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。
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.
作者
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
GE Xinmin;XUE Zong’an;ZHOU Jun;HU Falong;LI Jiangtao;ZHANG Hengrong;WANG Shuolong;NIU Shenyuan;ZHAO Ji’er(School of Geosciences,China University of Petroleum,Qingdao 266580,China;CNPC Key Well Logging Laboratory,Xi’an 710077,China;Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266071,China;Oil and Gas Survey Center of China Geological Survey,Beijing 100083,China;China Petroleum Logging Co.Ltd.,Xi’an 710077,China;PetroChina Research Institute of Petroleum Exploration&Development,Beijing 100083,China;PetroChina Qinghai Oilfield Company,Dunhuang 736202,China;Zhanjiang Branch of CNOOC Ltd.,Zhanjiang 524057,China;Sinopec Research Institute of Petroleum Engineering,Beijing 100101,China)
出处
《石油勘探与开发》
SCIE
EI
CAS
CSCD
北大核心
2022年第2期296-305,共10页
Petroleum Exploration and Development
基金
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)。
关键词
核磁共振T_(2)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
NMR T2 spectrum
Gaussian mixture model
expectation-maximization algorithm
Akaike information criterion
unsupervised clustering method
quantitative pore structure evaluation
