岩性油气藏 ›› 2026, Vol. 38 ›› Issue (4): 91–100.doi: 10.12108/yxyqc.20260408

• 地质勘探 • 上一篇    下一篇

基于改进电成像测井矿物概率谱的碎屑岩组分定量表征方法

任昱霏1,2(), 闫建平2,3,4(), 闫柯1, 黄莉莎5, 王敏6, 耿斌6   

  1. 1 中国石油西南油气田公司 勘探事业部， 成都 610041
    2 西南石油大学 地球科学与技术学院， 成都 610500
    3 西南石油大学 天然气地质四川省重点实验室， 成都 610500
    4 西南石油大学 油气藏地质及开发工程全国重点实验室， 成都 610500
    5 中国石油塔里木油田公司 勘探开发研究院， 新疆 库尔勒 841000
    6 中国石化胜利油田分公司 勘探开发研究院， 山东 东营 257015
  • 收稿日期:2025-12-27 修回日期:2026-02-04 出版日期:2026-07-01 发布日期:2026-07-06
  • 第一作者:任昱霏（2001—），女，硕士，助理工程师，主要从事测井地质学方面的研究工作。地址：(610041)四川省成都市高新区天府大道北段12号。Email:renyufei03@163.com。
  • 通信作者: 闫建平
  • 基金资助:
    国家科技重大专项课题“致密油有效储层表征技术”(2017ZX5072-002);国家自然科学基金项目“低电阻率页岩气储层：成因机制差异及含气饱和度模型研究”(42372177);中国石油-西南石油大学创新联合体科技合作项目“川南深层与昭通中浅层海相页岩气规模效益开发关键技术研究”(2020CX020000)

Quantitative characterization method for clastic rock components based on improved electrical imaging logging mineral probability spectrum

REN Yufei1,2(), YAN Jianping2,3,4(), YAN Ke1, HUANG Lisha5, WANG Min6, GENG Bin6   

  1. 1 Exploration Division, PetroChina Southwest Oil and Gas Field Company, Chengdu 610041, China
    2 School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    3 Natural Gas Geology Key Laboratory of Sichuan Province, Southwest Petroleum University, Chengdu 610500, China
    4 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
    5 Research Institute of Exploration and Development, PetroChina Tarim Oilfield Company, Korla 841000, Xinjiang, China
    6 Research Institute of Exploration and Development, Sinopec Shengli Oilfield Company, Dongying 257015, Shandong, China
  • Received:2025-12-27 Revised:2026-02-04 Online:2026-07-01 Published:2026-07-06
  • Contact: YAN Jianping E-mail:renyufei03@163.com;yanjp_tj@163.com

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摘要：

针对深层复杂碎屑岩储层矿物组分非均质性强、传统测井方法刻画精度有限的问题，提出了一种基于改进电成像测井矿物概率谱的组分定量计算方法，实现了对长英质、方解石和黏土等关键矿物含量的连续精确计算。研究结果表明：①将电成像电阻率数据转化为标准化像素值，通过直方图均衡化与正态分布处理构建像素波形谱，进而基于Archie公式和电导率并联模型，对像素值进行孔隙度贡献校正，提取出反映纯矿物成分的成像矿物谱。②利用X射线衍射（XRD）全岩分析数据对成像矿物谱进行标定，采用最优分割算法明确区分泥质、长英质和方解石的最佳像素值截止阈值（方解石/长英质界限15%、长英质/黏土界限70%），建立从成像矿物谱到矿物含量的定量计算模型。③实际单井应用中，电成像矿物计算模型显著提升了矿物识别精度，计算矿物含量与XRD实测数据相关系数R2 > 0.800 0，可识别因钙质胶结导致的低物性层段，有助于复杂碎屑岩储层的精细评价。

关键词: 碎屑岩, 电成像测井, 矿物组分, 岩性识别, 电导率, 储层精细评价, 黄流组, 莺歌海盆地

Abstract:

To address the strong heterogeneity of mineral components in deep complex clastic reservoirs and the limited accuracy of conventional logging methods, a quantitative calculation method based on improved electrical imaging logging mineral probability spectra was proposed, achieving continuous and precise determination of key mineral contents such as feldspar, calcite, and clay. The research findings indicate: （1） The resistivity data of electrical imaging was converted into standardized pixel values, and pixel waveform spectra were constructed through histogram equalization and normal distribution processing. Subsequently, based on Archie’s formula and the parallel conductivity model, porosity contribution corrections were applied to the pixel values to extract imaging mineral spectra reflecting pure mineral components. （2） The imaging mineral spectra were calibrated using whole-rock X-ray diffraction (XRD) analysis data from core samples. The optimal segmentation algorithm was employed to determine the optimal pixel value thresholds for distinguishing mud, feldspar, and calcite (calcite/felsic mineral boundary at 15%; felsic mineral/clay boundary at 70%), thereby establishing a quantitative calculation model from imaging mineral spectra to mineral content. （3） In practical single-well applications, the electrical imaging mineral calculation model significantly improved mineral identification accuracy, with correlation coefficient R2 between calculated mineral content and XRD measured data greater than 0.800 0, which can identify low physical property intervals caused by calcareous cementation, and is conducive to the detailed evaluation of complex clastic reservoirs.

Key words: clastic rock, electrical imaging logging, mineral component, lithology identification, conductivity, reservoir detailed evaluation, Huangliu Formation, Yinggehai Basin

中图分类号: 

  • TE122

图1

莺歌海盆地乐东地区新近系黄流组碳酸盐胶结物与物性的相关性"

图2

莺歌海盆地乐东地区新近系黄流组电成像像素点概率密度提取原理"

图3

莺歌海盆地乐东地区新近系黄流组电成像测井像素波形谱图"

图4

莺歌海盆地乐东地区新近系黄流组均衡化处理后的像素波形图"

图5

莺歌海盆地乐东地区新近系黄流组电成像测井计算孔隙度谱示意图"

图6

莺歌海盆地乐东地区新近系黄流组电成像测井图像矿物概率图谱求解流程图"

图7

基于最优分割算法实现矿物截止值确定流程图"

表1

莺歌海盆地乐东地区新近系黄流组成像矿物谱不同截止值组合误差对比"

灰质
截止值/%		 长英质
截止值/%		 灰质MAE/% 长英质MAE/% 泥质MAE/% 综合MAE/%
14 69 4.19 4.37 3.84 4.13
14 70 4.19 4.55 3.59 4.11
14 71 4.19 4.81 3.56 4.19
15 69 2.42 2.77 3.84 3.01
15 70 2.42 2.68 3.59 2.90
15 71 2.42 2.78 3.56 2.92
16 69 3.86 4.82 3.84 4.17
16 70 3.86 4.32 3.59 3.92
16 71 3.86 4.07 3.56 3.83

图8

莺歌海盆地乐东地区新近系黄流组最优分割算法的成像矿物谱截止值确定结果 注：w（矿物）计算为成像矿物模型计算的矿物成分质量分数，%；w（矿物）实测为XRD全岩分析实测的矿物成分质量分数，%。"

图9

电成像测井计算矿物组分流程示意图"

图10

莺歌海盆地乐东地区新近系黄流组电成像测井图像矿物组分计算值与实测值相关性"

图11

莺歌海盆地乐东地区新近系黄流组X-2井电成像测井图像矿物组分计算结果"

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