• 石油与天然气工程 •

### 基于灰色关联支持向量机的河道预测在S油田N区的应用

1. (1.大庆职业学院，黑龙江 大庆 163255； 2.东北石油大学 石油工程学院，黑龙江 大庆 163318)
• 收稿日期:2017-05-09 修回日期:2017-07-05 出版日期:2018-04-26 发布日期:2018-04-25
• 作者简介::李阳(1985-),女,硕士,讲师,从事油藏描述、油田开发研究;E-mail:249562496@qq.com。
• 基金资助:
:中国石油科技创新基金项目(2016D-5007-0212)。

### Application of River Prediction Based on Gray Relational and  Support Vector Machine in N Area of S Oilfield

Li Yang 1, 2

1. (1.Daqing Vocational College, Daqing Heilongjiang 163255, China; 2.College of Petroleum Engineering Institute, Northeast Petroleum University, Daqing Heilongjiang 163318, China)
• Received:2017-05-09 Revised:2017-07-05 Online:2018-04-26 Published:2018-04-25

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.

#### 引用本文

Li Yang. Application of River Prediction Based on Gray Relational and  Support Vector Machine in N Area of S Oilfield[J]. Journal of Liaoning Petrochemical University, 2018, 38(02): 40-46.

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