油气勘探

基于孔隙结构参数的相控渗透率地震预测方法

  • 甘利灯 ,
  • 王峣钧 ,
  • 罗贤哲 ,
  • 张明 ,
  • 李贤斌 ,
  • 戴晓峰 ,
  • 杨昊
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  • 1. 中国石油勘探开发研究院,北京 100083;
    2. 电子科技大学,成都 611731
甘利灯(1964-),男,福建闽侯人,博士,中国石油勘探开发研究院教授级高级工程师,主要从事地震资料解释和油藏地球物理技术研究与应用。地址:北京市海淀区学院路20号,中国石油勘探开发研究院油气地球物理研究所,邮政编码:100083。E-mail:gld@petrochina.com.cn

收稿日期: 2019-05-18

  修回日期: 2019-07-12

  网络出版日期: 2019-09-17

A permeability prediction method based on pore structure and lithofacies

  • GAN Lideng ,
  • WANG Yaojun ,
  • LUO Xianzhe ,
  • ZHANG Ming ,
  • LI Xianbin ,
  • DAI Xiaofeng ,
  • YANG Hao
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  • 1. Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China;
    2. University of Electronic Science and Technology of China, Chengdu 611731, China

Received date: 2019-05-18

  Revised date: 2019-07-12

  Online published: 2019-09-17

Supported by

国家自然科学基金青年基金项目(41804126); 中国石油天然气集团有限公司科学研究与技术开发项目(2017D-3503,2018D-4407)

摘要

针对在储集层孔隙结构复杂、岩相分布非均质性较大情况下采用孔隙度直接线性回归法预测渗透率存在适用性差的问题,提出综合考虑储集层孔隙结构、孔隙度和岩相等因素进行复杂储集层渗透率预测的方法。首先采用储集层段孔隙度、弹性参数及剪切骨架柔度因子进行岩相分析,然后在每类岩相中采用弹性参数、孔隙度及剪切骨架柔度因子进行多元回归得到预测渗透率。通过研究区测井资料渗透率预测实验表明,刻画孔隙结构的剪切柔度因子对渗透率的影响比常规弹性参数敏感,可以更好地用于渗透率预测;岩相分类精度会对渗透率预测产生较大影响,精确地岩相分类仍然是渗透率预测前提条件。实际研究区应用效果验证了基于孔隙结构参数的相控渗透率地震预测方法准确有效,为渗透率预测提供了一种有效方法。图13表2参14

本文引用格式

甘利灯 , 王峣钧 , 罗贤哲 , 张明 , 李贤斌 , 戴晓峰 , 杨昊 . 基于孔隙结构参数的相控渗透率地震预测方法[J]. 石油勘探与开发, 2019 , 46(5) : 883 -890 . DOI: 10.11698/PED.2019.05.07

Abstract

Permeability prediction using linear regression of porosity does not work well for the reservoir with complex pore structure and large variation of lithofacies. A new method is proposed to predict permeability by comprehensively considering pore structure, porosity and lithofacies. In this method, firstly, the lithofacies classification is carried out using the elastic parameters, porosity and shear frame flexibility factor. Then, for each lithofacies, the elastic parameters, porosity and shear frame flexibility factor are used to obtain permeability from regression. The permeability prediction test by logging data of the study area shows that the shear frame flexibility factor that characterizes the pore structure is more sensitive to permeability than the conventional elastic parameters, so it can predict permeability more accurately. In addition, the accuracy of lithofacies classification has great influence on the permeability prediction, so accurate lithofacies classification is still the precondition of permeability prediction. The field data application verifies that the proposed permeability prediction method based on pore structure parameters and lithofacies is accurate and effective. This method provides an effective mean for permeability prediction.

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