石油勘探与开发 >

2022 , Vol. 49 >Issue 2: 296 - 305

DOI: https://doi.org/10.11698/PED.2022.02.08

油气勘探

核磁共振横向弛豫时间谱高斯混合聚类及应用

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  • 1.中国石油大学（华东）地球科学与技术学院,山东青岛266580;
    2.中国石油天然气集团有限公司测井重点实验室,西安710077;
    3.海洋国家实验室海洋矿产资源评价与探测功能实验室,山东青岛266071;
    4.中国地质调查局油气资源调查中心,北京100083;
    5.中国石油集团测井有限公司,西安710077;
    6.中国石油勘探开发研究院,北京100083;
    7.中国石油青海油田公司,甘肃敦煌736202;
    8.中海石油（中国）有限公司湛江分公司,广东湛江524057;
    9.中国石化石油工程技术研究院,北京100101
葛新民（1985-）,男,江西于都人,博士,中国石油大学（华东）副教授,主要从事测井岩石物理、核磁共振测井和复杂油气层测井评价方法等研究。地址：山东省青岛市黄岛区长江西路66号,中国石油大学（华东）工科楼C534室,邮政编码：266580。E-mail: gexinmin2002@163.com

收稿日期: 2021-08-30

  网络出版日期: 2022-03-16

基金资助

国家自然科学基金（42174142）; 国家科技重大专项（2017ZX05039-002）; 中国石油天然气集团有限公司测井重点实验室运行基金（2021DQ20210107-11）; 中央高校基本科研业务费专项（19CX02006A）; 中国石油天然气集团有限公司重大科技项目（ZD2019-183-006）

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An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

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  • 1. School of Geosciences, China University of Petroleum, Qingdao 266580, China;
    2. CNPC Key Well Logging Laboratory, Xi’an 710077, China;
    3. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China;
    4. Oil and Gas Survey Center of China Geological Survey, Beijing 100083, China;
    5. China Petroleum Logging Co. Ltd., Xi’an 710077, China;
    6. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China;
    7. PetroChina Qinghai Oilfield Company, Dunhuang 736202, China;
    8. Zhanjiang Branch of CNOOC Ltd., Zhanjiang 524057, China;
    9. Sinopec Research Institute of Petroleum Engineering, Beijing 100101, China

Received date: 2021-08-30

  Online published: 2022-03-16

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摘要

为使核磁共振测井横向弛豫时间（T2）谱的定量表征结果更为直观地反映储集层类型和孔隙结构,提出基于高斯混合模型（GMM）的T2谱无监督聚类和孔隙结构定量识别方法。首先对T2谱数据进行主成分降维,减弱数据间的相关性;其次采用高斯混合模型概率密度函数对降维数据进行拟合,结合期望值最大化算法和赤池信息准则变化率得到模型参数和最佳聚类群集;最后分析不同聚类群集的T2谱特征、孔隙结构类型等,并与T2几何平均值、T2算术平均值等进行对比,通过数值模拟和核磁共振测井资料验证算法有效性。研究表明,基于GMM方法的聚类结果与T2谱形态、T2谱、孔隙结构、油气产能等具有很好的对应性,为孔隙结构定量识别、储集层级别划分和产能评价等提供新的手段。

关键词： 核磁共振T2谱; 高斯混合模型; 期望最大化算法; 赤池信息准则; 无监督聚类; 孔隙结构定量标准

本文引用格式

葛新民, 薛宗安, 周军, 胡法龙, 李江涛, 张恒荣, 王烁龙, 牛深园, 赵吉儿 . 核磁共振横向弛豫时间谱高斯混合聚类及应用[J]. 石油勘探与开发, 2022 , 49(2) : 296 -305 . DOI: 10.11698/PED.2022.02.08

Abstract

To make the quantitative results of nuclear magnetic resonance (NMR) transversal relaxation (T2) spectrums reflect is proposed the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T2 spectrums based on the Gaussian mixture model (GMM). We conducted the principal component analysis on T2 spectrums in order to reduce the dimension data and the dependence of the original variables. The dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T2 spectrum features and pore structure types of different clustering groups were analyzed and compared with T2 geometric mean and T2 arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T2 spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

Key words： NMR T2 spectrum; Gaussian mixture model; expectation-maximization algorithm; Akaike information criterion; unsupervised clustering method; quantitative pore structure evaluation

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