用形态学滤波从电导率图像中提取缝洞孔隙度谱

doi:10.13278/j.cnki.jjuese.201704305

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

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. 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. 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)

摘要/Abstract

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

李振苓, 沈金松, 李曦宁, 王磊, 淡伟宁, 郭森, 朱忠民, 于仁江. 用形态学滤波从电导率图像中提取缝洞孔隙度谱[J]. 吉林大学学报(地球科学版), 2017, 47(4): 1295-1307.

Li Zhenling, Shen Jinsong, Li Xining, Wang Lei, Dan Weining, Guo Sen, Zhu Zhongmin, Yu Renjiang. Estimating Porosity Spectrum of Fracture and Karst Cave from Conductivity Image by Morphological Filtering[J]. Journal of Jilin University(Earth Science Edition), 2017, 47(4): 1295-1307.

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用形态学滤波从电导率图像中提取缝洞孔隙度谱

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

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