前馈神经网络的岩性识别方法

卢新卫; 金章东

doi:10.11743/ogg19990119

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专辑

前馈神经网络的岩性识别方法
METHOD OF LITHOLOGIC RECOGNIZING BASED ON FEEDFORWARD NEURAL NETWORK
1999年20卷第1期页码：81-84
网络首发：1999-03-25，

纸质出版：1999
DOI： 10.11743/ogg19990119
稿件说明：

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卢新卫, 金章东. 前馈神经网络的岩性识别方法[J]. 石油与天然气地质, 1999,20(1):81-84. DOI： 10.11743/ogg19990119.

Lu Xinwei, Jin Zhangdong. METHOD OF LITHOLOGIC RECOGNIZING BASED ON FEEDFORWARD NEURAL NETWORK[J]. 1999, 20(1): 81-84. DOI： 10.11743/ogg19990119.

摘要

测井资料的地质解释是测井过程中十分重要的环节。岩芯资料少

测井资料较多及测井参数分布的模糊性

是岩性识别中的困难所在。在引入前馈神经网络方法的基础上

以取芯井岩芯与测井参数的对应关系作为识别模式

经过向识别模式学习获得模式识别智能知识

从而利用这些智能知识去识别未取芯井的测井岩性。通过对胜利油田永一地区沙河街组四段测井岩性的计算机判识

正确判别率达100%.应用结果表明

神经网络方法性能良好

具有极好的应用前景。

Abstract

Geological explanation of logging data is an important link of logging work.When there are scanty core data and more log data

feedforward neural network is a very effective method to recognize the logging lithology.This paper introduces an identifying method of logging lithology by the pattern recognition approach using feedbach nueral network.The approach firstly regards the relationship between drilling cores and logging parameters as an identifying pattern so as to obtain intelligent knowledge from the identifying pattern

and finally applies the knowledge to identify the logging lithology of well.The method was used in lithologic recognition of the 4th member of Shahejie Formation in Shengli Oilfields

the result is satisfactory and shows good application prospects.