姓名：肖聰
職稱(chēng)：副教授/碩士生導(dǎo)師，博士生導(dǎo)師
教育和工作經(jīng)歷： (從本科開(kāi)始 至今)
2009.9 - 2013.6 長(zhǎng)江大學(xué)石油工程系，石油工程，學(xué)士
2013.9 - 2016.6 中國(guó)石油大學(xué)(北京)石油工程系，油氣田開(kāi)發(fā)工程，碩士
2016.9 - 2021.1 荷蘭代爾夫特理工大學(xué)應(yīng)用數(shù)學(xué)系，應(yīng)用數(shù)學(xué)，博士
2021.4 - 2024.07 中國(guó)石油大學(xué)（北京），校青年拔尖人才，講師，
2024.07 － 至今 中國(guó)石油大學(xué)（北京），副教授
電子郵箱：[email protected]
個(gè)人主頁(yè)：https://www.researchgate.net/profile/Cong-Xiao-6
所在系所：油氣田開(kāi)發(fā)工程系
研究方向：深度學(xué)習(xí)智能反演優(yōu)化理論與方法、非常規(guī)油氣藏智能壓裂理論與方法
教學(xué)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(chéng),馬新仿,等.基于模型降維和遞歸神經(jīng)網(wǎng)絡(luò)的油藏參數(shù)反演[J].計(jì)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(chéng)，馬新仿等?；谏疃葘W(xué)習(xí)代理模型的油藏自動(dòng)歷史擬合算法研究，第七屆數(shù)字油田國(guó)際學(xué)術(shù)會(huì)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(zhuān)利與軟著:
1、縫網(wǎng)壓裂多縫延伸穿透行為的判定方法和裝置，2018
2、一種地質(zhì)參數(shù)的反演方法、裝置、電子設(shè)備及存儲(chǔ)介質(zhì)，2022
3、致密油壓裂水平井的產(chǎn)量預(yù)測(cè)方法、裝置和計(jì)算機(jī)設(shè)備，2023
4、用于水平井產(chǎn)能的預(yù)測(cè)方法、存儲(chǔ)介質(zhì)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(shù)設(shè)計(jì)裝置，2024
6、《Surrogate-Assisted Reservoir History Matching》，Delft University of Technology, 2021. ISBN:978-94-6366-365-6.
主要科學(xué)研究項(xiàng)目：
1、《頁(yè)巖油平臺(tái)井悶井壓力干擾響應(yīng)機(jī)理與智能診斷方法研究》，國(guó)家自然科學(xué)基金青年項(xiàng)目，2024-2026，主持
2、《基于機(jī)器學(xué)習(xí)和智能算法的體積壓裂縫網(wǎng)-井網(wǎng)自動(dòng)優(yōu)化技術(shù)研究》，頁(yè)巖油氣富集機(jī)理與有效開(kāi)發(fā)國(guó)家重點(diǎn)實(shí)驗(yàn)室開(kāi)發(fā)基金，2021，主持
3、《基于深度學(xué)習(xí)的頁(yè)巖壓裂縫網(wǎng)智能反演與產(chǎn)能預(yù)測(cè)一體化研究》，中國(guó)石油大學(xué)（北京）青年拔尖人才引進(jìn)啟動(dòng)項(xiàng)目，2021-2024年，主持
4、《CO2壓裂數(shù)值模擬代理模型智能調(diào)控與優(yōu)化軟件開(kāi)發(fā)》，2023-2024年，主持
5、《厚層頁(yè)巖油立體開(kāi)發(fā)與整體壓裂優(yōu)化設(shè)計(jì)技術(shù)》，2023-2024年，參與
6、《多層系頁(yè)巖油立體壓裂關(guān)鍵技術(shù)研究》，2022-2024年，參與
7、《非常規(guī)油氣藏CO2壓裂提高采收率技術(shù)研究與應(yīng)用》，2023-2025年，參與
重要獎(jiǎng)勵(lì)與榮譽(yù)：
1、中國(guó)石油和化工自動(dòng)化行業(yè)科學(xué)技術(shù)處獎(jiǎng)一等獎(jiǎng)，2024.
2、中國(guó)產(chǎn)學(xué)研合作與創(chuàng)新促進(jìn)獎(jiǎng)優(yōu)秀成果獎(jiǎng)，2023
社會(huì)與學(xué)術(shù)兼職：
Journal of Petroleum Science and Engineering，Journal of Natural Gas Science and Engineerin， SPE Journal以及Water Resource Research等國(guó)際權(quán)威期刊審稿人。《Natural Gas Industry B》(天然氣工業(yè)英文版)副主編,《Petroleum Science》和《東北石油大學(xué)學(xué)報(bào)》青年編委。
石工
設(shè)計(jì)