PREDICT FAN DELTA SAND BODY BY USING MULTI-ATTRIBUTES VOLUME CLASSIFICATION TECHNOLOGY

Conventional neural network technology for seismic waveform classification by using one single seismic attribute is very difficult to be used to predict seismic facies and sand body distribution in low signal and noisy ratio areas.Seisfacies multi-attribute volume classification technology is based on seismic wave theory,principal component analysis (PCA),hybrid classification method and self-organizing neural network technology,by using the principle of similarity to cluster analysis seismic attribute and also seismic facies are automatically analyzed.Thus a seismic facies classification volume is obtained.Integrated with well data,the seismic facies volume is analyzed in 3D visualization.It is better to predict reservoir sand body distribution in three-dimensional space and greatly reduce uncertainty caused by single attribute seismic facies analysis.This technology is used in Wanzhuang area,Huabei Oilfield.Fan delta sand body distribution is correctly described,three prospective lithologic reservoirs are predicted clearly.Based on these results,well T12X and T47 were drilled and encountered thicker oil pays,which proved the multi-attribute volume classification prediction results.