Prediction Of Coalbed Methane Enrichment Area Based On Seismic Multi Attribute Fusion Method

The pre-critical work of CBM resources development and utilization is to predict coalbed methane-rich region, the key method is forecasting method based seismic exploration. As a more mature CBM development regions in China earlier, Qinshui Basin, the underground coal seam structure is relatively simple, the larger coal seam thickness, higher degree of exploration. In view of a single seismic attribute inversion method uncertainty and Multiple Solutions, this paper used the fusion of multiple seismic attributes of a block area of coalbed methane prediction.The geological and petro-physical factors of coalbed methane enrichment had been summarized, and analyzes the relationship between the response and the gas content of coal and rock elastic parameters. Through laboratory analysis the measured data of coal gas content and shear wave, bulk modulus, etc. correlation between P-wave velocity changes caused by reduced gas content in the range of about 10% to 16%, the rate of change in the amount of geophysical exploration can already tell the difference.Discussed extraction methods and precautions of these attributes, and analysis the sensitivity(correlation analysis method, etc.) between them and coalbed methane content. In-depth study of multiple seismic attributes fusion method, commonly used are: multiple linear regression, cluster analysis based on seismic attributes fusion wells, each of which has its own advantages and disadvantages; In this paper, based on the exploration area for the wells seismic attributes fusion.By processing and fine interpretation of seismic data and three-dimensional post stack seismic wave impedance inversion as a solid foundation, the paper predicts the thickness information of the 2 #coal and 10 # coal, the error is less than 10%; extracted four well seismic attributes, compared to single attributes property prediction of gas content prediction and multi-attribute integration property, smaller errors are clearly in multi-attribute integration; used of information seam thickness and gas content distribution information fusion to obtain the distribution of coalbed methane-rich region, and drilling data demonstrate the effect of the higher forecast accuracy.