期刊文献+

人工智能神经网络在岩性识别、孔隙度和渗透率预测中的应用——以十红滩铀矿床为例 被引量:5

Application of Artificial Intelligence Neural Networks in Lithology Identification and Porosity and Permeability Prediction——An example from Shihongtan uranium deposit
下载PDF
导出
摘要 分析了传统测井解释方法的局限性。从神经网络的机理、特点出发,提出了一种基于人工智能神经网络技术的岩性识别、孔隙度和渗透率预测方法。首先选取适当的测井资料向量组成一个训练模式对,由多个训练模式对构成一个学习样本集。通过神经网络的学习,使网络记住这些特征并形成预测模型,最后根据预测模型计算相应参数。以十红滩地区的找矿目的层为对象,进行了岩性分析与对比,预测了孔隙度与渗透率,并与实测值进行了对比。上述实例分析表明,该方法用于砂岩型铀矿预测岩性、孔隙度和渗透率具有一定的可行性。与传统方法相比,该方法不需要建立具体的解释模型和计算公式,有较好的适应性和预测精度。基于人工智能神经网络技术的岩性识别、孔隙度和渗透率预测方法具有较高的实用价值。 By analyzing the limitations of the traditional logging data interpretation methods,we proposed an artificial-intelligence-neural-network-based method for lithology identification and porosity and permeability prediction according to the mechanisms and characteristics of neural networks.A training patter matching from proper logging data vectors is selected first,and then a learning sample union from several training pattern matchings is constituted,making the networks remember this characteristics and format the prediction model by learning this sample union;finally,the required parameters calculated.Lithology identification,and porosity and permeability prediction of the main set of uranium ore body occurring in Shihongtan uranium deposit with this method are consistent with real documentation.Practical application of this approach shows that it is feasible for sandstone uranium deposit.Compared with traditional methods,the approach does not require establishing concrete interpretation model and computational formula.As a result,a better adaptability and higher accuracy of prediction are obtained.The approach is valuable practice.
作者 李继安
机构地区 核工业
出处 《西北地质》 CAS CSCD 2010年第2期32-37,共6页 Northwestern Geology
基金 "十一五"国家科技支撑计划重点项目(2006BAB0B05)
关键词 人工智能神经网络 岩性识别 孔隙度和渗透率预测 artificial intelligence neural networks lithology identification porosity and permeability prediction
  • 相关文献

参考文献12

二级参考文献69

共引文献209

同被引文献115

引证文献5

二级引证文献19

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部