姓名：肖聪
职称：副教授/硕士生导师，博士生导师
教育和工作经历： (从本科开始 至今)
2009.9 - 2013.6 长江大学石油工程系，石油工程，学士
2013.9 - 2016.6 中国石油大学(北京)石油工程系，油气田开发工程，硕士
2016.9 - 2021.1 荷兰代尔夫特理工大学应用数学系，应用数学，博士
2021.4 - 2024.07 中国石油大学（北京），校青年拔尖人才，讲师，
2024.07 － 至今 中国石油大学（北京），副教授
电子邮箱：18810907235@163.com
个人主页：https://www.researchgate.net/profile/Cong-Xiao-6
所在系所：油气田开发工程系
研究方向：深度学习智能反演优化理论与方法、非常规油气藏智能压裂理论与方法
教学情况：《采油工程》（全英授课）、《试井分析》（全英授课）、《高等采油工程》（全英授课）
代表性论文著作：
[1] Xiao C, Zhang SC . Robust optimization of geoenergy production using data-driven deep recurrent auto-encoder and fully-connected neural network proxy.Expert Systems with Applications.2024
[2] Xiao C , Zhang SC . Robust production forecast and uncertainty quantification for waterflooding reservoir using hybrid recurrent auto-encoder and long short-term memory neural network.Geoenergy Science and Engineering,2023
[3] Xiao C , Zhang SC . Deep-learning-generalized data-space inversion and uncertainty quantification framework for accelerating geological CO2 plume migration monitoring.Geoenergy Science and Engineering.2023
[4] Xiao C , Zhang SC . Data-driven model predictive control for closed-loop refracturing design and optimization in naturally fractured shale gas reservoir under geological uncertainty.Computers and Chemical Engineering.2023
[5] Xiao C , Zhang SC . Model-reduced adjoint-based inversion using deep-learning: Example of geological carbon sequestration modelling.water resources research.2022
[6] Xiao C , Zhang SC . Machine-learning-based well production prediction under geological and hydraulic fracture parameters uncertainty for unconventional shale gas reservoirs.JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING.2022
[7] Xiao, C., Lin, H.X, Leeuwenburgh, O., Heemink, A. Surrogate-assisted inversion for large-scale history matching: Comparative study between projection-based reduced-order modeling and deep neural network[J]. Journal of Petroleum Science and Engineering, 2022.
[8] 肖聪,张士诚,马新仿,等.基于模型降维和递归神经网络的油藏参数反演[J].计算物理,2022,39(05):564-578.
[9] Xiao, C., Leeuwenburgh, O , Heemink, A., Lin, H.X. Conditioning of Deep-Learning Surrogate Models to Image Data with Application to Reservoir Characterization[J]. Knowledge-Based Systems, 2021, 3.
[10] Xiao C , Deng Y, Wang GD . Deep-Learning-Based Adjoint State Method: Methodology and Preliminary Application to Inverse Modeling[J]. Water Resources Research, 2021, 2.
[11] Xiao, C., Leeuwenburgh, O ,Heemink, A., Lin, H.X. Efficient estimation of space varying parameters in numerical models using non-intrusive subdomain reduced order modeling[J]. Journal of Computational Physics, 2020, 424.
[12] Xiao C , Tian L . Surrogate‐Based Joint Estimation of Subsurface Geological and Relative Permeability Parameters for High‐Dimensional Inverse Problem by Use of Smooth Local Parameterization[J]. Water Resources Research, 2020, 56(7).
[13] Xiao C , Tian L . Modelling of fractured horizontal wells with complex fracture network in natural gas hydrate reservoirs[J]. International Journal of Hydrogen Energy, 2020, 45( 28):14266-14280.
[14] Xiao C , Tian L , Zhang L , et al. Distributed Gauss-Newton Optimization with Smooth Local Parameterization for Large-Scale History-Matching Problems[J]. SPE Journal, 2020, 25(1):056-080.
[15] Xiao C , Zhan M B , Leng T C . Semi-analytical modeling of productivity analysis for five-spot well pattern scheme in methane hydrocarbon reservoirs[J]. International Journal of Hydrogen Energy, 2019, 44( 49):26955-26969.
[16] Xiao, C., Leeuwenburgh, O ,Heemink, A., Lin, H.X. Non-intrusive Subdomain POD-TPWL Algorithm for Reservoir History Matching[J]. Computational Geosciences, 2018, 23(6).
[17] Xiao C , Dai Y , Tian L , et al. A Semi-analytical Methodology for Pressure-Transient Analysis of Multi-well-Pad-Production Scheme in Shale Gas Reservoirs, Part 1: New Insights Into Flow Regimes and Multi-well Interference[J]. SPE Journal, 2018.
[18] Xiao C , Tian L , Zhang Y , et al. A Novel Approach To Detect Interacting Behavior Between Hydraulic Fracture and Natural Fracture Using Semi-analytical Pressure-Transient Model[J]. SPE Journal, 2017.
[19] Xiao C , Tian L , et al. Comprehensive application of semi-analytical PTA and RTA to quantitatively determine abandonment pressure for CO2 storage in depleted shale gas reservoirs[J]. Journal of Petroleum Science and Engineering, 2016.
[20] 肖聪，张士诚，马新仿等。基于深度学习代理模型的油藏自动历史拟合算法研究，第七届数字油田国际学术会议，2021年11月3日-5日。
[21] Xiao, C., et al, O., Projection-based autoregressive neural network for model-reduced adjoint-based variational data assimilation, Presented at The 82nd EAGE Annual Conference & Exhibition. Netherlands, 18 - 23, October, 2021.
[22] Xiao, C., et al, O., Deep Learning Surrogate-Assisted Assimilation of Image-type Data, Presented at International EnKF Workshop. Norway, 11 - 15, June, 2021.
[23] Xiao, C., Heemink, A., Lin, H.X. and Leeuwenburgh, O., Efficient Deep-Learning Inversion for Big-Data Assimilation: Application to Seismic History Matching, Presented at ECMOR XVII, Edinburgh, United Kingdom, 14-17 September, 2020.
[24] Xiao, C., Heemink, A., Lin, H.X. and Leeuwenburgh, O., Subdomain Reduced-Order Modelling with Smooth Local Parameterization for Large-Scale Inversion Problem, Presented at ENUMATH 2019 conference, The Netherlands, 30 September - 4 October, 2019.
[25] [Xiao, C., Heemink, A., Lin, H.X. and Leeuwenburgh, O., A machine-learning Based Subdomain POD-TPWL for Large-Scale Inversion Problems, Presented at InterPore2019, Valencia, Spain, 6 -10 May, 2019.
[26] Xiao, C., Heemink, A., Lin, H.X. and Leeuwenburgh, O., Subdomain Adjoint-Based Variational Data Assimilation for Reservoir History Matching, Presented at 13th International EnKF Workshop. Bergen, Norway, 28 - 30, May, 2018.
代表性专利与软著:
1、缝网压裂多缝延伸穿透行为的判定方法和装置，2018
2、一种地质参数的反演方法、装置、电子设备及存储介质，2022
3、致密油压裂水平井的产量预测方法、装置和计算机设备，2023
4、用于水平井产能的预测方法、存储介质及处理器，2023
5、一种多尺度的水平井二氧化碳前置蓄能压裂模拟及压裂参数设计装置，2024
6、《Surrogate-Assisted Reservoir History Matching》，Delft University of Technology, 2021. ISBN:978-94-6366-365-6.
主要科学研究项目：
1、《页岩油平台井闷井压力干扰响应机理与智能诊断方法研究》，国家自然科学基金青年项目，2024-2026，主持
2、《基于机器学习和智能算法的体积压裂缝网-井网自动优化技术研究》，页岩油气富集机理与有效开发国家重点实验室开发基金，2021，主持
3、《基于深度学习的页岩压裂缝网智能反演与产能预测一体化研究》，中国石油大学（北京）青年拔尖人才引进启动项目，2021-2024年，主持
4、《CO2压裂数值模拟代理模型智能调控与优化软件开发》，2023-2024年，主持
5、《厚层页岩油立体开发与整体压裂优化设计技术》，2023-2024年，参与
6、《多层系页岩油立体压裂关键技术研究》，2022-2024年，参与
7、《非常规油气藏CO2压裂提高采收率技术研究与应用》，2023-2025年，参与
重要奖励与荣誉：
1、中国石油和化工自动化行业科学技术处奖一等奖，2024.
2、中国产学研合作与创新促进奖优秀成果奖，2023
社会与学术兼职：
Journal of Petroleum Science and Engineering，Journal of Natural Gas Science and Engineerin， SPE Journal以及Water Resource Research等国际权威期刊审稿人。《Natural Gas Industry B》(天然气工业英文版)副主编,《Petroleum Science》和《东北石油大学学报》青年编委。
石工
设计