Please wait a minute...
浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (3): 568-576    DOI: 10.3785/j.issn.1008-973X.2012.03.028
地球科学     
基于Haar小波变换的渐弃型废弃河道相识别
孔凡立1,李霁2,邹乐君１,沈晓华１,吴文渊１,苏楠１
1.浙江大学 地球科学系, 浙江 杭州 310027; 2.浙江省电子产品检验所, 浙江 杭州 310012
Haar wavelet transformation-based gradually
 abandoned channel facies identification
KONG Fan-li1,LI Ji2,ZOU Le-jun1,SHEN Xiao-hua1,WU Wen-yuan1,SU Nan1
1.Department of Earth Science, Zhejiang University, Hangzhou 310027, China;
2. Zhejiang Testing Institute of Electronic Products, Hangzhou 310012, China
 全文: PDF  HTML
摘要：

针对测井曲线进行沉积微相分析和渐弃型废弃河道微相计算机自动识别的问题,提出基于Haar小波变换的测井曲线沉积微相和渐弃型废弃河道识别的方法.在该方法中,应用小波分解将测井曲线的信号分解为宏观特征信息、细节信息和噪音信息三部分；将渐弃型废弃河道和普通河道作为同一类,利用Bayes判别分析对经小波变换后得到的SP、RMN、RMG曲线的宏观特征信息建立其判相模型,通过该模型将渐弃型废弃河道和普通河道与其他沉积微相进行分离；利用渐弃型废弃河道和普通河道沉积微相在微电极幅度差上的差异,将RMN – RMG值与地层深度建立拟合曲线,利用曲线的斜率为判相依据,识别出渐弃型废弃河道和普通河道.采用C/C++语言在C++ Builder平台上编写了本方法的应用程序,实现了从测井曲线沉积微相分析到平面相图自动生成的计算机化.该方法已被应用于大庆油田南二区东块1 480口井和南一区20口井的测井曲线分析处理,其结果与人工识别成果对比表明准确率达80%以上,能满足油田实际生产的要求.
Abstract:

This work exploited the application of Haar wavelet approach to sedimentary microfacies analysis using log data and gradually abandoned channel facies identifying. This method  first, uses wavelet transformation to separate noise and minutiae from large scale patterns for log data. Then normal channels and gradually abandoned channels are regarding as the same, distinguished from the other sedimentary microfacies by Bayesian model created by SP, RMN and RMG values obtained from the large scale patterns. Finally, according to difference in the RMNGMG between the two microfacies, the slope of the best fit line of RMNRMG against depth is used to identify gradually abandoned channels and normal ones. A program to implement the method was developed in C/C++ on C++ Builder platform, which computerizes the sedimentary microfacies analysis with log data and facies ichnography creation. This highly computerized, easy-operating method has been used to analyze the log data of 1 480 wells in the east part of No.2 south area and 20 wells in No.1 south area in Daqing Oil Field, and the result was well consistent with that of manual work. The success rate of the procedure in correctly identifying the sedimentary microfacies is 80%, which shows its great potential for use in the exploration of meandering depositional oil fields．
出版日期: 2012-03-01
:  P 512.2  
基金资助:

国家重大科技专项资金资助项目(R20100006).
通讯作者: 邹乐君，男，教授，博导.     E-mail: zoulejun2006@zju.edu.cn
作者简介: 孔凡立(1986-)，男，硕士生，研究领域为构造地质学. E-mail: kfl621@126.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　

引用本文:

孔凡立,李霁,邹乐君,沈晓华,吴文渊,苏楠. 基于Haar小波变换的渐弃型废弃河道相识别[J]. J4, 2012, 46(3): 568-576.

KONG Fan-li,LI Ji,ZOU Le-jun,SHEN Xiao-hua,WU Wen-yuan,SU Nan. Haar wavelet transformation-based gradually
 abandoned channel facies identification. J4, 2012, 46(3): 568-576.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2012.03.028    或    http://www.zjujournals.com/eng/CN/Y2012/V46/I3/568

［1］ 马世忠, 黄孝特, 张太斌. 定量自动识别测井微相的数学方法［J］. 石油地球物理勘探, 2000, 35(5): 582-590, 616．
MA Shizhong, HUANG Xiaote, ZHANG Taibin. Mathematic method for quantitative automatic identification of logging microfacies ［J］. Oil Geophysical Prospecting, 2000, 35(5): 582-590, 616．
［2］ 刘红歧, 陈平, 夏宏泉. 测井沉积微相自动识别与应用［J］. 测井技术, 2006, 30(3): 233-236．
LIU Hongqi, CHEN Ping, XIA Hongquan. Automatic identification of sedimentary microfacies with log data and its application ［J］. Well Logging Technology, 2006, 30(3): 233-236．
［3］ WOLFF M, PELISSIERCOMBESCURE J. Faciologautomatic electrofacies determination ［C］∥ SPWLA 23rd Annual Logging Symposium. Corpus Christi: Society of Petrophysicists & Well Log Analysts, 1982: 6-9．
［4］ 文政, 雍世和, 王中文. 应用测井资料定量识别沉积微相［J］. 沉积学报, 1996, 14(1): 40-46．
WEN Zheng, YONG Shihe, WANG Zhongwen. Quantitative discrimination of sedimentary microfacies by using well log data ［J］. Acta Sedimentologica Sinica, 1996, 14(1): 40-46．
［5］ 王任一, 梅廉夫. 基于方向概率密度和小波描述子的沉积微相识别研究［J］. 沉积学报, 2008, 6(6): 947-956．
WANG Renyi, MEI Lianfu. Sedimentation microfacies identification based on direction probability density and wavelet descriptor ［J］. Acta Sedimentologica Sinica, 2008, 6(6): 947-956．
［6］ TANG Hong, WHITE C D. Multivariate statistical log logfacies classification on a shallow marine reservoir ［J］. Journal of Petroleum Science and Engineering, 2008, 61: 88-93．
［7］ LI Yumei, ANDERSONSPRECHER R. Facies identification from well logs: a comparison of discriminant analysis and naive Bayes classifier ［J］. Journal of Petroleum Science and Engineering, 2006, 53: 149-157．
［8］ 倪新锋, 田景春, 陈洪德, 等.应用测井资料定量识别沉积微相——以鄂尔多斯盆地姬塬白豹地区三叠系延长组为例［J］. 成都理工大学学报：自然科学版,2007, 34(1): 57-61．
NI Xinfeng, TIAN Jingchun, CHEN Hongde, et al. Quantitative discrimination of sedimentary microfacies by use of log data—taking the triassic Yanchang formation in JiyuanBaibao region of ordos basin for example ［J］. Journal of Chengdu University of Technology ：Science & Technology Edition, 2007, 34(1): 57-61．
［9］ CHANG Hsiencheng, KOPASKAMERKEL D C, CHEN Huichuan, et al. Lithofacies identification using multiple adaptive resonance theory networking and group and group decision expert system ［J］. Computer & Geosciences, 2000, 26: 591-601．
［10］ 冉启全, 李士伦, 顾小芸. 神经网络模式识别沉积微相［J］. 沉积学报, 1996, 14（增刊）: 186-191．
RAN Qiquan, LI Shilun, GU Xiaoyun. Identification of sedimentary microfacies with with neural network ［J］. Acta Sedimentologica Sinica, 1996, 14(suppl): 186-191．
［11］ DAUBECHIES I. Ten lectures on wavelets ［M］. 1st ed. ［S. 1.］: Society for Industrial and Applied Mathematics, 1992: 3-4．
［12］ 唐启义, 冯明光. 实用统计分析及其DPS数据处理系统［M］. 北京: 科学出版社, 2002: 359-363．
［13］ 王元英. 曲流河废弃河道的识别方法及其应用［J］. 大庆石油学院学报, 2007, 31(5): 65-67．
WANG Yuanying. Identification and application of abandoned channel in meander river ［J］. Journal of Daqing Petroleum Institute, 2007, 31(5): 65-67.
No related articles found!