘要】：煤與瓦斯突出災(zāi)害已經(jīng)成為導致中國煤礦特大惡性事故的“頭號殺手”，“瓦斯不治，礦無寧日”。中國煤系地層構(gòu)造十分復雜且地應(yīng)力大，采掘時極易發(fā)生瓦斯突出現(xiàn)象。面對如此嚴峻的煤礦安全形勢，深入研究礦井和瓦斯地質(zhì)賦存情況，為礦井安全生產(chǎn)提供可靠的地質(zhì)保障和科學依據(jù)刻不容緩。構(gòu)造煤作為煤與瓦斯突出的高危煤體是發(fā)生突出的必要條件，預測煤層中構(gòu)造煤發(fā)育程度是評價瓦斯突出危險可能性的重要依據(jù)之一。 論文利用巖性地震反演方法(包括概率神經(jīng)網(wǎng)絡(luò)反演方法、彈性波阻抗反演方法和同步反演方法)，，研究了陽煤集團新景煤礦佛洼區(qū)15#煤層構(gòu)造煤發(fā)育情況，以煤層中構(gòu)造煤分布的角度評價來完成煤層瓦斯突出危險性預測。 第一，從構(gòu)造煤巖石地球物理特征出發(fā)，分析了構(gòu)造煤與原生煤在物理性質(zhì)和化學性質(zhì)方面的顯著差異，總結(jié)歸納出幾種常見巖石物理量的相互轉(zhuǎn)化經(jīng)驗公式，為識別煤層中構(gòu)造煤提供理論依據(jù)。 第二，從地震屬性技術(shù)和基于模型反演理論入手，分析了地震屬性反演的各種方法，研究了基于概率神經(jīng)網(wǎng)絡(luò)的疊后地震反演方法。 第三，從疊前地震反演的理論基礎(chǔ)Zeoppritz方程出發(fā)，研究了基于Zeoppritz方程近似公式的彈性波阻抗反演理論，以及另一種疊前地震反演方法同步反演。 第四，完成陽煤集團新景煤礦佛洼區(qū)三維地震資料處理和地震巖性反演計算。利用概率神經(jīng)網(wǎng)絡(luò)反演方法獲得孔隙度數(shù)據(jù)體；利用聲波阻抗反演方法獲得聲波阻抗數(shù)據(jù)體；利用彈性波阻抗反演方法獲得彈性波阻抗數(shù)據(jù)體；利用同步反演方法獲得λ*ρ和μ*ρ巖性指示因子數(shù)據(jù)體。 第五，利用各種巖性數(shù)據(jù)體對15#煤層中構(gòu)造煤分布進行預測。將孔隙度數(shù)據(jù)作為定性解釋依據(jù)；彈性波阻抗(≤0.17)和聲波阻抗(≤0.2)交會區(qū)域，λ*ρ值(≤15)和值(≤10)交會區(qū)域作為定量解釋依據(jù)聯(lián)合解釋。 最后，提出了綜合評價因子X的概念，實質(zhì)是各類巖性數(shù)據(jù)體給定權(quán)重的線性組合。通過綜合評價因子將15#煤層劃分為5個巖性區(qū)域，即無構(gòu)造煤分布區(qū)域、幾乎無構(gòu)造煤分布區(qū)域、構(gòu)造煤分布范圍較小區(qū)域、構(gòu)造煤分布范圍較大區(qū)域和構(gòu)造煤分布區(qū)域。利用綜合巖性指標預測了15#煤層瓦斯突出危險的可能性。
[Abstract]:The disaster of coal and gas outburst has become the "number one killer" that leads to serious accidents in coal mines in China. Gas outburst is easy to occur in mining. In the face of such a severe coal mine safety situation, in-depth study of mine and gas geological occurrence, It is urgent to provide reliable geological guarantee and scientific basis for mine safety production. Structural coal, as a high risk coal body for coal and gas outburst, is a necessary condition for outburst. Predicting the development degree of tectonic coal in coal seam is one of the important bases for evaluating the possibility of gas outburst. Using lithologic seismic inversion methods (including probabilistic neural network inversion method, elastic wave impedance inversion method and synchronous inversion method), the paper studies the development of structural coal in 15 # coal seam in Fuwa area of Xinjing Coal Mine of Yangmai Coal Group. The risk prediction of coal seam gas outburst is accomplished by evaluating the distribution of tectonic coal in coal seam. First, based on the geophysical characteristics of tectonic coal and rock, the significant differences between tectonic coal and primary coal in physical and chemical properties are analyzed, and the empirical formulas for the mutual transformation of several common rock physical quantities are summarized. It provides theoretical basis for identifying structural coal in coal seam. Secondly, based on seismic attribute technology and model-based inversion theory, various methods of seismic attribute inversion are analyzed, and poststack seismic inversion method based on probabilistic neural network is studied. Thirdly, based on the Zeoppritz equation, the elastic wave impedance inversion theory based on the approximate formula of Zeoppritz equation and another method for simultaneous inversion of prestack seismic inversion are studied. In 4th, 3D seismic data processing and seismic lithology inversion calculation were completed in Fuwa area of Xinjing Coal Mine of Yangmei Group. Porosity data volume was obtained by using probabilistic neural network inversion method, acoustic impedance data volume was obtained by acoustic impedance inversion method, and acoustic impedance data volume was obtained by acoustic impedance inversion method. The elastic wave impedance data volume is obtained by the elastic wave impedance inversion method, and the 位 * 蟻 and 渭 * 蟻 lithologic indicator factor data volumes are obtained by the synchronous inversion method. In 5th, using various lithologic data bodies to predict the distribution of tectonic coal in coal seam 15#, the porosity data was taken as the basis of qualitative interpretation. Elastic wave impedance (鈮

本文編號：1527983