[1]刘华蓥,孙春雨,张方舟,等. 基于粗集理论的气测录井数据归一化处理[J].计算机技术与发展,2015,25(07):189-192.
　LIU Hua-ying,SUN Chun-yu,ZHANG Fang-zhou,et al. Gas Logging Data Normalization Processing Based on Rough Set Theory[J].,2015,25(07):189-192.
点击复制

 基于粗集理论的气测录井数据归一化处理()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年07期
页码:
189-192
栏目:
应用开发研究
出版日期:
2015-07-10

文章信息/Info

Title:
 Gas Logging Data Normalization Processing Based on Rough Set Theory
文章编号:
1673-629X（2015)07-0189-04
作者:
 刘华蓥; 孙春雨; 张方舟; 邱露露; 高晓松
 东北石油大学 计算机与信息技术学院
Author(s):
 LIU Hua-ying ; SUN Chun-yu ; ZHANG Fang-zhou ; QIU Lu-lu ; GAO Xiao-song
关键词:
 ; 数据归一化; 气测录井; RBF神经网络; 粗集理论
Keywords:
 ; data normalization; gas logging ; RBF neural network; genetic algorithm; rough set theory
分类号:
TP39
文献标志码:
A
摘要:
 在现代录井工程中，地质情况的不同、钻井工艺的优差等诸多原因都会影响气测录井资料的录取。即使在同一地区、同一层位进行钻井勘探，测量的气测资料结果也会存在很大差异。可想而知如果在不同的地区、不同的层位进行勘探会导致测量气测资料变得更加困难与复杂。因此，快速、有效的规范气测资料及参数选择是现代录井工艺与油气层识别技术中至关重要的步骤。针对RBF神经网络算法具有收敛速度慢且不稳定等缺点，无法有效处理气测录井资料，文中提出了一种基于粗集理论的归一化处理方法，利用粗集理论对气测样本数据归一化处理后提高了RBF神经网络的训练速度。为了验证方法的可行性，以辽河油田的气测录井数据为背景进行仿真计算，实验结果表明此方法有效地提高了RBF神经网络处理气测录井数据速度。
Abstract:
 In the modern mud logging engineering,different geological conditions,the good and poor of drilling process,and other reasons will have effects on gas logging data. Drilling exploration,even in the same area,the same horizon,makes the measurement results of gas logging data different highly. It can imagine if in different layers of different area,exploration will result in measuring gas data becomes more difficult and complex. Therefore,fast,effective and standardized gas logging data and parameter selection is essential to modern mud logging technology and oil and gas reservoir recognition technology. In view of the disadvantage of slow convergence and instability of RBF neural networks algorithm,unable to handle the gas effective logging data,present a normalization method based on rough set theo-ry,using rough set theory on gas logging normalized sample data to improve RBF neural network training speed. In order to verify the feasibility of the method,taking the Liaohe oil field gas logging data calculation as the background of the simulation,the experimental re-sults show that this method effectively improves gas logging data speed RBF neural network dealt with.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(07):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(07):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(07):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(07):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(07):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(07):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(07):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(07):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(07):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(07):47.
[11]张方舟,张媛媛,金宗泽,等. 基于NNFR神经模糊推理的储层参数识别与评价[J].计算机技术与发展,2015,25(06):211.
　ZHANG Fang-zhou,ZHANG Yuan-yuan,JIN Zong-ze,et al. Fuzzy Inference Reservoir Parameter Identification and Evaluation Based on NNFR[J].,2015,25(07):211.

更新日期/Last Update: 2015-09-08