摘要
随着油田勘探开发程度的不断提高,要找到有利的油气聚集带以及在开发阶段提高油田采收率,都必须进行储层沉积相分析。这里介绍一种利用自组织神经网络识别曲线形态的方法。采用将测井曲线网格化,再利用自组织神经网络识别曲线形态,进而去判别沉积相。此方法可以对测井曲线形态进行识别,且消除了测井曲线中的不确定因素,运用该方法对实际测井曲线形态的识别基本正确。
With the deep exploration and development of oilfield,sedimentary facies analysis is needed in order to find more favorable hydrocarbon accumulation zone,or to increase productivity.The paper introduces a method to identify the log curve shape using self-organization neural network.In this method,the curve is firstly gridded,then the curve shape is identified using the self-organization neural network,and the sedimentary facies could be identified finally.This method can be used to identify the log curve shape and remove some uncertain factors from log data.In this paper,the identification of the actual curve is basically accurate by using the method.
出处
《物探化探计算技术》
CAS
CSCD
2009年第6期611-616,共6页
Computing Techniques For Geophysical and Geochemical Exploration
关键词
自组织神经网络
曲线形态
模式识别
沉积相
self-organization neural network
curve shape
pattern recognizing
sedimentary facies
