| 基于小波高频属性的泥页岩裂缝测井识别方法研究 |
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| 引用本文： | 赖富强,夏炜旭,龚大建,毛海艳,罗涵.基于小波高频属性的泥页岩裂缝测井识别方法研究[J].地球物理学进展,2020(1):124-131. |
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| 作者姓名： | 赖富强 夏炜旭 龚大建 毛海艳 罗涵 |
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| 作者单位： | 重庆科技学院复杂油气田勘探开发重庆市重点实验室;中国国储能源化工集团股份公司;铜仁中能天然气有限公司 |
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| 基金项目： | “十三五”国家科技重大专项子课题“岑巩区块海相高演化页岩气勘查评价应用试验”(2016ZX05034004-003);国家青年科学基金项目“基于成像测井的泥页岩储层可压裂性评价方法研究”(41402118);重庆市基础研究与前沿探索项目“页岩储层多组分多尺度数字岩心构建及脆性微观成因机理模拟研究”(cstc2018jcyjAX0503);重庆市教委科学技术研究项目“基于深度学习的碳酸盐岩储层缝洞自动识别及评价研究(KJQN201801502)”联合资助. |
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| 摘 要： | 近几年受地震属性分析技术的启发,出现了利用测井属性分析泥页岩裂缝油气藏的新方法.为了避免常规测井信号受到噪声干扰影响裂缝识别效果,利用小波变换的高频属性对裂缝识别展开研究.以贵州岑巩地区下寒武统牛蹄塘组的泥页岩裂缝性油气藏为例,观察和分析野外黑色页岩露头、岩心薄片及镜下照片,总结了该地区裂缝发育特征和成因;然后着重利用研究区的测井资料,运用小波阈值去噪和小波高频属性方法对裂缝进行了识别.研究结果表明:研究区主要发育高角度剪性缝、张剪性缝、低角度滑脱缝及层间缝,采用的小波阈值去噪方法具有高信噪比和低均方差特点,在保留原始信号特点的基础上得到更加真实的信号;通过小波高频属性方法提取了声波时差信号中的高频信号,识别出了常规测井方法无法识别的泥页岩裂缝,该套方法成功在贵州岑巩页岩气裂缝识别中得到成功应用,并具有一定的借鉴意义.
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| 关 键 词： | 泥页岩 裂缝 牛蹄塘组 测井属性 小波高频属性 |
Logging identification method of mud shale fractures based on wavelet high frequency attribute |
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| LAI Fu-qiang,XIA Wei-xu,GONG Da-jian,MAO Hai-yan,LUO Han.Logging identification method of mud shale fractures based on wavelet high frequency attribute[J].Progress in Geophysics,2020(1):124-131. |
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| Authors: | LAI Fu-qiang XIA Wei-xu GONG Da-jian MAO Hai-yan LUO Han |
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| Institution: | (Key Laboratory of Complex Oil and Gas Field Exploration and Development,Chongqing University of Science and Technology,Chongqing 401331,China;China Energy Reserve Corporation,Beijing 100107,China;Tongren Sino-Energy Natural Gas Corporation,Tongren 554300,China) |
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| Abstract: | In recent years,inspired by the seismic attribute analysis technique,a new method of logging attribute analysis of shale fractured oil and gas reservoirs has emerged.In order to avoid the conventional logging signal is subject to noise interference thus affect the fracture recognition effect,the paper studies the fracture recognition based on the wavelet transform method.Taking the shale fractured oil and gas reservoirs of the Lower Cambrian Niutitang Formation in the Cengang block of Guizhou Province as an example,the fractures of black shale outcrops,core sheets and microscopic photographs were observed and analyzed and summarizes the characteristics and causes of fracture development in this area.Then focus on using study area of logging data,through the wavelet threshold denoising and wavelet high frequency attribute method to identify the fractures.The results show that the study area mainly develops high angle shear seam,Zhang shear seam,low angle slip seam and interlayer seam,the wavelet threshold denoising method has the characteristics of high signal to noise ratio and low mean square error,and obtains more real signal on the basis of preserving the original signal characteristics.The high frequency signal in the acoustic time difference signal is extracted by the wavelet high frequency attribute method,and the shale fractures which is not recognized by the conventional logging method is identified,the set of methods has been successfully applied in the identification of shale gas fractures in Cengang,Guizhou Province,and has certain reference significance. |
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| Keywords: | Mud shale Fracture Niutitang Formation Logging attribute Wavelet high frequency attribute |
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