| 基于重置神经网络在油气储层预测方面的应用 |
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| 引用本文： | 王铮.基于重置神经网络在油气储层预测方面的应用[J].物探化探计算技术,2007,29(3):249-252. |
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| 作者姓名： | 王铮 |
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| 作者单位： | 成都理工大学,信息管理学院,四川,成都,610059 |
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| 摘 要： | 神经网络在地质、油气储层方面有着极其广泛的应用,但是神经网络的结构不仅直接影响到网络性能的优劣,而且较大的影响了其现实应用的效果。这里尝试着将与重置算法相结合的BP神经网络应用于油气储层预测方面,并证实重置神经网络具有更好的应用前景和现实意义。
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| 关 键 词： | 重置算法 BP神经网络 结构优化 渗透率 孔隙度 |
| 文章编号： | 1001-1749(2007)03-0249-04 |
| 修稿时间： | 05 14 2006 12:00AM |
The prediction of petrophysical properties based on the artificial neural networks with an early-restart algorithm |
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| WANG Zheng.The prediction of petrophysical properties based on the artificial neural networks with an early-restart algorithm[J].Computing Techniques For Geophysical and Geochemical Exploration,2007,29(3):249-252. |
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| Authors: | WANG Zheng |
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| Abstract: | Although the artificial neural networks have extremely extensive application in the geology,oil gas predictions,the structure of the neural networks not merely influences the quality of the network performance directly,and the prediction ability to real application.This paper combines the Back Propogation(BP) neural network with an early-restart algorithm and applies it to the hydrocarbon reservoir prediction,and the results verify that the neural network has better application prospective and realistic meaning. |
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| Keywords: | early-restart algorithm BP neural network optimization Permeability porosity |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
