电成像测井在复杂砂砾岩储集层岩性识别中的应用——以准噶尔盆地玛湖凹陷西斜坡百口泉组为例

针对常规测井资料分辨率低、易受油气影响、难以有效反映砂砾岩粒径大小的问题，本文以准噶尔盆地玛湖凹陷百口泉组砂砾岩储集层为例，提出一种利用电成像测井自动识别复杂砂砾岩储集层岩性的方法。首先按粒级差异，将砂砾岩分为泥岩、砂岩、细砾岩、小中砾岩和大中砾岩；利用岩心刻度测井，分析总结了不同岩性的电成像测井特征；以图像处理技术为支撑，从电成像测井静态图像入手，通过图像灰度转化、构建灰度共生矩阵，计算砂砾岩岩性样本的对比度、相关度、熵、均匀度和能量5个特征值；依据多元统计学知识，利用贝叶斯判别分析法建立了泥岩、砂岩、细砾岩、小中砾岩及大中砾岩的岩性判别函数，进而对岩性进行判别。结果表明，电成像测井判别的岩性与岩心对比符合程度高，识别效果好。

Recognition of Complicated Sandy Conglomerate Reservoir Based on Micro-Resistivity Imaging Logging: A Case Study of Baikouquan Formation in Western Slope of Mahu Sag,Junggar Basin

Regarding the problems of low resolution of conventional logging data which can’t effectively reflect grain size of sandy conglomerate and taking the sandy conglomerate reservoir of Baikouquan formation in Mahu sag, Junggar basin as an example, the paper proposes a method to automatically recognize lithologies of complex sandy conglomerate reservoirs by using micro-resistivity imaging logging. Firstly, according to the differences in grain size, the sandy conglomerate can be classified into mudstone, sandstone, fine conglomerate, small medium-sized conglomerate and big medium-sized conglomerate. Based on the core calibrating logs, the characteristics of the micro-resistivity imaging logging are analyzed and summarized for different lithologies. With the technology of image processing, 5 characteristic values such as contrast, relevancy, entropy, uniformity and energy of the sandy conglomerate samples are calculated through image gray scale transformation and gray scale co-occurrence matrix establishment. On the basis of multivariate statistics, Bayes discriminant method is used to build lithology discriminant functions for mudstone, sandstone, fine conglomerate, small medium-sized conglomerate and big medium-sized conglomerate and the lithologies of the rocks can be determined. The results show that the lithologies identified by the micro-resistivity imaging logging highly match with those obtained from core analysis and good identification effects have been gained