| 人工神经网络在致密砂岩气层识别中的应用 |
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| 引用本文： | 李云省,杨诚.人工神经网络在致密砂岩气层识别中的应用[J].物探化探计算技术,2002,24(1):42-45. |
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| 作者姓名： | 李云省 杨诚 |
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| 作者单位： | 1. 电子科技大学,成都,610051
2. 西南石油局研究院,成都,610081 |
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| 摘 要： | 致密砂岩气层识别是一个难题。如何在已有的地质、钻井、测井、地震等资料的基础上，利用现代人工智能方法进行气层识别，值得我们去探计。作者在本文中利用一个改进的BP网络模型，充分利用现有的地质、测井等资料，以单井为单元进行垂向气层识别，然后又以地震资料为约束进行气层横向预测，结果非常令人满意。
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| 关 键 词： | BP模型 致密砂岩 气层识别 横向预测 人工神经网络 |
| 文章编号： | 1001-1749(2002)01-0042-04 |
| 修稿时间： | 2001年6月12日 |
THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO THE IDENTIFICATION OF GAS-BEARING LAYERS IN TIGHT SANDSTONES |
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| LI Yun-sheng ,YANG Cheng.THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO THE IDENTIFICATION OF GAS-BEARING LAYERS IN TIGHT SANDSTONES[J].Computing Techniques For Geophysical and Geochemical Exploration,2002,24(1):42-45. |
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| Authors: | LI Yun-sheng YANG Cheng |
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| Institution: | LI Yun-sheng 1,YANG Cheng 2 |
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| Abstract: | It is very difficult to identify gas-bearing layers in tight sandstones. It is, thus,a worth studying to use an artificial intelligence method to recognize such kinds of gas-bearing layers on the data base of geology, well drilling, well logging as well as seismic exploration. In this paper, we use an improved BP network to identify the gas-bearing layers in a vertical direction with a full utility of the geological and logging data. Then the lateral prediction of the gas-bearing layers is made with the restraints of seismic data. The results are very satisfying. |
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| Keywords: | BP neural network model tight sandstone gas identification lateral prediction |
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