姓名：肖聰
職稱：副教授/碩士生導師，博士生導師
教育和工作經歷： (從本科開始 至今)
2009.9 - 2013.6 長江大學石油工程系，石油工程，學士
2013.9 - 2016.6 中國石油大學(北京)石油工程系，油氣田開發工程，碩士
2016.9 - 2021.1 荷蘭代爾夫特理工大學應用數學系，應用數學，博士
2021.4 - 2024.07 中國石油大學（北京），校青年拔尖人才，講師，
2024.07 － 至今 中國石油大學（北京），副教授
電子郵箱：18810907235@163.com
個人主頁：https://www.researchgate.net/profile/Cong-Xiao-6
所在系所：油氣田開發工程系
研究方向：深度學習智能反演優化理論與方法、非常規油氣藏智能壓裂理論與方法
教學情況：《采油工程》（全英授課）、《試井分析》（全英授課）、《高等采油工程》（全英授課）
代表性論文著作：
[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] 肖聰,張士誠,馬新仿,等.基于模型降維和遞歸神經網絡的油藏參數反演[J].計算物理,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] 肖聰，張士誠，馬新仿等?；谏疃葘W習代理模型的油藏自動歷史擬合算法研究，第七屆數字油田國際學術會議，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.
代表性專利與軟著:
1、縫網壓裂多縫延伸穿透行為的判定方法和裝置，2018
2、一種地質參數的反演方法、裝置、電子設備及存儲介質，2022
3、致密油壓裂水平井的產量預測方法、裝置和計算機設備，2023
4、用于水平井產能的預測方法、存儲介質及處理器，2023
5、一種多尺度的水平井二氧化碳前置蓄能壓裂模擬及壓裂參數設計裝置，2024
6、《Surrogate-Assisted Reservoir History Matching》，Delft University of Technology, 2021. ISBN:978-94-6366-365-6.
主要科學研究項目：
1、《頁巖油平臺井悶井壓力干擾響應機理與智能診斷方法研究》，國家自然科學基金青年項目，2024-2026，主持
2、《基于機器學習和智能算法的體積壓裂縫網-井網自動優化技術研究》，頁巖油氣富集機理與有效開發國家重點實驗室開發基金，2021，主持
3、《基于深度學習的頁巖壓裂縫網智能反演與產能預測一體化研究》，中國石油大學（北京）青年拔尖人才引進啟動項目，2021-2024年，主持
4、《CO2壓裂數值模擬代理模型智能調控與優化軟件開發》，2023-2024年，主持
5、《厚層頁巖油立體開發與整體壓裂優化設計技術》，2023-2024年，參與
6、《多層系頁巖油立體壓裂關鍵技術研究》，2022-2024年，參與
7、《非常規油氣藏CO2壓裂提高采收率技術研究與應用》，2023-2025年，參與
重要獎勵與榮譽：
1、中國石油和化工自動化行業科學技術處獎一等獎，2024.
2、中國產學研合作與創新促進獎優秀成果獎，2023
社會與學術兼職：
Journal of Petroleum Science and Engineering，Journal of Natural Gas Science and Engineerin， SPE Journal以及Water Resource Research等國際權威期刊審稿人?！?/span>Natural Gas Industry B》(天然氣工業英文版)副主編,《Petroleum Science》和《東北石油大學學報》青年編委。
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