摘要
在储集层地球物理响应分析和研究的基础上,应用多属性回归与神经网络串联反演方法,对研究区进行了可以表征薄储集层的自然伽马曲线反演。分析认为,砂体预测结果符合研究区整体沉积特征,纵向分辨率较高,横向砂体边界清晰,能够反映储集层的分布规律,为研究区今后的勘探指明了方向。
This paper presents GR curves inversion for thin reservoir characterization in the studied area, using the series inversion of muhi -attribute regression （MAR） and probabilistic neural network （PNN） based on the geophysical response analysis of reservoir. The ~sult shows that the sand body prediction accords with the whole sedimentary features in the studied area, with high vertical resolution, clear boundary of lateral sand bodies. It could properly reflect the distribution of reservoirs and can be as a guide for next petroleum exploration in this area.
出处
《新疆石油地质》
CAS
CSCD
北大核心
2013年第3期324-327,共4页
Xinjiang Petroleum Geology
关键词
多属性回归
概率神经网络
串联反演
薄储集层预测
muhi-attribute regression
probabilistic neural network
series inversion
thin reservoir prediction
