[1] 肖立志. 核磁共振成像测井与岩石核磁共振及其应用[M]. 北京: 科学出版社, 1998.
XIAO Lizhi. Nuclear magnetic resonance imaging logging and rock nuclear magnetic resonance and its application[M]. Beijing: Science Press, 1998.
[2] 肖立志, 谢然红, 廖广志. 中国复杂油气藏核磁共振测井理论与方法[M]. 北京: 科学出版社, 2012.
XIAO Lizhi, XIE Ranhong, LIAO Guangzhi. Theory and method of NMR logging for Chinese complex oil and gas reservoirs[M]. Beijing: Science Press, 2012.
[3] DUNN K J, BERGMAN D J, LATORRACA G A. Nuclear magnetic resonance petrophysical and logging applications[M]. New York: Elsevier, 2002.
[4] 胡法龙, 周灿灿, 李潮流, 等. 基于弛豫-扩散的二维核磁共振流体识别方法[J]. 石油勘探与开发, 2012, 39(5): 552-558.
HU Falong, ZHOU Cancan, LI Chaoliu, et al. Fluid identification method based on 2D diffusion-relaxation nuclear magnetic resonance (NMR)[J]. Petroleum Exploration and Development, 2012, 39(5): 552-558.
[5] MUTINA A, BACHMAN N, RENDON L. Fast multidimensional NMR logging provides advanced fluid characterization at a step change in logging speed[R]. London, UK: SPWLA 59th Annual Logging Symposium, 2018.
[6] SUN B Q. In situ fluid typing and quantification with 1D and 2D NMR logging[J]. Magnetic Resonance Imaging, 2007, 25(4): 521-524.
[7] KAUSIK R, JIANG T M, VENKATARAMANAN L, et al. Reservoir producibility index (RPI) based on 2D T1-T2 NMR logs[R]. Texas, USA: SPWLA 60th Annual Logging Symposium, 2019.
[8] ZHANG H, WANG Y, FANG T, et al. First application of new generation NMR T1-T2 logging and interpretation in unconventional reservoirs in China[R]. SPE 202261-MS, 2020.
[9] 范宜仁, 刘建宇, 葛新民, 等. 基于核磁共振双截止值的致密砂岩渗透率评价新方法[J]. 地球物理学报, 2018, 61(4): 1628-1638.
FAN Yiren, LIU Jianyu, GE Xinmin, et al. Permeability evaluation of tight sandstone based on dual T2 cutoff values measured by NMR[J]. Chinese Journal of Geophysics, 2018, 61(4): 1628-1638.
[10] 李鹏举, 谷宇峰. 核磁共振T2谱转换伪毛管压力曲线的矩阵方法[J]. 天然气地球科学, 2015, 26(4): 700-705.
LI Pengju, GU Yufeng. Matrix method of transforming NMR T2 spectrum to pseudo capillary pressure curve[J]. Natural Gas Geoscience, 2015, 26(4): 700-705.
[11] 闫建平, 温丹妮, 李尊芝, 等. 基于核磁共振测井的低渗透砂岩孔隙结构定量评价方法: 以东营凹陷南斜坡沙四段为例[J]. 地球物理学报, 2016, 59(4): 1543-1552.
YAN Jianping, WEN Danni, LI Zunzhi, et al. The quantitative evaluation method of low permeable sandstone pore structure based on nuclear magnetic resonance (NMR) logging[J]. Chinese Journal of Geophysics, 2016, 59(4): 1543-1552.
[12] GE X M, FAN Y R, ZHU X J, et al. Determination of nuclear magnetic resonance T2 cutoff value based on multifractal theory: An application in sandstone with complex pore structure[J]. Geophysics, 2015, 80(1): 11-21.
[13] 丁娱娇, 柴细元, 邵维志, 等. 基于核磁共振T2谱集中度的低孔隙度低渗透率储层饱和度参数研究[J]. 测井技术, 2017, 41(4): 405-411.
DING Yujiao, CHAI Xiyuan, SHAO Weizhi, et al. Key parameters of water saturation based on concentration of T2 spectrum distribution[J]. Well Logging Technology, 2017, 41(4): 405-411.
[14] 白松涛, 程道解, 万金彬, 等. 砂岩岩石核磁共振T2谱定量表征[J]. 石油学报, 2016, 37(3): 382-391.
BAI Songtao, CHENG Daojie, WAN Jinbin, et al. Quantitative characterization of sandstone NMR T2 spectrum[J]. Acta Petrolei Sinica, 2016, 37(3): 382-391.
[15] 张超谟, 陈振标, 张占松, 等. 基于核磁共振T2谱分布的储层岩石孔隙分形结构研究[J]. 石油天然气学报, 2007, 29(4): 80-86.
ZHANG Chaomo, CHEN Zhenbiao, ZHANG Zhansong, et al. Fractal characteristics of reservoir rock pore structure based on NMR T2 distribution[J]. Journal of Oil and Gas Technology, 2007, 29(4): 80-86.
[16] GE X M, FAN Y R, CAO Y C, et al. Reservoir pore structure classification technology of carbonate rock based on NMR T2 spectrum decomposition[J]. Applied Magnetic Resonance, 2014, 45(2): 155-167.
[17] 钟吉彬, 阎荣辉, 张海涛, 等. 核磁共振横向弛豫时间谱分解法识别流体性质[J]. 石油勘探与开发, 2020, 47(4): 691-702.
ZHONG Jibin, YAN Ronghui, ZHANG Haitao, et al. A decomposition method of nuclear magnetic resonance T2 spectrum for identifying fluid properties[J]. Petroleum Exploration and Development, 2020, 47(4): 691-702.
[18] BAUER K, KULENKAMPFF J, HENNINGES J, et al. Lithological control on gas hydrate saturation as revealed by signal classification of NMR logging data[J]. Journal of Geophysical Research. Solid Earth, 2015, 120(9): 6001-6017.
[19] ANAND V. Novel methodology for accurate resolution of fluid signatures from multi-dimensional NMR well-logging measurements[J]. Journal of Magnetic Resonance, 2017, 276: 60-68.
[20] ROJAS P A, RINCÓN M, NETTO P, et al. Application of machine learning tool to separate overlapping fluid components on NMR T2 distributions: Case studies from laboratory displacement experiment and well logs[R]. SPE 197684-MS, 2019.
[21] XU R, DENG T Q, JIANG J J, et al. Integration of NMR and conventional logs for vuggy facies classification in the Arbuckle Formation: A machine learning approach[J]. SPE Reservoir Evaluation & Engineering, 2020, 23(3): 917-929.
[22] 孟昆, 王胜建, 薛宗安, 等. 利用核磁共振资料定量评价页岩孔隙结构[J]. 波谱学杂志, 2021, 38(2): 215-226.
MENG Kun, WANG Shengjian, XUE Zong’an, et al. Quantitative evaluation of shale pore structure using nuclear magnetic resonance data[J]. Chinese Journal of Magnetic Resonance, 2021, 38(2): 215-226.
[23] 闫伟林, 张兆谦, 陈龙川, 等. 基于核磁共振技术的古龙页岩含油饱和度评价新方法[J]. 大庆石油地质与开发, 2021, 40(5): 78-86.
YAN Weilin, ZHANG Zhaoqian, CHEN Longchuan, et al. New evaluating method of oil saturation in Gulong shale based on NMR technique[J]. Petroleum Geology & Oilfield Development in Daqing, 2021, 40(5): 78-86.
[24] 陈惠, 冯春珍, 赵建鹏, 等. 基于分形与核磁共振测井的储层孔隙结构表征与分类[J]. 测井技术, 2021, 45(1): 50-55.
CHEN Hui, FENG Chunzhen, ZHAO Jianpeng, et al. Pore structure characterization and classification based on fractal theory and nuclear magnetic resonance logging[J]. Well Logging Technology, 2021, 45(1): 50-55.
[25] 高新, 靳国旺, 熊新, 等. 融合差异图与高斯混合模型相结合的SAR图像变化检测[J]. 测绘科学技术学报, 2020, 37(1): 68-73.
GAO Xin, JIN Guowang, XIONG Xin, et al. Change detection in synthetic aperture radar images based on image fusion and Gaussian mixture model[J]. Journal of Geomatics Science and Technology, 2020, 37(1): 68-73.
[26] 戚贵玲, 何冰冰, 张榆锋, 等. 基于高斯混合模型聚类的B超图像颈动脉内膜和中膜厚度检测[J]. 生物医学工程学杂志, 2020, 37(6): 1080-1088.
QI Guiling, HE Bingbing, ZHANG Yufeng, et al. Detection of carotid intima and media thicknesses based on ultrasound B-mode images clustered with Gaussian mixture model[J]. Journal of Biomedical Engineering, 2020, 37(6): 1080-1088.
[27] 张美霞, 李丽, 杨秀, 等. 基于高斯混合模型聚类和多维尺度分析的负荷分类方法[J]. 电网技术, 2020, 44(11): 4283-4293.
ZHANG Meixia, LI Li, YANG Xiu, et al. A load classification method based on Gaussian mixture model clustering and multi-dimensional scaling analysis[J]. Power System Technology, 2020, 44(11): 4283-4293.
[28] 兰志刚, 靳卫卫, 朱明亮, 等. 基于高斯混合模型的海冰图像非监督聚类分割研究[J]. 海洋科学, 2011, 35(11): 97-100.
LAN Zhigang, JIN Weiwei, ZHU Mingliang, et al. Sea ice image segmentation with unsupervised clustering based on the Gaussian mixture model[J]. Marine Sciences, 2011, 35(11): 97-100.
[29] 张宝一, 陆浩, 杨莉, 等. 顾及梯度的高斯混合模型在三维属性场空间聚类中的应用[J]. 地质找矿论丛, 2019, 34(3): 460-470.
ZHANG Baoyi, LU Hao, YANG Li, et al. Application of the gradient-based Gaussian mixture model to spatial clustering of three-dimensional attribute field[J]. Contributions to Geology and Mineral Resources Research, 2019, 34(3): 460-470.
[30] 王亚利, 都伟冰, 王双亭. 高斯混合模型自动阈值法遥感冰川信息提取[J]. 遥感学报, 2021, 25(7): 1434-1444.
WANG Yali, DU Weibing, WANG Shuangting. Extracting glacier information from remote sensing imageries by automatic threshold method of Gaussian mixture model[J]. Journal of Remote Sensing, 2021, 25(7): 1434-1444.
[31] YANG M S, LAI C Y, LIN C Y. A robust EM clustering algorithm for Gaussian mixture models[J]. Pattern Recognition, 2012, 45(11): 3950-3961.
[32] NOWAKOWSKA E, KORONACKI J, LIPOVETSKY S. Clusterability assessment for Gaussian mixture models[J]. Applied Mathematics and Computation, 2015, 256: 591-601.
[33] 王一妹, 刘辉, 宋鹏, 等. 基于高斯混合模型聚类的风电场短期功率预测方法[J]. 电力系统自动化, 2021, 45(7): 37-43.
WANG Yimei, LIU Hui, SONG Peng, et al. Short-term power forecasting method of wind farm based on Gaussian mixture model clustering[J]. Automation of Electric Power Systems, 2021, 45(7): 37-43.
[34] 柴秀俊, 王宏伟, 王林, 等. 基于高斯混合聚类的切换系统的辨识[J]. 控制理论与应用, 2021, 38(5): 634-640.
CHAI Xiujun, WANG Hongwei, WANG Lin, et al. Identification of switched systems based on Gaussian mixture clustering[J]. Control Theory & Applications, 2021, 38(5): 634-640.
[35] FRIEL N, MCKEONE J P, OATES C J, et al. Investigation of the widely applicable Bayesian information criterion[J]. Statistics and Computing, 2017, 27(3): 833-844.
[36] 张发才, 李喜旺, 樊国旗. 基于高斯混合聚类的风电出力场景划分[J]. 计算机系统应用, 2021, 30(1): 146-153.
ZHANG Facai, LI Xiwang, FAN Guoqi. Wind power output scene division based on Gaussian hybrid clustering[J]. Computer Systems & Applications, 2021, 30(1): 146-153.
[37] GE X M, WANG H, FAN Y R, et al. Joint inversion of T1-T2 spectrum combining the iterative truncated singular value decomposition and the parallel particle swarm optimization algorithms[J]. Computer Physics Communications, 2016, 198: 59-70.
[38] 周海涛, 王志刚, 刘昌明. 基于主成分分析和高斯混合模型的耐火材料损伤信号分类[J]. 武汉科技大学学报, 2014, 37(4): 269-272.
ZHOU Haitao, WANG Zhigang, LIU Changming. Classification of refractory damage signals based on the principal component analysis and Gaussian mixture model[J]. Journal of Wuhan University of Science and Technology, 2014, 37(4): 269-272.
[39] 申波, 毛志强, 樊海涛, 等. 基于主成分分析技术计算蚀变地层孔隙度的新方法[J]. 测井技术, 2012, 36(2): 130-134.
SHEN Bo, MAO Zhiqiang, FAN Haitao, et al. A new porosity calculation method based on principal component analytical technology for altered formation[J]. Well Logging Technology, 2012, 36(2): 130-134.
[40] 钟仪华, 李榕, 张志银, 等. 基于主成分分析的离散过程神经网络水淹层动态预测方法[J]. 测井技术, 2010, 34(5): 432-436.
ZHONG Yihua, LI Rong, ZHANG Zhiyin, et al. Dynamic recognition method for water-flooded layer with discrete process neural network based on the principal component analysis[J]. Well Logging Technology, 2010, 34(5): 432-436.