Large-scale Fracture Prediction of Coal Seam Based on Post-stack Seismic Geometric Attributes Fusion
The large-scale fracture in coal seam is one of the main factors leading to gas out-burst and water inrush in coal mines.The accurate prediction of large-scale fracture in coal seam is very important for coal seam gas development and mine water damage control.Based on the post-stack seismic data,this paper extracts the post-stack seismic geometric attributes that can reflect the geometric characteristics and distribution information of fractures in the target horizon.In order to avoid multiple solutions or uncertainties of single seismic geometry attributein predicting large-scale fractures of coal seams,PCA(principal component analy-sis)-BP(back propagation)neural network(combining principal component analysis and back propagation neural network)model are used to fuse seismic geometric properties such as coherence,curvature,inclination and varianceto predictthe distribution characteristics of large-scale fractures in target coal seam.By comparing the predicted results with the actual disclosure,90％of the fault locations are consistent with the large-scale fracture develop-ment area,and the fusion results are in good agreement with the structural interpretation re-sults.It shows that the prediction method based on the fusion of seismic geometric attributes has a desirable effect in depicting the characteristics of large-scale fractures in coal seams.
NETL
NSTL
万方数据
维普
