职称：教授
单位：智能油气田研究所
最高学历/学位：博士
学科：海洋油气工程学科,石油与天然气工程领域
所学专业：能源与资源工程
电子邮箱：wenyue@upc.edu.cn
联系电话：
地址邮编：山东省青岛市经济技术开发区长江西路66号，266580
孙文跃
作者：
发布者：于梦飞
发布时间：2023-02-16
访问次数：4874
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个人主页
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学习与工作经历
学习经历：
- 2014-2018，斯坦福大学，能源与资源工程，博士
- 2012-2014，斯坦福大学，石油工程，硕士
- 2008-2012，北京大学，力学，学士工作经历：
- 2022.12-至今，中国石油大学（华东），石油工程学院，教授，博导
- 2020.02-2022.09，Xecta Digital Labs. (休斯顿)，高级研发工程师
- 2018.07-2020.02，Anadarko国际石油公司（休斯顿），油藏工程师
- 2015-2017（暑期），雪佛龙石油公司、壳牌石油公司（休斯顿），科研实习生 -
研究方向
- 油藏历史拟合、生产优化及不确定性分析；
- 智能油气田开发理论与技术；
- 深度学习在油藏模拟中的应用；
- 基于最新技术架构的石油工程领域专业软件研发
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招生方向
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主讲课程
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学术兼职
- 2022-2023，Technical committee for International Petroleum Technology Conference (IPTC), Bangkok, Thailand
- SPE J.，SPE Reservoir Eval. Eng., Comput. Geosci., JCP等期刊审稿人
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指导研究生
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承担科研课题
海外引进高层次人才项目，2022-2027，负责人
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获奖情况
- 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
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荣誉称号
中国石油大学（华东）光华学者
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著作
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论文
- 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.
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专利
- Reduced physics well production monitoring. US Patent 11,514,216.
- Graph network fluid flow monitoring, US Patent 11,501,043.
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学术交流
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个人风采