摘要
测井相分析是研究地层沉积相的一种手段。利用基于BP神经网络的测井相分析进行沉积相识别研究,首先将已知地区地层剖面划分为有限的测井相,通过对岩心及其对应的沉积相进行研究,用数学方法及知识推理确定各个测井相到沉积相的映射转换关系,并利用这种关系,建立沉积相库。在此基础上,运用MATLAB中的工具箱建立BP神经网络模型,把已知沉积相的测井曲线特征作为样本进行训练学习,并将提取的测井曲线特征进行分类识别,从而确定地层的沉积相。应用表明,BP神经网络能够快速完成沉积相识别,可靠性较高,可以用于测井相分析及沉积相研究。
Logging facies analysis is a method to study sedimentary facies on the basis of BP neural network.First,we have to divide the known areas of stratigraphic column into limited logging facies.Then we sould determine the transformational relation from electrofacies to sedimentary facies on the basis of mathematical methods and knowledge through research on cores and corresponding sedimentary facies.Taking advantage of such relationship establishes the sedimentary facies library.using MATLAB toolbox to establish the BP neural network model to study the characteristics of the logging curve of known sedimentary facies as training samples,classify the extrac-tion of logging characteristics,so that it can make sure the formation of sedimentary facies.Application shows that BP neural network can quickly identify sedimentary facies with high reliability,which can be used for logging fa-cies analysis and sedimentary facies.
出处
《煤田地质与勘探》
CAS
CSCD
北大核心
2012年第1期68-71,共4页
Coal Geology & Exploration
关键词
神经网络
测井相分析
沉积相
识别
neural network
logging facies analysis
sedimentary facies
identification
