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孫文躍
作者: 發布者:於夢飛 發布時間:2023-02-16 訪問次數:4917

職稱:教授

單位:智能油氣田研究所

最高學曆/學位:博士

學科:海洋油氣工程學科,石油與天然氣工程領域

所學專業:能源與資源工程

電子郵箱:wenyue@upc.edu.cn

聯係電話:

地址郵編:山東省青島市經濟技術開發區長江西路66號,266580

  • 個人主頁
  • 學習與工作經曆
    學習經曆:
    - 2014-2018,斯坦福大學,能源與資源工程,博士
    - 2012-2014,斯坦福大學,石油工程,碩士
    - 2008-2012,北京大學,力學,學士


    工作經曆:
    - 2022.12-至今,中國石油大學(華東),必威app精裝版客服 ,教授,博導
    - 2020.02-2022.09,Xecta Digital Labs. (休斯頓),高級研發工程師
    - 2018.07-2020.02,Anadarko國際石油公司(休斯頓),油藏工程師
    - 2015-2017(暑期),雪佛龍石油公司、殼牌石油公司(休斯頓),科研實習生

  • 研究方向
    - 油藏曆史擬合、生產優化及不確定性分析;
    - 智能油氣田開發理論與技術;
    - 深度學習在油藏模擬中的應用;
    - 基於最新技術架構的石油工程領域專業軟件研發
  • 招生方向
  • 主講課程
  • 學術兼職
    - 2022-2023,Technical committee for International Petroleum Technology Conference (IPTC), Bangkok, Thailand
    - SPE J.,SPE Reservoir Eval. Eng., Comput. Geosci., JCP等期刊審稿人
  • 指導研究生
  • 承擔科研課題
    海外引進高層次人才項目,2022-2027,負責人
  • 獲獎情況
    - SPE Journal Outstanding Technical Editor Award, 2018
    - Stanford University Henry J. Ramey, Jr. Fellowship Award, 2017
    - 1st place, SPE International Paper Contest, PhD Section, 2017
  • 榮譽稱號
    中國石油大學(華東)光華學者
  • 著作
  • 論文
    - Guo, Z., Sun, W. & Sankaran, S. (2023). Reservoir Modeling, History Matching, and Characterization with a Reservoir Graph Network Model. SPE Reservoir Evaluation & Engineering, 1-13.
    - Guo, Z., Sun, W. & Sankaran, S. (2022). Efficient Reservoir Management with a Reservoir Graph Network Model. In SPE Western Regional Meeting.
    - Nagao, M., Sun, W. & Sankaran, S. (2022). Data-Driven Discovery of Physics for Reservoir Surveillance. In SPE Western Regional Meeting.
    - Sankaran, S., Molinari, D., Zalavadia, H., Stoddard, T., Sun, W., Singh, G. & James, C. (2022) Unlocking Unconventional Production Optimization Opportunities Using Reduced Physics Models for Well Performance Analysis–Case Study. In International Petroleum Technology Conference.
    - Sun, W., & Sankaran, S. (2021). A Graph Network Based Approach for Reservoir Modeling. In SPE Annual Technical Conference and Exhibition.
    - Sankaran, S. & Sun, W. (2020). A Flow Network Model Based on Time of Flight for Reservoir Management. In Abu Dhabi International Petroleum Exhibition & Conference.
    - Jiang, S., Sun, W. & Durlofsky, L.J. (2020). A Data-Space Inversion Procedure for Well Control Optimization and Closed-Loop Reservoir Management. Computational Geosciences 24, 361-379.
    - Zalavadia, H., Sankaran, S., Kara, M., Sun, W. & Gildin, E.. (2019) A Hybrid Modeling Approach to Production Control Optimization Using Dynamic Mode Decomposition. In SPE Annual Technical Conference and Exhibition.
    - Liu, Y., Sun, W., & Durlofsky, L. J. (2019). A Deep-Learning-Based Geological Parameterization for History Matching Complex Models. Mathematical Geosciences, 51, 725-766.
    - He, J., Sun, W., & Wen, X. H. (2019). Rapid Forecast Calibration Using Nonlinear Simulation Regression with Localization. In SPE Reservoir Simulation Conference.
    - Sun, W., & Durlofsky, L. J. (2019). Data-Space Approaches for Uncertainty Quantification of CO2 Plume Location in Geological Carbon Storage. Advances in Water Resources, 123, 234-255.
    - Jiang, S., Sun, W., & Durlofsky, L. J. (2018). A Data-Space Approach for Well Control Optimization under Uncertainty. In ECMOR XVI-16th European Conferences on the Mathematics of Oil Recovery.
    - Sun, W., Hui, M. H., & Durlofsky, L. J. (2017). Production forecasting and uncertainty quantification for naturally fractured reservoirs using a new data-space inversion procedure. Computational Geosciences, 21(5-6), 1443-1458.
    - Sun, W., & Durlofsky, L. J. (2017). A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems. Mathematical Geosciences, 49(6), 679-715.
    - Sun, W., Vink, J. C. & Gao, G. (2017). A Practical Method to Mitigate Spurious Uncertainty Reduction in History Matching Workflows with Imperfect Reservoir Models. In SPE Reservoir Simulation Conference.
  • 專利
    - Reduced physics well production monitoring. US Patent 11,514,216.
    - Graph network fluid flow monitoring, US Patent 11,501,043.
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