Abstract: It is difficult to predict the accuracy of the river by using the conventional seismic attributes, due to the complex conditions of the terrigenous clastic basin reservoirs and the rapid change of the rock facies. In this paper, XII7-12 layer in N area of S oilfield is taken as an example. Through the seismic forward analysis, the seismic section reflection characteristics of different types of reservoirs are different, and the seismic attributes of the combined layer can be used as the effective dimension for the main channel prediction in this area. On this basis, using the method of the combination of Gray Relational Analysis and Support Vector Machine, the N zone seismic attribute prediction based on Gray Relational Support Vector Machine is completed. Drilling confirmed that based on Gray Correlation and Support Vector Machine attribute prediction, the drilling coincidence rate is higher. By using the advantage of seismic inversion to predict the boundary of the channel sand, the comprehensive analysis of dynamic and static data of the polymer flooding well group effectively solved the contradiction between the injection and production system of the XII7-12 strata system in the N area of the S oilfield, thus, it further confirms the accuracy of attribute prediction based on GRA-SVM. Comprehensive research shows that this method is suitable for high accuracy of the river prediction and can be used as a better river prediction method under complex geological conditions.

Key words: Grey relational analysis, Support vector machine, Channel, Daqing placanticline

摘要： 于陆源碎屑盆地储层条件复杂、岩相相变快等因素影响，应用常规地震属性预测河道难以达到精度的要求。以S油田N区XII7-12层系为例，通过地震正演分析得出，不同类型储层的地震剖面反射特征不同，合层层系的地震属性可以作为该区主河道预测的有效尺度。在此基础上，利用灰色关联分析与支持向量机结合的方法，完成N区基于灰色关联支持向量机（GRA-SVM）地震属性预测。经钻井证实，基于GRA-SVM属性预测的钻井符合率较高，借助地震反演预测河道砂边界的优势，通过对聚驱井组动静态资料综合分析，有效解决了S油田N区聚驱井组XII7-12层系的注采系统矛盾，从而进一步验证了基于GRA-SVM属性预测的准确性。综合研究表明，此方法用于河道预测精度较高，可作为复杂地质条件下一种较好的河道预测方法。

关键词: 灰色关联分析, 支持向量机, 河道, 大庆长垣