3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However,...3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset- based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.展开更多
The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation ...The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.展开更多
基金supported by the National Natural Science Foundation of China(No.41604107)the Scientific Research Staring Foundation of University of Electronic Science and Technology of China(No.ZYGX2015KYQD049)
文摘3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset- based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.
基金supported by the National Natural Science Foundation of China(Nos.42130812,42174151,and 41874155).
文摘The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.
