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Application of Self-Organizing Competitive Network in Lithologic Identification of the Logging Data

机译:自组织竞争网络在测井资料岩性识别中的应用

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The geological information of logging data is very important for people to determine oil reserves and make the plan of exploitation. So it is essential to identify litho logy of the logging data. Neural network with self-organizing, self-learning and the ability of highly non-linear mapping has been widely used in the field of classification. It has achieved good results. Using self-organizing and self-learning ability of self-organizing neural network, this paper analyzes the factor of litho logic identification, establishes self-organizing competitive network model based on MATLAB. By comparing the two structures of basic competitive network and self-organizing competitive network we achieve litho logy classification. Experimental results show that it is feasible to identify litho logy of the logging data by self-organizing network model. It is a new method of litho logic identification and Its correct rate is high.
机译:测井数据的地质信息对人们确定石油储量和制定开采计划非常重要。因此,识别测井数据的岩性至关重要。具有自组织,自学习和高度非线性映射功能的神经网络已广泛用于分类领域。取得了良好的效果。利用自组织神经网络的自组织和自学习能力,分析了光刻逻辑识别的因素,建立了基于MATLAB的自组织竞争网络模型。通过比较基本竞争网络和自组织竞争网络的两种结构,我们实现了岩性分类。实验结果表明,通过自组织网络模型识别测井资料的岩性是可行的。它是一种新的岩性逻辑识别方法,正确率较高。

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