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师资步队

师资步队

数学系

姓名:董少群【副教授】

董少群.jpg

小我私家基本信息:

董少群 ,男 ,博士(后) ,副教授 ,硕士生导师 ,博士生导师 。主要从事油气人工智能、应用统计、智能油藏形貌研究 。揭晓论文70余篇 ,其中第一或通讯作者32篇;以第一发明人申请国家发明专利14项 ,其中6项已授权;第一作者挂号盘算机软件著作权12项;相助出书专著、课本2部 。任《Petroleum Science》 ,《International Journal of Coal Science & Technology》、《石油科学转达》、《东北石油大学学报》、《西安石油大学学报》等多个期刊青年编委 ,兼任AAPG、JGR: Solid Earth、《石油勘探与开发》等30余个期刊审稿人 。主持国家自然科学基金青年科学基金项目1项、中国博士后科学基金特殊资助项目1项、省部级揭榜挂帅项目1项、中石油及中石化科研项目5项 ,研究效果获教育部科技前进奖等省部级一等奖2项、省部级二等奖1项 。


事情履历:

(1)2022.07-至今 ,凯时(北京) ,理学院 ,副教授 。

(2)2019.09-2022.07 ,凯时(北京) ,理学院 ,讲师 。

  (3)  2017-2018 ,澳大利亚阿德莱德大学 ,会见交流 。


10篇代表论文:

[1] Semi-supervised neural network for complex lithofacies identification using well logs [J]. IEEE Transactions on Geoscience and Remote Sensing. 2024, 任命待刊.

[2] A deep learning object detection method for fracture identification using conventional well logs [J]. IEEE Transactions on Geoscience and Remote Sensing. 2024,62:5920716.

[3] Fracture identification in shale reservoir using a deep learning method: Chang 7 reservoirs, Triassic Yanchang formation [J]. Geoenergy Science and Engineering. 2024,238:212853.

[4] Fracture identification in reservoirs using well log data by window sliding recurrent neural network [J]. Geoenergy Science and Engineering. 2023, 230: 212165.

[5] How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles [J]. Petroleum Science. 2023, 20(2): 733-752.

[6] A deep kernel method for lithofacies identification using conventional well logs [J]. Petroleum Science. 2023, 20(3): 1411-1428.

[7] 致密碳酸盐岩储集层裂痕智能展望要领[J]. 石油勘探与开发. 2022, 49(6): 1179-1189.
     An intelligent prediction method of fractures in tight carbonate reservoirs. Petroleum Exploration and Development. 2022, 49(6): 1364-1376.(双语刊发)

[8] Lithofacies identification in carbonate reservoirs by multiple kernel Fisher discriminant analysis using conventional well logs: A case study in A oilfield, Zagros Basin, Iraq[J]. Journal of Petroleum Science and Engineering. 2022, 210: 110081.

[9] Fracture identification by semi-supervised learning using conventional logs in tight sandstones of Ordos Basin, China[J]. Journal of Natural Gas Science and Engineering. 2020, 76: 103131.

[10] A fast method for fracture intersection detection in discrete fracture networks [J]. Computers and Geotechnics. 2018, 98: 205-216.


授课信息:

(1)油气人工智能基础及应用

(2)随机模拟

(3)概率统计基础

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