姓名：薛亮
职称/职务：教授/博导/副院长/北京市科技领军人才
办公地点：中油大厦
办公电话：010-89732260
电子邮箱：xueliang@cup.edu.cn
★〡研究方向
1.裂缝性油气藏高效数值模拟
2.地质油藏模型一体化迭代更新
3.数据物理联合驱动人工智能算法
★〡教育与工作经历
2001-2005，中国地质大学（北京），本科
2005-2007，中国地质大学（北京），硕士
2007-2011，美国亚利桑那大学（University of Arizona），博士
2011-2014，北京大学，博士后
2014-2015，中国石油大学（北京），讲师
2015-2023，中国石油大学（北京），副教授
2023-至今， 中国石油大学（北京），教授
★〡招生方向/招生专业
油气田开发、地热、CCUS、人工智能
★〡学术论文
[1] Hai-Yang Chen, Liang Xue*, Li Liu, Gao-Feng Zou, Jiang-Xia Han, Yu-Bin Dong, Meng-Ze Cong, Yue-Tian Liu, Seyed Mojtaba Hosseini-Nasab, Physics-informed graph neural network for predicting fluid flow in porous media, Petroleum Science, 22 (10), 2025, 4240-4253.
[2] Yubin Dong, Liang Xue*, Xianzhi Song, Zhongwei Huang, Yuetian Liu, Haiyang Chen, Multi-physics coupling mechanisms and key development factors in enhanced geothermal systems for hot dry rock, Energy, 337, 2025, 138533.
[3] Pei Xuehao, Liu Yuetian*, Xue Liang. Equivalent force model of deformation induced by oil and gas reservoir development and its volume boundary element method solution[J].石油勘探与开发（英文版）, 2025, 11: 485-495.
[4] Liang Xue, Shuai Xu, Jie Nie, Ji Qin, Jiang-Xia Han, Yue-Tian Liu, Qin-Zhuo Liao, An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network, Petroleum Science, Volume 21, Issue 4, 2024, Pages 2475-2484.
[5] Xiaoyi Wei, Wensong Huang, Lingli Liu, Jianjun Wang, Zehong Cui, Liang Xue*, Low-rank coalbed methane production capacity prediction method based on time-series deep learning, Energy, Volume 311, 2024, 133247.
[6] Liang Xue, Yangwen Zhu, Junfan Ren, Hanying Liao, Quangqi Dai, Bin Tu. Coupled optimization method for CO2-EOR and storage based on machine learning, Journal of Porous Media, 2024, DOI: 10.1615/JPorMedia.2024052865.
[7] Lei Xu, Yulong Chen, Yuntian Chen, Longfeng Nie, Xuetao Wei, Liang Xue, Dongxiao Zhang. Swarm Learning for energy series modeling: A decentralized collaborative learning design based on blockchain.
[8] Han, J., Xue, L.*, Liu, Q. et al. 2024. Global Probabilistic Forecasting for Multiple Tight Gas Wells Using Deep Autoregressive Networks. SPE J. SPE-223596-PA (in press; posted 25 September 2024)
[9] Shaohua You, Qinzhuo Liao*, Zhengting Yan, Gensheng Li, Shouceng Tian, Xianzhi Song, Haizhu Wang, Liang Xue, Gang Lei, Xu Liu, Shirish Patil, Super-resolution reconstruction of 3D digital rocks by deep neural networks, Geoenergy Science and Engineering, Volume 237, 2024, 212781.
[10] Jiangxia Han, Liang Xue*, Ying Jia, Mpoki Sam Mwasamwasa1, Felix Nanguka, Charles Sangweni, Hailong Liu, Qian Li. Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks. Computer Modeling in Engineering & Sciences 2024, 138(2), 1323-1340.
[11] He, Yuting; Liu, Yuetian; Li, Jingpeng; Fan, Pingtian; Liu, Xinju; Chai, Rukuan; Xue, Liang (2024). Experimental study on the effect of CO2 dynamic sequestration on sandstone pore structure and physical properties. FUEL, 375, 132622 .
[12] Jiang-Xia Han, Liang Xue*, Yun-Sheng Wei, Ya-Dong Qi, Jun-Lei Wang, Yue-Tian Liu, Yu-Qi Zhang, Physics-informed neural network-based petroleum reservoir simulation with sparse data using domain decomposition, Petroleum Science, 2023, 20 (6): 3450-3460 .
[13] Xue, L., Li, D., Dou, H. Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives. Advances in Geo-Energy Research, 2023, 10(1): 65-70.
[14] Qinzhuo Liao, Gensheng Li*, Jun Li, Liang Xue, Shouceng Tian, Xianzhi Song, Convergence analysis of Lattice Boltzmann method for Stokes flow in digital rock characterization, Geoenergy Science and Engineering, Volume 230, 2023, 212161.
[15] Han, JiangXia, and Liang Xue. "Multiple Production Time Series Forecasting Using Deepar and Probabilistic Forecasting." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, October 2023.
[16] Xue, L.*, Wang, J., Han, J., Yang, M., Mwasmwasa, M. S., Nanguka, F. Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data. Advances in Geo-Energy Research, 2023, 8(3): 159-169. https://doi.org/10.46690/ager.2023.06.03
[17] Gang Lei, Liang Xue*, Qinzhuo Liao, Jun Li, Yang Zhao, Xianmin Zhou, Chunhua Lu, A novel analytical model for porosity-permeability relations of argillaceous porous media under stress conditions, Geoenergy Science and Engineering, Volume 225, 2023, 211659
[18] Du, Enda., Yuetian Liu*, Liang Xue*, Xiao-lei Zhu and Laiming Song. “A Data-Driven Model for Production Prediction of Strongly Heterogeneous Reservoir Under Uncertainty.” Geoenergy science and engineering (2023): 223, 211542.
[19] Xuehao Pei, Yuetian Liu*, Liang Xue*, Laiming Song, Tengda Rang, A new determination method for the anisotropic permeability tensor based on the passive differential pressure ratio, International Communications in Heat and Mass Transfer, 2023, 140, 106544.
[20] Liao, Q., Xue, L., Wang, B., Lei, G*. A new upscaling method for microscopic fluid flow based on digital rocks. Advances in Geo-Energy Research, 2022, 6(4): 357-358.
[21] Liu, Q., Xue, L.*, Sarout, J., Lin, Q., Pan, W., Liu, Y., Feng, R*. (2022). Automatic history matching of multistage fractured shale gas reservoir constrained by microseismic data. Journal of Petroleum Science and Engineering, 213, 110357.
[22] Qin-Zhuo Liao, Liang Xue*, Gang Lei, Xu Liu, Shu-Yu Sun, Shirish Patil, Statistical prediction of waterflooding performance by K-means clustering and empirical modeling, Petroleum Science, 2022
[23] Xue, L., Gu, S., Mi, L., Zhao, L., Liu, Y., & Liao, Q*. (2022). An automated data-driven pressure transient analysis of water-drive gas reservoir through the coupled machine learning and ensemble Kalman filter method. Journal of Petroleum Science and Engineering, 208, 109492.
[24] Rukuan Chai, Yuetian Liu*, Liang Xue*, Zhenhua Rui, Ruicheng Zhao, Jingru Wang, Formation damage of sandstone geothermal reservoirs: During decreased salinity water injection, Applied Energy, Volume 322, 2022, 119465 (SCI, TOP期刊)
[25] Du, Enda; Liu, Yuetian*; Cheng, Ziyan; Xue, Liang*; Ma, Jing; and Xuan He. "Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network." SPE Reservoir Evaluation & Engineering, 2021, 1–17.
[26] Liang Xue*, Shaohua Gu, Xieer Jiang, Yuetian Liu, Chen Yang. (2021) Ensemble-based optimization of hydraulically fractured horizontal well placement in shale gas reservoir through Hough-transform parameterization. Petroleum Science. 18: 839-851.
[27] Wang, Nanzhe; Chang, Haibin*; Zhang, Dongxiao*; Xue, Liang; Chen, Yuntian. (2021). Efficient well placement optimization based on theory-guided convolutional neural network. Journal of Petroleum Science and Engineering. 208. 109545.
[28] Liang Xue, Yuetian Liu, Yifei Xiong, Yanli Liu, Xuehui Cui, Gang Lei*. (2021), A data-driven shale gas production forecasting method based on the multi-objective random forest regression, Journal of Petroleum Science and Engineering, 196. (SCI, TOP期刊)
[29] Rukuan Chai, Yuetian Liu*, Yuting He, Mingjun Cai, Jialiang Zhang, Feifei Liu, and Liang Xue*, Effects and Mechanisms of Acidic Crude Oil–Aqueous Solution Interaction in Low-Salinity Waterflooding. Energy & Fuels, 2021. 35(12): p. 9860-9872.
[30] Liang Xue, Yuetian Liu, Tongchao Nan, Qianjun Liu, XieerJiang. (2020), An efficient automatic history matching method through the probabilistic collocation based particle filter for shale gas reservoir, Journal of Petroleum Science and Engineering, 190.
[31] 薛亮,范基梁,聂捷,等.基于岩石物理领域知识约束长短期记忆神经网络的储层参数预测.计算物理,2025,1-13.
[32] 任俊帆, 薛亮, 聂捷, 肖镭, 廖广志. 基于随机森林算法的二氧化碳驱油与封存主控因素研究，地质科技通报，2024, 43(3): 147-156.
[33] 韩江峡, 薛亮, 位云生, 等. 基于深度自回归神经网络的多井产量概率预测[J]. 石油科学通报, 2024, 9(4): 679–689.
[34] 裴雪皓，刘月田，林子愉，樊平天，米辽，薛亮.多孔介质各向异性动态渗透率模型[J].石油勘探与开发,2024,51(01):173-181.
[35] 薛亮，戴城*，韩江峡，杨明瑾，刘月田. 油藏渗流物理和数据联合驱动的深度神经网络模型.油气地质与采收率，2022, 29（1）：145-151.
[36] 薛亮*，顾少华,王嘉宝,刘月田,涂彬.基于粒子群优化和长短期记忆神经网络的气井生产动态预测[J].石油钻采工艺,2021,43(04):525-531.
[37] 糜利栋,顾少华,薛亮*,赵林.(2021) 基于数据驱动技术的智能试井解释方法——以有水气藏产水气井为例.天然气工业.2：:19-124.
[38] 薛亮*，吴雨娟，刘倩君，刘月田，王军，蒋龙，程紫燕.(2019)裂缝性油气藏数值模拟与自动历史拟合研究进展,石油科学通报,4(4): 335-346
★〡发明专利
[1] 压裂性裂缝油藏产能模拟方法及装置, 专利号：ZL201910055675.9, 第一发明人
[2] 一种水平井参数优化方法及装置；专利号：ZL201910738688.6, 第一发明人
[3] 基于微震事件的裂缝油气藏历史拟合的方法、装置及系统；专利号：ZL201910613225.7, 第一发明人
[4] 基于深度学习的页岩气饱和度确定方法、装置和设备；专利号：ZL202010690745.0, 第一发明人
[5] 基于卷积编码动态序列网络的产量预测方法、装置及设备；专利号：ZL202111219923.2, 第一发明人
[6] 基于卷积神经网络的页岩气产量确定方法、装置和设备；专利号：ZL202011229932.5, 第一发明人
[7] 基于长短期记忆神经网络的页岩气产量确定方法、装置；专利号：ZL 202110096222.8, 第一发明人
[8] 多重时间序列井网产量概率预测方法、装置、设备及介质; 专利号：ZL202311091361.7, 第一发明人
★〡论著
[1] Xue, L., Xiaozhe, G., Hao, C., (2020.12) Fluid Flow in Porous Media：Fundamentals and Applications, World Scientific Publishing, Singapore
[2] 隋微波，陈冬，薛亮.《石油工程专业英语基础》，石油工业出版社，2021年7月出版.
[3] Xue, L. (2012), Multimodel Analysis of Data Collection Schemes, LAMBERT Academic Publishing, Germany.
★〡科研项目
[1] 国家自然科学基金委面上基金，页岩气跨尺度多区复合运移机理与数据联合驱动的产能预测，负责
[2] 北京市自然科学基金面上项目, 基于深度学习方法的致密气渗流高效随机模拟研究，负责
[3] 中国石油科技创新基金，致密砂岩油藏低盐度水驱提高采收率机理研究，负责
[4] 中国石油大学（北京）拔尖人才科研启动基金，基于不确定性分析的油藏开发监测方案设计，负责
[5] 国家科技重大专项子课题，页岩气开发历史拟合与优化研究，负责
★〡获奖
[1] 中国石油和化学工业联合会科学技术奖科技进步一等奖
[2] 中国石油和化工自动化行业科学技术奖技术发明一等奖
[3] 绿色矿山科学技术奖科技进步一等奖
[4] 中国发明协会发明创业奖创新一等奖
[5] 北京市科学技术奖技术发明二等奖
★〡社会与学术兼职
2019年-现在，Petroleum Science期刊副主编
2021年-现在，Advances in Geo-energy Research期刊青年编委
2021年-现在，北京市昌平区政协委员
2007年-现在，美国地球物理协会AGU会员
2014年-现在，石油工程师协会SPE会员
SPE Journal、JPSE、Water Resources Research、Journal of Hydrology等期刊审稿人
