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基于BP神经网络的测井相分析及沉积相识别
引用本文:吴灿灿,李壮福.基于BP神经网络的测井相分析及沉积相识别[J].煤田地质与勘探,2012(1):68-71.
作者姓名:吴灿灿  李壮福
作者单位:中国矿业大学资源与地球科学学院
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。

关 键 词:神经网络  测井相分析  沉积相  识别

Logging facies analysis and sedimentary facies identification based on BP neural network
WU Cancan,LI Zhuangfu.Logging facies analysis and sedimentary facies identification based on BP neural network[J].Coal Geology & Exploration,2012(1):68-71.
Authors:WU Cancan  LI Zhuangfu
Affiliation:(School of Resource and Earth Science,China University of Mining &Technology,Xuzhou 221116,China)
Abstract: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.
Keywords:neural network  logging facies analysis  sedimentary facies  identification
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