基于地震属性的支持向量机河道砂体厚度预测

doi:10.11885/j.issn.1674-5086.2012.10.29.01

摘要/Abstract

摘要：

松辽盆地北部中浅层扶余油层的主力储层为河道砂体，以厚度薄、沉积规模小和横向变化快为特点。针对河
道砂体的准确识别、砂体厚度的精确预测难题，提出在优化地震属性组合的基础上确定支持向量机模型，进而用支持
向量机方法定量预测河道砂体。对振幅、频率、相位、地震波形分类和相干等多种有效地震属性开展优选组合，将最好
的组合作为最终输入数据。然后调试SVM 关键参数：损失函数C、不敏感损失函数参数ε 及γ 系数，最终全部井点数
据参与计算，得出最后预测结果。预测结果不但能较好地保持地震属性的横向分辨率，整体变化趋势符合研究区的沉
积地质规律，而且在井点处吻合程度高，具有较高的砂体厚度定量预测能力。

关键词: 河道砂体, 地震属性, 优化组合, 支持向量机, 砂体厚度预测

Abstract:

Abstract：Main reservoir of Fuyu layer of middle and shallow part of northern Songliao Basin is channel sandy body，which is
characterized by its thin thickness，small sedimentary scale and lateral heterogeneity. With regard to this problem，we present the
model of support vector machine as a solution. By the optimization of the combination of effective seismic attributes，including
amplitude，frequency，phase，seismic waveform classification and coherent，the best group was for the final input data. Then
the key parameters of the SVM model include loss function C，insensitive loss function parameters ε and γ coefficient，and all
of the well point data was involved in the calculation to indicate the thickness of the channel sand. The result can maintain the
lateral resolution of the seismic data and reflect its general sedimentary pattern as well. The result of subsequently drilled wells
indicated that this method has good qualitatively predictive ability for the thickness of channel sand.

Key words: Key words：channel sand, seismic attributes, optimization, support vector machine, predication of the sand body thickness

沈加刚1，宋宗平2，关晓巍1. 基于地震属性的支持向量机河道砂体厚度预测[J]. 西南石油大学学报(自然科学版), DOI: 10.11885/j.issn.1674-5086.2012.10.29.01.

Shen Jiagang1, Song Zongping2, Guan Xiaowei1. Application of Support Vector Machine to the Prediction of the Thickness
of Channel Sand Based on Seismic Attributes[J]. 西南石油大学学报(自然科学版), DOI: 10.11885/j.issn.1674-5086.2012.10.29.01.

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