[1] 陈建阳,于兴河,张志杰,等. 储层地质建模在油藏描述中的应用[J]. 大庆石油地质与开发,2005,24（3）：17-18.

Chen Jianyang, Yu Xinghe, Zhang Zhijie, et al. Application of reservoir modeling in reservoir description of Baolige oil field[J]. Petroleum Geology & Oilfield Development in Daqing, 2005, 24(3): 17-18.
[2] 郭莉,王延斌,张春雷,姜福聪. 同位协同随机建模在储层预测中的应用[J].大庆石油地质与开发,2006,25（3）:5-6, 15.

Guo Li, Wang Yanbin, Zhang Chunlei, et al. Application of coordinate cooperation random modeling in reservoir prediction[J]. Petroleum Geology & Oilfield Development in Daqing, 2006, 25(3): 5-6, 15.
[3] 邓飞,王瑞,王美平,周熙襄. 复杂三维地层建模及快速射线追踪的研究与实现[J].大庆石油地质与开发, 2007,26（1）：113-118.

Deng Fei, Wang Rui, Wang Meiping, et al. Research and realization of complex three dimensional stratum modeling and fast ray tracing[J]. Petroleum Geology & Oilfield Development in Daqing, 2007, 26(1): 113-118.
[4] 付志国,石成方,赵翰卿,等. 喇萨杏油田河道砂岩厚油层夹层分布特征[J].大庆石油地质与开发, 2007,26（4）： 55-58.

Fu Zhiguo, Shi Chengfang, Zhao Hanqing, et al. The distribution characteristics of interlayer in thick channel sand oil reservoir in Lasaxing oilfield[J]. Petroleum Geology & Oilfield Development in Daqing, 2007, 26(4): 55-58.
[5] 舒志华,张立有,刘刚. 复合砂体中单一河道的识别方法[J].大庆石油地质与开发,2006,25（4）：18-20.

Shu Zhihua, Zhang Liyou, Liu Gang. Identification of single channel in compound sand body[J]. Petroleum Geology & Oilfield Development in Daqing, 2006, 25(4): 18-20.
[6] 赵翰卿. 高分辨率层序地层对比与我国的小层对比[J].大庆石油地质与开发,2005,24（1）： 5-9, 12.

Zhao Hanqing. High-resolution sequential stratigraphy correlation and Chinese subzone correlation[J]. Petroleum Geology & Oilfield Development in Daqing, 2005, 24(1): 5-9, 12.
[7] 何宇航,于开春. 分流平原相复合砂体单一河道识别及效果分析[J].大庆石油地质与开发,2005,24（2）：17-19,104.

He Yuhang, Yu Kaichun. Recognition and its effect analysis of single river channel in composite sand body with distributary plain facies[J]. Petroleum Geology & Oilfield Development in Daqing, 2005, 24(2): 17-19,104.
[8] 沈华,尹微,徐佑平. 提高砂岩油藏储层预测精度的方法[J].大庆石油地质与开发, 2005,24（3）： 24-27,104.

Shen Hua, Yin Wei, Xu Youping. Improving predicting accuracy of sandstone oil reservoirs[J]. Petroleum Geology & Oilfield Development in Daqing, 2005, 24(3): 24-27, 104.
[9] Deutsch C V, Wang L B. Hierarchical object-based stochastic modeling of fluvial reservoirs[J]. Mathematical Geology, 1996, 28(7): 857-880.
[10] Pyrcz M J, Boisvert J B, Deutsch C V. ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems[J]. Computers & Geosciences, 2009, 35(8): 1671-1685.
[11] 陈更新,赵凡,王建功,等. 分区域多点统计随机地质建模方法：以柴达木盆地辫状河三角洲沉积储集层为例[J]. 石油勘探与开发,2015,42（5）：638-645.

Chen Gengxin, Zhao Fan, Wang Jiangong, et al. Regionalized multiple-point stochastic geological modeling: A case from braided delta sedimentary reservoirs in Qaidam Basin, NW China[J]. Petroleum Exploration and Development, 2015, 42(5): 638-645.
[12] 廖春,屈信忠,赵英,等. 基于储层构型和流动单元的河流三角洲三维地质建模技术：以尕斯库勒油田为例[J]. 石油天然气学报,2014,36（6）：6-10.

Liao Chun, Qu Xinzhong, Zhao Ying, et al. River delta 3D geological modeling technology based on reservoir architecture and flow unit:By taking Gasikule oilfield for example[J]. Journal of Oil and Gas Technology, 2014, 36(6): 6-10.
[13] 吴胜和,李文克. 多点地质统计学：理论、应用与展望[J]. 古地理学报,2005,7（1）：137-144.

Wu Shenghe, Li Wenke. Multiple-point geostatistics: Theory, application and perspective[J]. Journal of Palaeogeography, 2005, 7(1): 137-144.
[14] 于兴河. 油气储层表征与随机建模的发展历程及展望[J]. 地学前缘,2008,15（1）：1-15.

Yu Xinghe. A review of development course and prospect of petroleum reservoir characterization and stochastic modeling[J]. Earth Science Frontiers, 2008, 15(1): 1-15.
[15] 尹艳树,王进,文志刚,等. 浅水三角洲分流河道三维储层建模方法比较[J]. 西南石油大学学报(自然科学版),2013,35（3）：47-51.

Yin Yanshu, Wang Jin, Wen Zhigang, et al. Comparison of the reservoir stochastic modeling methods for the underwater distributary channels in shallow water delta[J]. Journal of Southwest Petroleum University (Science & Technology Edition), 2013, 35(3): 47-51.
[16] 叶小明,霍春亮,王鹏飞,等. 基于目标的海上三角洲相油田地质建模[J]. 物探化探计算技术,2018,40（3）：404-410.

Ye Xiaoming, Huo Chunliang, Wang Pengfei, et al. Object-based stochastic modeling of delta reservoirs in offshore oilfield[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2018, 40(3): 404-410.
[17] 李宇鹏,吴胜和,耿丽慧,等. 基于空间矢量的点坝砂体储层构型建模[J]. 石油学报,2013,34（1）：133-139.

Li Yupeng, Wu Shenghe, Geng Lihui, et al. Spatial-vector-based reservoir architecture modeling of point-bar sand[J]. Acta Petrolei Sinica, 2013, 34(1): 133-139.
[18] 尹艳树,张昌民,李少华,等. 一种新的曲流河点坝侧积层建模方法[J]. 石油学报,2011,32（2）：315-319.

Yin Yanshu, Zhang Changmin, Li Shaohua, et al. A new stochastic modeling method for 3-D forecasting lateral accretion beddings of point bars in meandering rivers[J]. Acta Petrolei Sinica, 2011, 32(2): 315-319.
[19] 胡迅,尹艳树,冯文杰,等. 深水浊积水道训练图像建立与多点地质统计建模应用[J]. 石油与天然气地质,2019,40（5）：1126-1134.

Hu Xun, Yin Yanshu, Feng Wenjie, et al. Establishment of training images of turbidity channels in deep waters and application of multi-point geostatistical modeling[J]. Oil & Gas Geology, 2019, 40(5): 1126-1134.
[20] 李少华,刘显太,王军,等. 基于沉积过程建模算法Alluvsim的改进[J]. 石油学报,2013,34（1）：140-144.

Li Shaohua, Liu Xiantai, Wang Jun, et al. Improvement of the Alluvsim algorithm modeling based on depositional processes[J]. Acta Petrolei Sinica, 2013, 34(1): 140-144.
[21] 李少华,卢文涛. 基于沉积过程的储集层随机建模方法：以河流相储集层为例[J]. 古地理学报,2011,13（3）：325-333.

Li Shaohua, Lu Wentao. Depositional process-based reservoir stochastic modeling: A case of fluvial reservoir modeling[J]. Journal of Palaeogeography, 2011, 13(3): 325-333.
[22] 吴胜和,李宇鹏. 储层地质建模的现状与展望[J]. 海相油气地质,2007,12（3）：53-60.

Wu Shenghe, Li Yupeng. Reservoir modeling: Current situation and development prospect[J]. Marine Origin Petroleum Geology, 2007, 12(3): 53-60.
[23] Arpat G B, Caers J. A Multiple-scale, pattern-based approach to sequential simulation[G]//Geostatistics Banff 2004, Springer, 2005: 255-264.
[24] Honarkhah M, Caers J. Stochastic simulation of patterns using distance-based pattern modeling[J]. Mathematical Geosciences, 2010, 42(5): 487-517.
[25] Strebelle S B, Journel A G. Reservoir modeling using multiple-point statistics[C]//SPE annual technical conference and exhibition. New Orleans: SPE, 2001: 71324.
[26] 文子桃,林承焰,陈仕臻,等. 多点地质统计学建模参数敏感性分析[J]. 西安石油大学学报（自然科学版）,2017,32（1）：44-51.

Wen Zitao, Lin Chengyan, Chen Shizhen, et al. Sensitivity analyses of parameters for multiple-point geostatistics modeling[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2017, 32(1): 44-51.
[27] 王立鑫,尹艳树,冯文杰,等. 多点地质统计学中训练图像优选方法及其在地质建模中的应用[J]. 石油勘探与开发,2019,46（4）：703-709.

Wang Lixin, Yin Yanshu, Feng Wenjie, et al. A training image optimization method in multiple-point geostatistics and its application in geological modeling[J]. Petroleum Exploration and Development, 2019, 46(4): 703-709.
[28] 吴小军,李晓梅,谢丹,等. 多点地质统计学方法在冲积扇构型建模中的应用[J]. 岩性油气藏,2015,27（5）：87-91.

Wu Xiaojun, Li Xiaomei, Xie Dan, et al. Application of multiple-point geostatistics method to structure modeling of alluvial fan[J]. Lithologic Reservoirs, 2015, 27(5): 87-91.
[29] 尹艳树,吴胜和,张昌民,等. 基于储层骨架的多点地质统计学方法[J]. 中国科学（D辑）：地球科学,2008,38（增刊2）：157-164.

Yin Yanshu, Wu Shenghe, Zhang Changmin, et al. A reservoir skeleton-based multiple point geostatistics method[J]. Science China (Seri. D): Earth Sciences, 2008, 38(Suppl.2): 157-164.
[30] 郝慧珍,顾庆,胡修棉. 基于机器学习的矿物智能识别方法研究进展与展望[J]. 地球科学,2021,46（9）：3091-3106.

Hao Huizhen, Gu Qing, Hu Xiumian. Research advances and prospective in mineral intelligent identification based on machine learning[J]. Earth Science, 2021, 46(9): 3091-3106.
[31] 郭艳军,周哲,林贺洵,等. 基于深度学习的智能矿物识别方法研究[J]. 地学前缘,2020,27（5）：39-47.

Guo Yanjun, Zhou Zhe, Lin Hexun, et al. The mineral intelligence identification method based on deep learning algorithms[J]. Earth Science Frontiers, 2020, 27(5): 39-47.
[32] 刘彦锋,张文彪,段太忠,等. 深度学习油气藏地质建模研究进展[J]. 地质科技通报,2021,40（4）：235-241.

Liu Yanfeng, Zhang Wenbiao, Duan Taizhong, et al. Progress of deep learning in oil and gas reservoir geological modeling[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 235-241.
[33] 宋孝忠,张群. 煤岩显微组分组图像自动识别系统与关键技术[J]. 煤炭学报,2019,44（10）：3085-3097.

Song Xiaozhong, Zhang Qun. Automatic image recognition system and key technologies of maceral group[J]. Journal of China Coal Society, 2019, 44(10): 3085-3097.
[34] 李明超,刘承照,张野,等. 耦合颜色和纹理特征的矿物图像数据深度学习模型与智能识别方法[J]. 大地构造与成矿学,2020,44（2）：203-211.

Li Mingchao, Liu Chengzhao, Zhang Ye, et al. A deep learning and intelligent recognition method of image data for rock mineral and its implementation[J]. Geotectonica et Metallogenia, 2020, 44(2): 203-211.
[35] 戴乾军,余昌,马海林,等. 一种基于图像融合的测井电成像动态图像生成方法[J]. 电子测量技术,2021,44（1）：120-124.

Dai Qianjun, Yu Chang, Ma Hailin, et al. Dynamic image generating method of electric imaging logging based on image blending[J]. Electronic Measurement Technology, 2021, 44(1): 120-124.
[36] 庄培钦. 基于深度学习的细粒度图像识别方法研究[D].中国科学院大学(中国科学院深圳先进技术研究院),2019.
[37] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Proceedings of the 27th international conference on neural information processing systems. Montreal: MIT Press, 2014: 2672-2680.
[38] 张福临. 深度学习在岩石薄片图像生成中的研究与应用[D]. 西安：西安石油大学,2021.
[39] 李兴保. 基于生成对抗网络的数字岩心重构研究[D]. 合肥：合肥工业大学,2020.
[40] 张挺,王先武,杜奕,等. 基于DCGANs的二维页岩图像重构方法[J]. 上海电力大学学报,2021,37（4）：402-406.

Zhang Ting, Wang Xianwu, Du Yi, et al. 2D shale image reconstruction based on DCGANs[J]. Journal of Shanghai University of Electric Power, 2021, 37(4): 402-406.
[41] 陈龙. 基于生成对抗网络的多孔介质重构[D]. 西安：长安大学,2020.
[42] Dupont E, Zhang T, Tilke P, Liang L, Bailey W. Generating realistic geology conditioned on physical measurements with generative adversarial networks[EB/OL]. 2018. arXiv preprint arXiv:1802.03065.
[43] Laloy E, Hérault R, Jacques D, et al. Training-image based geostatistical inversion using a spatial generative adversarial neural network[J]. Water Resources Research, 2018, 54(1): 381-406.
[44] Zhang T F, Tilke P, Dupont E, et al. Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks[J]. Petroleum Science, 2019, 16(3): 541-549.
[45] Mosser L, Dubrule O, Blunt M J. Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models[J]. arXiv preprint arXiv:1802.05622, 2018.
[46] Song S H, Mukerji T, Hou J G. Geological facies modeling based on progressive growing of generative adversarial networks (GANs)[J]. Computational Geosciences, 2021, 25(3): 1251-1273.
[47] Chan S, Elsheikh A H. Parametrization of stochastic inputs using generative adversarial networks with application in geology[J]. Frontiers in Water, 2020, 2: 5.
[48] Mirza M, Osindero S. Conditional generative adversarial nets[J]. Computer Science, 2014: 2672-2680.
[49] Zhang L, Yang M, Feng X C. Sparse representation or collaborative representation: Which helps face recognition?[C]//2011 international conference on computer vision. Barcelona: IEEE, 2011. 471-478.
[50] Xu Z X, Yao M, Wu Z H, et al. Incremental regularized extreme learning machine and it’s enhancement[J]. Neurocomputing, 2016, 174: 134-142.
[51] 汪志远,降爱莲,奥斯曼·穆罕默德. 基于正则互表示的无监督特征选择方法[J]. 计算机应用,2020,40（7）：1896-1900.

Wang Zhiyuan, Jiang Ailian, Muhammad O. Unsupervised feature selection method based on regularized mutual representation[J]. Journal of Computer Applications, 2020, 40(7): 1896-1900.
[52] Nikhil K. Deep learning with Python[M]. New York: Springer, 2017: 119-117.
[53] Isola P, Zhu J Y, Zhou T H, et al. Image-to-image translation with conditional adversarial networks[C]//2017 IEEE conference on computer vision and pattern recognition. Honolulu: IEEE, 2017: 5967-5976.
[54] 林志鹏,单敬福,陈乐,等. 苏里格气田苏6区块盒8段古河道砂体演化规律[J]. 油气地质与采收率,2018,25（5）：1-9.

Lin Zhipeng, Shan Jingfu, Chen Le, et al. Evolutionary process of palaeochannel sandbodies of the 8th member of Shihezi Formation in block Su6, Sulige gasfield[J]. Petroleum Geology and Recovery Efficiency, 2018, 25(5): 1-9.
[55] 徐文,段志强,赵忠军,等. 鄂尔多斯盆地苏里格气田苏6区块盒8段密集井网区古河道砂体定量化表征[J]. 中国科技论文,2020,15（1）：50-59.

Xu Wen, Duan Zhiqiang, Zhao Zhongjun, et al. Quantitative characterization of paleochannel sandbody evolution in dense well net area of He 8th layer of Su 6 block in the Sulige gasfield, Ordos Basin[J]. China Sciencepaper, 2020, 15(1): 50-59.
[56] 王涛,侯明才,王文楷,等. 苏里格气田召30井区盒8段层序格架内砂体构型分析[J]. 天然气工业,2014,34（7）：27-33.

Wang Tao, Hou Mingcai, Wang Wenkai, et al. Sand body configuration of sequence stratigraphic framework of the 8th member of the Permian Lower Shihezi Fm in Zhao 30 wellblock, eastern Sulige gas field, Ordos Basin[J]. Natural Gas Industry, 2014, 34(7): 27-33.
[57] 谢庆宾,孙建,陈菁萍,等. 苏里格大气田多成因河道砂体的分布模式研究[J]. 地学前缘,2013,20（2）：40-51.

Xie Qingbin, Sun Jian, Chen Jingping, et al. Model of the distribution of the ploygenetic channel sand body of Sulige large gas field[J]. Earth Science Frontiers, 2013, 20(2): 40-51.
[58] 白振华,詹燕涛,王赢,等. 苏里格气田苏14井区盒8段河流相砂体展布与演化规律研究[J]. 岩性油气藏,2013,25（1）：56-62.

Bai Zhenhua, Zhan Yantao, Wang Ying, et al. Fluvial sand bodies distribution and evolution of He 8 member in Su 14 block of Sulige gas field[J]. Lithologic Reservoirs, 2013, 25(1): 56-62.