摘要
火成岩泥质含量的计算对火成岩储层孔隙度、饱和度等参数的确定以及储层测井解释研究起着十分重要的作用。为准确计算火成岩泥质含量,在辽河盆地东部凹陷区域,以玄武岩为例,以10口井、40个数据点的岩芯X射线衍射泥质含量资料为基础,采用多元回归分析方法和BP神经网络算法计算火成岩泥质含量。结果表明,BP神经网络算法和多元回归分析方法均有较高的准确度。BP神经网络算法可以应用到火成岩泥质含量计算的领域。
Shale content calculation of igneous rocks plays an important role in the determination of porosity and saturation of igneous rock reservoirs and the study of reservoir logging interpretation. To accurately calculate the shale content of igneous rocks,based on a set of clay data by X-ray diffraction of basalt including 10 logs and 40 points in eastern depression of Liaohe Basin,the multiple regression analysis method and BP neural network algorithm are adopted. The results show that both BP neural network algorithm and multiple regression analysis method have high accuracy. And the BP neural network algorithm can be applied in the field of shale content calculation of igneous rocks.
出处
《世界地质》
CAS
2015年第4期1120-1124,共5页
World Geology
基金
国家重点基础研究发展计划(973)项目(2012CB822002)
关键词
火成岩
泥质含量
辽河盆地
多元回归分析
BP神经网络
igneous rock
shale content
Liaohe Basin
multiple regression analysis
BP neural network
