用神经网络与构造属性进行交互地震相分析
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摘要
在地震资料解释工作中,一般的地震相分析工作效率较低,有时难度较大。通过计算灰度共生矩阵GL-CM,从数学上描述小范围数据区内像素值的分布,量化地震反射的空间组织结构。将构造属性与神经网络分类技术相结合,通过训练网络和质量控制,利用波形相似性和地震属性进行地震相分析,可用于对三维地震资料进行地震相的划分,得到三维地震相分类数据体。这种方法减少了许多耗时的工作,使解释人员能够集中精力研究地震相,并将其综合成地质成果图。
Conventionally seismic facies analysis is implemented manually which is time-consuming, and in some cases, is difficult to map different seismic facies consistently. Calculation of grey-level co-occurrence matrices (GLCM) helps mathematically describe the distribution of pixel values within a subregion of data, and effectively quantify the spatial structures of seismic reflections. Thus seismic textural analysis and neural network can be successfully combined. After training and QC, seismic facies are classified in terms of waveform similarity and seismic attributes. 3-D seismic facies classification data volume can be generated.
Conventionally seismic facies analysis is implemented manually which is time-consuming, and in some cases, is difficult to map different seismic facies consistently. Calculation of grey-level co-occurrence matrices (GLCM) helps mathematically describe the distribution of pixel values within a subregion of data, and effectively quantify the spatial structures of seismic reflections. Thus seismic textural analysis and neural network can be successfully combined. After training and QC, seismic facies are classified in terms of waveform similarity and seismic attributes. 3-D seismic facies classification data volume can be generated.
引文
1 宋维琪.应用地震属性与测井数据反演储层参数[J].勘探地球物理进展,2003,26(3) :216-219
2 Vinther R, Mosegaard K, Abatzis I, et al. 3D seismic texture classification[J]. SPE 35482,1996,57-63
3 Randen T,Monsen E, Signer C,et al. Three-dimensional texture attributes for seismic data analysis[J]. Expanded Abstracts of 70th Annual Internal SEG Mtg, 2000,668-671
4 West B P, May S R, Eastwood J E, et al. Interactive seismic facies classification using textural attributes and neural networks[J]. The Leading Edge, 2002, 21 (10) : 1042-1050
5 Baraldi A,Parmiggian F. An investigation of the texture characteristics associated with gray level co-occurrence matrix statistical parameters [J]. IEEE Transaction on Geosciences and Remote Sensing, 1995, 33(2) : 293-303
6 肖温格.遥感中的图像处理和分类技术[M].北京:科学出版社,1991． 113～116
7杨国权,王永刚,吴名,等.岔路河断陷T_(B-C)层序地震相沉积相研究[J].石油物探,1997,36(增刊):41～44
8 刘传虎.陆相湖盆洼陷带浊积砂体岩性油藏地震描述技术[J].石油物探,2000,39(4) :64-74
9孙建国.勘探地球物理技术最新进展--2002年SEG年会综述Ⅱ:解释与压力预测[J].勘探地球物理进展,2003,26(2) :148～159
2 Vinther R, Mosegaard K, Abatzis I, et al. 3D seismic texture classification[J]. SPE 35482,1996,57-63
3 Randen T,Monsen E, Signer C,et al. Three-dimensional texture attributes for seismic data analysis[J]. Expanded Abstracts of 70th Annual Internal SEG Mtg, 2000,668-671
4 West B P, May S R, Eastwood J E, et al. Interactive seismic facies classification using textural attributes and neural networks[J]. The Leading Edge, 2002, 21 (10) : 1042-1050
5 Baraldi A,Parmiggian F. An investigation of the texture characteristics associated with gray level co-occurrence matrix statistical parameters [J]. IEEE Transaction on Geosciences and Remote Sensing, 1995, 33(2) : 293-303
6 肖温格.遥感中的图像处理和分类技术[M].北京:科学出版社,1991． 113～116
7杨国权,王永刚,吴名,等.岔路河断陷T_(B-C)层序地震相沉积相研究[J].石油物探,1997,36(增刊):41～44
8 刘传虎.陆相湖盆洼陷带浊积砂体岩性油藏地震描述技术[J].石油物探,2000,39(4) :64-74
9孙建国.勘探地球物理技术最新进展--2002年SEG年会综述Ⅱ:解释与压力预测[J].勘探地球物理进展,2003,26(2) :148～159
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