节点文献

河道纹理属性分析中的灰度共生矩阵参数研究

GLCM parameters in fluvial texture analysis

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 王治国尹成雷小兰古发明吴晓华

【Author】 Wang Zhiguo1,Yin Cheng1,Lei Xiaolan2,Gu Faming3 and Wu Xiaohua4.1.School of Resources and Environment Engineering,Southwest Petroleum University,Chengdu,Sichuan 610500,China 2.The Second Gas Production Plant,Changqing Oilfield,CNPC,Xi’an,Shaanxi 710200,China 3.Changqing Branch of Geophysical Research Institute,BGP Inc.,CNPC,Xi’an,Shaanxi 710021,China 4.Sichuan Geophysical Company,CNPC,Chengdu,Sichuan 610213,China

【机构】 西南石油大学资源与环境学院长庆油田第二采气厂东方地球物理公司研究院长庆分院川庆钻探地球物理勘探公司

【摘要】 河道纹理是河流相沉积引起的地震振幅强弱变化的动力学表征。计算地震纹理属性时,灰度共生矩阵多个相互关联参数的不同选择组合对运算速度和应用效果有很大的影响。依据现代曲流河形态抽象出一个简化的三维曲流河道储层模型,通过三维高斯射线束方法数值模拟出一个无噪声的叠后地震数据体。选择不同组合的四个关键参数(灰度阶数、分析窗口尺度、灰度点对间的方向和步长),从正演地震数据中计算出四种常用的灰度共生矩阵的二次统计特征量(能量、熵、对比度和均质性)。从理论公式和水平切片的视觉效果两个角度评判,得出了一组适合描述河道纹理的灰度共生矩阵参数。其中能量和熵选择小灰度级,对比度和均质性选择高灰度级,分析窗口尺度最大为地质目标尺度的一半,灰度点对方向选择0°、45°、90°和135°四个方向,灰度点对步长都取为1。将该灰度共生矩阵参数方案应用于渤海湾盆地部分浅层河道储层的地震纹理计算,钻井证实其河道纹理形态识别的正确性。

【Abstract】 Channel texture is an acoustic expression of a fluvial facies via seismic amplitude fluctuations.The Gray Level Co-occurrence Matrix(GLCM) attribute has been proved to be a promising method for seismic texture analysis.As we try to extract seismic texture attributes,however,it is a big uncertainty how to select the optimal GLCM parameters which will impact the final estimated seismic texture results and also affect the computing time.In this paper,we study the relationship between GLCM parameters and final seismic texture results to simplify the computation of GLCM.We build an ideal synthetic channel reservoir model which is derived from a modern meandering river.Then we simulate a noise-free post-stack seismic data using 3D Gaussian beam approach.With the synthetic channel model data,we will show how to select the four key GLCM parameters including the gray levels,the size of moving window,and the distance and direction of gray pairs.Selecting various combinations of these four key parameters,we extract four GLCM secondary statistical measurements(Energy,Entropy,Contrast,and Homogeneity).Based on theoretical equations and horizontal slices of texture,we ultimately get a proper co-occurrence matrix parameter for fluvial reservoir from our synthetic channel model.For energy and entropy,the number of gray levels is lower.For contrast and homogeneity,the number of gray levels is higher.The size of moving window is smaller than the half of the size of geological target.The distance of gray pairs is usually 1.And we usually represent repetitive patterns of gray pairs at angles of 0°,45°,90° and 135° to the axes.At last,we apply our method on the field data from Bohai Bay,China.This real seismic example shows that GLCM is an effective method for accurate and reliable channel texture measurements.

【基金】 国家高技术研究发展计划(863)项目(2006AA09A102-14);国家科技重大专项(2011ZX05023-005-011,2011ZX05024-001-005)联合资助
  • 【文献出处】 石油地球物理勘探 ,Oil Geophysical Prospecting , 编辑部邮箱 ,2012年01期
  • 【分类号】TP391.41
  • 【被引频次】19
  • 【下载频次】377
节点文献中: