吉林大学学报(地球科学版) ›› 2017, Vol. 47 ›› Issue (4): 1295-1307.doi: 10.13278/j.cnki.jjuese.201704305

• 地球探测与信息技术 • 上一篇    下一篇

用形态学滤波从电导率图像中提取缝洞孔隙度谱

李振苓1, 沈金松2, 李曦宁2, 王磊2, 淡伟宁3, 郭森1, 朱忠民2, 于仁江3   

  1. 1. 中石油测井有限公司华北测井事业部, 西安 710077;
    2. 中国石油大学(北京)地球物理与信息工程学院, 北京 102249;
    3. 中石油华北油田分公司勘探开发研究院, 河北 任丘 062552
  • 收稿日期:2016-11-22 出版日期:2017-07-26 发布日期:2017-07-26
  • 通讯作者: 沈金松(1964),男,研究员,博士生导师,主要从事地球物理测井和电磁法研究,E-mail:shenjinsongcup@163.com E-mail:shenjinsongcup@163.com
  • 作者简介:李振苓(1970),女,高级工程师,主要从事测井解释评价工作,E-mail:lizl2013@cnpc.com.cn
  • 基金资助:
    中国石油天然气股份有限公司重大科技专项项目（2017E-15）

Estimating Porosity Spectrum of Fracture and Karst Cave from Conductivity Image by Morphological Filtering

Li Zhenling1, Shen Jinsong2, Li Xining2, Wang Lei2, Dan Weining3, Guo Sen1, Zhu Zhongmin2, Yu Renjiang3   

  1. 1. China Petroleum Well Logging Company, CNPC, Xi'an 710077, China;
    2. Faculty of Geophysics and Informatics, China University of Petroleum, Beijing 102249, China;
    3. Research Institude of Exploration and Development, CNPC Huabei Oilfield Company, Renqiu 062552, Hebei, China
  • Received:2016-11-22 Online:2017-07-26 Published:2017-07-26
  • Supported by:
    Supported by Major Science and Technology Special Project of China National Petroleum Corporation (2017E-15)

PDF (PC)

368

摘要： 为了更好地实现对缝洞型储层孔隙结构和孔隙度的精细评价，基于高覆盖率和高分辨率电成像测井的电导率数据，用多尺度形态学滤波方法分离了基质孔、裂缝和溶蚀孔洞，提取了缝洞孔隙度谱。首先分析了电成像测井对裂缝和溶蚀孔洞的响应模式；其次，在简单介绍数学形态学算子的基础上，给出了结构元素选择和滤波算子构造的方法，用于电成像测井数据的噪声压制和缝洞异常电导率信息的提取；再次，基于缝洞发育处电导率异常的边缘检测结果，用椭圆形及不规则多边形函数拟合溶蚀孔洞，用多项式插值函数拟合裂缝边界，继而提取缝洞分布多类属性参数，获得缝洞孔隙度谱；最后，用实测数据对文中算法进行了测试，验证了多尺度数学形态学滤波方法用于电成像测井资料缝洞孔隙度谱计算的有效性。

关键词: 电成像测井, 多尺度形态学滤波, 结构元素, 缝洞异常边缘检测, 缝洞孔隙度谱

Abstract: From the electrical image logging data, which has complete coverage and high resolution, by adoption of the multi-scale morphology method, the total porosity volume has been separated into matrix porosity, fracture porosity and karst cave porosity, and the porosity spectrum of the fracture and karst cave has been derived as well. Firstly, the response modes of the FMI (formation microscanner image) corresponding to various fractures and karst caves were analyzed. Secondly, operators of mathematical morphology were introduced, and the method of structuring element selection and filtering operator construction were proposed to improve signal-noise ratio and identify conductivity anomaly from the FMI measurements. After that, based on the edge detection of the conductivity anomaly that were formed by fracture and karst caves, the detection results of karst caves were fitted with elliptic or polygonal functions, and the fracture results were fitted by polynomial. Thus, fracture and karst cave parameters, as well as the spectrum of porosity were deduced from the fitted edge detection results. Finelly, examples of numerical simulation data and field data were provided for the verification of the effectiveness and stability of the multi-scale morphology method in application of FMI processing.

Key words: electric imaging logging, multi-scale morphological filtering, structure element, edge detection of fracture and karst cave anomalies, porosity spectrum of fracture and karst cave

中图分类号: 

  • P631.8
[1] Luthi S M. Fractures Apertures from Electrical Borehole Scans[J]. Geophysics,1990,55(3):821-33.
[2] Luthi S M. Fractured Reservoir Analysis Using Modern Geophysical Well Techniques: Application to Basement Reservoirs in Vietnam[J]. Geological Society London Special Publications,2005,240(1):95-106.
[3] Donald J A,Bratton T R. Advancements in Acoustic Techniques for Evaluating Open Natural Fractures [C]//47th Annual Well Logging Analysts Symposium. Houston:SPWLA,2006:156-157.
[4] Endo T K,Tezuka T, Fukushima A,et al. Fracture Evaluation from Inversion of Stoneley Transmission and Reflections[C]//Proceedings of the Fourth SEGJ/SEG/ASEG International Symposium Fracture Imaging. Tokyo: [s. n.], 1998:389-394.
[5] 李茂兵. 电成像测井自动识别和定量评价研究[D]. 东营:中国石油大学(华东), 2010. Li Maobing. Automatic Recognition and Quantitative Evaluation Study on Electric Image Logging[D].Dongying: China University of Petroleum, 2010.
[6] 潘保芝, 蒋必辞, 刘文斌,等. 致密砂岩储层含气测井特征及定量评价[J]. 吉林大学学报(地球科学版), 2016, 46(3):930-937. Pan Baozhi, Jiang Bici, Liu Wenbin, et al. Gas-Bearing Logging Features and Quantitative Evaluation for Tight Sandstone Reservoirs[J]. Journal of Jilin University (Earth Science Edition), 2016, 46(3): 930-937.
[7] Delhomme J P. A Quantitative Characterization of Formation Heterogeneities Based on Borehole Image Analysis [C]//Trans 33rd Symp Soc Prof Well Log Analysts. Houston: [s. n.], 1992: 334-337.
[8] Haller D, Porturas F. How to Characterize Fratures in Reservoirs Using Borehole and Core Images: Case Studies[J]. Journal of the Acoustical Society of America, 1998, 136(1): 249-259.
[9] Ricky A W, Wisnu H, Parada D S, et al. Using Borehole Image Data for Identification of Fractures and Reservoir Characterization of Gas Carbonate Reservoir at Merbau Field, South Palembang Basin, Sumatra, Indonesia[C]//The 23rd World Gas Conference. Amsterdam: [s. n.], 2006: 1-24.
[10] Meyer F, Beucher S. Morphological Segmentation [J]. J Vis Comm and Image Representation, 1990, 1:21-46.
[11] Lucia. Petrophysical Parameters Estimated from Visual Descriptions of Carbonate Rocks:A Field Classification of Carbonate Pore Space[J]. Journal of Petroleum Technology,2013,35(35):629-637.
[12] Ramakrishnan T S, Rabaute A, Fordham E J, et al. A Petrophysical and Petrographic Study of Carbonate Cores from the Thamama Formation[C]//Abu Dhabi International Petroleum Exhibition and Conference. Abu Dhabi: SPE,1998: SPE-49502-MS.
[13] Antoine J N,Delhomme J P. Method to Derive Dips from Bedding Boundaries in Borehole Images[J]. Spe Formation Evaluation,1993,8(2):96-102.
[14] 叶斌,彭嘉雄. 基于顺序形态滤波的边缘检测[J]. 华中理工大学学报,2000,28(8):9-11. Ye Bin,Peng Jiaxiong. Edge Detection Based on Order Morphology Filtering[J]. Journal of Huazhong University of Science and Technology,2000,28(8):9-11.
[15] 李清松,潘和平,张荣. 电阻率成像测井进展[J]. 工程地球物理学报,2005,2(4):304-310. Li Qingsong,Pan Heping,Zhang Rong. The Process of Resistivity Imaging Log[J]. Chinese Journal of Engineering Geophysics,2005,2(4):304-310.
[16] Mallat S H,Wang W L. Singularity Detection and Processing with Wavelets [J]. IEEE Trans,Information Theory,1992,38(2):2132-2137.
[17] Mallat S,Zhong S. Characterization of Signals from Multiscale Edges[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,1992,14(7):710-732.
[18] Mallat S. Multi-Frequency Channel Decomposition of Image with Wavelet Models[J]. IEEE Trans,ASSP,1989,39(12):2762-2768.
[19] 赖富强. 应用声电成像测井进行裂缝检测与评价研究[D]. 东营:中国石油大学(华东),2007. Lai Fuqiang. Fracture Detecting with Acoustic and Electric Imaging Logging Data and Evaluation[D]. Dongying:China University of Petroleum,2007.
[20] Otsu N. A Threshold Selection Method from Gray-Level Histograms[J]. Systems Man & Cybernetics IEEE Transactions on,1979,9(1):62-66.
[21] Schlumberger Information Solutions. Geoframe Dip & Image Processing and Interpretation:Training and Exercise Guide[M]. Houston: Schlumberger,2001.
[22] Bourke L T. Recognizing Artifact Images of the Formation MicroScanner[C]//Proceeding of SPWLA 30th Annual Logging Symposium. Denver: [s. n.], 1989, 274-279.
[23] Lofts J C,Bedford J,Boulton H,et al. Feature Recognition and the Interpretation of Images Acquired from Horizontal Wellbores[J]. Geological Society London Special Publications,1997,122(1):345-365.
[24] Standen E. Tips for Analyzing Fractures on Electrical Wellbore Images[J]. World Oil April,1991,212(4):99-117.
[25] 秦巍,陈秀峰. 成像测井井壁图像裂缝自动识别[J]. 测井技术,2001,25(1):64-69. Qin Wei,Chen Xiufeng. A Math-Morphological Approach for Automatic Fracture Recognition on Sidewall Images[J]. Well Logging Technology,2001,25(1):64-69.
[26] Serra J. Mathematical Morphology for Boolean Lattices,Image Analysis and Mathematical Morphology: Volume 2:Theoretical Advances[M]. Pittsburgh: Academic Press,1988:37-46.
[27] Heijmans H J A M,Ronse C. The Algebraic Basis of Mathematical Morphology:Part I:Dilations and Erosions[J]. Computer Vision,Graphics,and Image Processing: Image Understanding,1990,50(2):245-295.
[28] Ronse C,Heijmans H J A M. The Algebraic Basis of Mathematical Morphology:Part Ⅱ:Openings and Closings[J]. Computer Vision,Graphics,and Image Processing: Image Understanding,1991,54(1):74-97.
[29] Soille P,Talbot H. Directional Morphological Fil-tering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(11):1313-1329.
[30] Niblack W. An Introduction to Digital Image Processing[M]. Englewood Cliffs:Prentice Hall,1986.
[31] Lee C K,Wong S P. A Mathematical Morphological Approach for Segmenting Heavily Noise Corrupted Image[J]. Pattern Recognition,1996,29(8):1347-1358.
[32] Ye S J,Baviller P. Automated Fracture Detection on High Resolution Resistivity Borehole Imagery[C]//Proceeding of SPE Annual Technical Conference and Exhibition. New Orleans: SPE, 1998: SPE-49300-MS.
[33] James J L. Morphologic Edge Detection [J]. IEEE Journal of Robotics and Automation,1987,3(2):142-156.
[34] 曹飞. 裂缝性岩石声波参数实验研究及裂缝性储层测井评价[D]. 长春:吉林大学, 2015. Cao Fei. The Acoustic Parameters Experimental Research of Fractured Rocks and the Log Evaluation of Fractured Reservoirs[D]. Changchun: Jilin University, 2015.
[35] 刘建庄,栗文清. 灰度图像的二维Otsu自动阈值分割法[J]. 自动化学报,1993,19(1):101-105. Liu Jianzhuang,Li Wenqing. The Automatic Thresholding of Gray-Level Pictures via Two-Dimensional Otsu Method[J]. Acta Automatica Sinica,1993,19(1):101-105.
[36] Soille P,Breen E,Jones R. Recursive Implementation of Erosions and Dilations Along Discrete Lines at Arbitrary Angles[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(5):562-567.
[37] 牟丹,王祝文,黄玉龙,等. 基于最小二乘支持向量机测井识别火山岩类型:以辽河盆地中基性火山岩为例[J]. 吉林大学学报(地球科学版),2015,45(2):639-648. Mou Dan, Wang Zhuwen, Huang Yulong, et al. Application of Least Squares Support Vector Machine to Lithology Identification: Taking Intermediate/Basaltic Rocks of Liaohe Basin as an Example[J]. Journal of Jilin University (Earth Science Edition), 2015, 45(2): 639-648.
[1] 潘保芝, 刘文斌, 张丽华, 郭宇航, 阿茹罕. 一种提高储层裂缝识别准确度的方法[J]. 吉林大学学报(地球科学版), 2018, 48(1): 298-306.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(地球科学版)》编辑部
地址：长春市西民主大街938号(130026)　电话：+86-431-88502374；13756165364　E-mail: xuebao1956@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发