Study On The Prediction And Evaluation Method Of Coal And Gas Outburst Based On Seismic Information

In China, 46% of coal mine is high-gas coal mine, which has large gas content, low coal seam permeability and difficulty of gas drainage. With complex structure and large ground stress, gas outburst phenomenon can occur when mining. Whit increase of mining depth, the number and extent of coal and gas outburst disaster has been the main killer of coal mine extra large vicious accident.This paper bases on gas geology and combines rock physics knowledge. It describes the elastic parameters features with different coal structure and log response characteristics, and then summarizes the empirical formula of coalfield. Based on stratigraphy characteristics of Yangquan Coal Mine, 3-D geological model of gas outburst coal seam is built, and obtain 3-D seismic direct data by numerical Simulation. Based on seismic records, attributes and wave impedance quantitative analysis, ability to distinguish of tectonic coal will be improved with a variety of seismic lithological information, the distribution range can be determined.The prediction and evaluation method of coal and gas outburst based on seismic information is presented in this thesis. Using seismic information, principal geological factors of gas deposit and outburst in coal seam can be evaluated. On the basis, classification criteria for multi-factor evaluation will be formulated, gas outburst risk coefficient G and the risk index R is introduced. Finally, the value of gas outburst riskindex R will be used as evaluating indicator.The paper predicts and evaluates fatalness of gas outburst in Fowa and Lunan mining area, Xinjing coal mine, Yangquan mining area. Angle gathers are extracted to produced angle stacked data as EI inversion input while 3-D seismic data processing. Therefore, the prediction and evaluation method of coal and gas outburst based on seismic information has great significance for preventing gas. At the same time, combined with the characteristics of wide azimuth acquisition, six azimuths gathers volumes are extracted to produce azimuth stacked data used as input for anisotropy. This dissertation contains 124 figures, 29 tables, and 155 references. In the target area prediction work, combined with EI inversion and the AI inversion results, coal body structure scale factor P is brought up, and set P>5 as the value boundary for tectonic coal. Fuse seismic attribute, AI inversion and the EI inversion results by probabilistic neural network technique, calculate porosity of coal seam and roof. Predict fracture development density and direction of coal seam by the azimuth anisotropic technique.In combination with logging data, use AI inversion and seismic attribute to acquire lithology information about roof and bottom, structural information and coal fold, and predict the thickness of coal bed. Then, quantify and risk classification of gas outburst geological factors. According to the actual geological and mining conditions of different zone, select factors to predict and calculate gas outburst risk coefficient G and the risk index R.Finally, the prediction results will be confirmed by the actual gas outburst points.Therefore, the prediction and evaluation method of coal and gas outburst based on seismic information has great significance for preventing gas.