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张东晓 国家级人才 美国国家工程院 院士 宁波东方理工大学常务副校长兼教务长、讲席教授。 北京大学力学与工程科学学院 讲席教授 中国力学学会第十二届理事会副理事长 美国地质学会会士（Fellow），国际石油工程师协会SPE最高荣誉会员。 电子邮箱：dxz@pku.edu.cn 个人主页： https://staff.eitech.edu.cn/engineering/zdx/main.htm |
教育经历：
1993/01～1993/12 美国Arizona大学 水文学 博士
1991/08～1992/12 美国Arizona大学 水文学 硕士
1990/07～1991/07 美国Arizona大学 地质工程 硕士研究生
1988/08～1989/07 东北大学 采矿工程系岩石力学方向 硕士研究生
1984/08～1988/07 东北大学 采矿工程系 学士
研究领域：
地下水文学、非常规油气开采、二氧化碳地质埋藏、 随机理论建模与数值计算、机器学习等方面应用
背景资料：
美国国家工程院院士，中国力学学会第十二届理事会副理事长，美国地质学会会士(Fellow)，国际石油工程师协会SPE最高荣誉会员。历任南方科技大学学术副校长，北京大学研究生院常务副院长、工学院院长、海洋研究院院长，美国南加州大学Marshall讲席正教授（终身制），俄克拉荷马大学石油和地质工程系米勒讲席正教授（终身制），北京大学能源与资源工程系首任系主任，美国拉萨拉莫斯(Los Alamos)国家实验室高级研究员。为地下水文学、非常规油气开采（煤层气、页岩气）、二氧化碳地质埋藏方面的国际著名学者，其随机理论建模、数值计算、历史拟合方面的研究成果已被国际同行广泛采用。曾担任英国国家研究理事会“能源研究评估委员会”委员、美国国家研究委员会“地球科学2010-2020科研规划委员会”委员、达沃斯世界经济论坛（WEF）“全球议程理事会”理事、中国学位与研究生教育学会文理科工作委员会主任、中国学位与研究生教育学会评估委员会（第六届）副主任、中国研究生院院长联席会秘书长。
工作经历：
2022.02-至今：宁波东方理工大学常务副校长兼教务长、讲席教授
2019.07-2022.02：南方科技大学学术副校长、教务长、讲席教授
2017.11-2019.08：北京大学研究生院常务副院长
2013.07-2019.07：北京大学工学院院长，能源与资源工程系讲席教授 ；北京大学海洋研究院院长
2010.08-2013.06：北京大学工学院常务副院长，能源与资源工程系讲席教授
2007.08-2010.08：美国南加州大学土木与环境工程系和化学工程与材料科学系，水资源与石油工程，Marshall讲席教授
2005.07-2010.08：北京大学工学院创院副院长（2005-2007）；能源和资源工程系讲席教授，首任系主任（2005-2007）
2004.03-2007.07：美国俄克拉荷马大学石油与地质工程系，米勒讲席教授（终身制）
1996.09-2004.03：美国国家实验室地球和环境科学部，高级研究员和研究室主任（1999-2003）
2002.08-2002.12：香港科技大学土木工程系访问学者，从美国国家实验室学术休假 ；教学：随机地下水文学，研究：地表/地下流动的耦合
2003.06-2010.12：南京大学地球科学系兼任教授
2000.12-2009.12：美国地球物理联合会《水资源研究》（Water Resources Research）副主编
2002.08-2012.12：国际石油工程师杂志（SPE Journal）副主编
2003.07-2008.12：美国土壤科学学会《非饱和带杂志》（Vadose Zone Journal）副主编
2004.07-至今：Elsevier出版社Advances in Water Resources编委会成员
2005.01-2011.12：美国工业与应用数学学会Multiscale Modeling and Simulation副主编
2007.01-至今：Springer出版社Journal of Computational Geosciences副主编
2010.01-至今：《温室气体：科学与技术》（Greenhouse Gases: Science and Technology）编辑顾问
1995.03-1996.08：Daniel B. Stephens＆Associates有限公司，高级水文学家
1994.01-1995.02：亚利桑那大学水文和水资源系，助理研究员
1993.01-1993.12：亚利桑那大学水文和水资源系，研究助理
1991.08-1992.12：亚利桑那大学水文和水资源系研究生，研究助理
1990.08-1991.07：亚利桑那大学采矿与地质工程系，研究生教学助理
学术兼职：
2017年：美国国家工程院院士
2009年：美国地质学会会士
2005年：美国工业与应用数学学会（SIAM）
2003年：美国土木工程师学会（ASCE）
2001年：美国地质学会（GSA）
1999年：石油工程师学会（SPE）；最高荣誉会员（2017）
1992年：美国地球物理联合会（AGU）
Researcher ID（Publons）网页：https://publons.com/researcher/2968087/dongxiao-zhang/
谷歌学术个人页面：Dongxiao Zhang（http://scholar.google.com/citations?user=HJdIx6QAAAAJ&hl=en）
代表性论文论著：
论著:
1. Zhang, D., Stochastic Methods for Flow in Porous Media: Coping with Uncertainties, Academic Press, San Diego, Calif., ISBN 012-7796215, 350页, 2002.
2. Zhang, D., and C.L. Winter, editors, Theory, Modeling and Field Investigation in Hydrogeology, Geological Society of America, 245页, 2000. (书, 编辑)
3. Fu, J., D. Zhang, and M. Lei, editors, Climate Mitigation and Adaptation in China: Policy, Technology and Market, Springer, 285页, ISBN: 978-981-16-4310-1, https://doi.org/10.1007/978-981-16-4310-1, 2022.
期刊论文:
328. Cong, Z, Y Chen, Z Wang, J Deng, H Chen, S Zhan, and D Zhang, Greedy compensatory agent based day-ahead photovoltaic power forecasting with a simplified Deep Q-Network, Renewable Energy, https://doi.org/10.1016/j.renene.2025.124616, 2025.
327. Hu, Y., Q. Li, D. Zhang, J. Yan, and Y. Chen, Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series, https://doi.org/10.48550/arXiv.2501.03747, 2025. (arXiv:2501.03747)
326. Peng, W, Y Chen, X Zhao, D Zhang, and S Wang Carbon neutrality in high-density cities: Roles of imported electricity, energy flexibility resources, and green hydrogen in Hong Kong’s power system, Energy Conversion and Management, https://doi.org/10.1016/j.enconman.2025.120926, 2026.
325. Dong, T., M. Pan, D. Wang, Y. Chen, L. Liang, S. Yang, Y. Jin, S. Luo, S. Liang, X. Huang, D. Zhao, A.D. Ziegler, D. Chen, L. Li, T. Zhou, and D. Zhang, Record-breaking 2023 Marine Heatwaves, Science, Vol 389, Issue 6758, pp. 369-374, DOI: 10.1126/science.adr0910, 2025.
324. Xu, H, Y Chen, R Cao, T Tang, M Du, J Li, AH Callaghan, and D Zhang, Generative discovery of partial differential equations by learning from math handbooks, Nature Communications, 16 (1): 10255, DOI: 10.1038/s41467-025-65114-2, 2025.
323. Xu, H., W. Wu, Y. Chen, D. Zhang, and F. Mo, Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning, Nature Communications, 16(1): 832, 1-12, https://doi.org/10.1038/s41467-025-56136-x, 2025.
322. Wang, Z, Y Chen, N Wang, G Chen, D Zhang, Generative Subsurface Flow Modeling With Pretrained Diffusion Model and Training‐Free Knowledge Alignment, Geophysical Research Letters 52 (22), https://doi.org/10.1029/2025GL118000, 2025.
321. Xiong, B, Y Chen, X Zhao, Z Su, J Fu, D Chen, and D Zhang, Multimodal ultra-short-term probabilistic solar power forecasting with generative AI and Transformer, Advances in Applied Energy, Volume 20, 100250, https://doi.org/10.1016/j.adapen.2025.100250, 2025.
320. Chen, D, G Wang, X Shi, M Jiang, S Zhu, H Zhang, D Zhang, Y Chen, and J Yan, Learning to Forecast for Better Scheduling: A Regret-Aware Framework for PV-BESS Optimization, Green Energy and Intelligent Transportation, https://doi.org/10.1016/j.geits.2025.100385, 2025.
319. Wang, Z, Y Chen, G Chen, Q Zheng, T Wu, and D Zhang, Generative emulation and uncertainty quantification of geological CO2 storage with conditional diffusion models, Applied Soft Computing, https://doi.org/10.1016/j.asoc.2025.113542, 2025.
318. Xu, H., Y. Chen, Z. Zeng, N. Li, J. Li, and D. Zhang, Exploring terrain-precipitation relationships with interpretable AI for advancing future climate projections, Nexus, 2(1), DOI: 10.1016/j.ynexs.2024.100045, 2025.
317. Gao, J., Y. Cheng, D. Zhang, and Y. Chen, Physics-constrained wind power forecasting aligned with probability distributions for noise-resilient deep learning, Applied Energy, 383, https://doi.org/10.1016/j.apenergy.2025.125295.
316. Liu, Z, Y Du, D Zhang, and Y Chen, Using electric vehicles as emergency power sources for extreme weather, The Innovation Energy, 2 (3), 100097-1-100097-3, DOI:10.59717/j.xinn-energy.2025.100097, 2025.
315. Xu, L., Y.L. Chen, Y. Chen, L. Nie, X. Wei, L. Xue, and D. Zhang, Swarm Learning for temporal and1spatial series data in energy systems: A decentralized collaborative learning design based on blockchain, Applied Energy, 381, https://doi.org/10.1016/j.apenergy.2024.125053, 2025.
314. Cong, Z, Y Chen, Z Wang, and D Zhang, Physics-Informed Day-Ahead PV Power Forecasting with Seasonal Trend Mitigation and Clear-Sky Template Integration, Energy 360, 100035, https://doi.org/10.1016/j.energ.2025.100035, 2025.
313. Xu, H, Y Chen, Z Zeng, N Li, J Li, and D Zhang, Protocol to discover terrain-precipitation relationships with interpretable artificial intelligence, STAR Protocols 6, 104062 https://doi.org/10.1016/j.xpro.2025.104062, 2025.
312. Zheng, Q, X Shi, Y Cai, L An, and D Zhang, Artificial Intelligence-Empowered Modeling and Management of Flow Batteries: A Mini-Review, Future Batteries, https://doi.org/10.1016/j.fub.2025.100107, 2025.
311. Liu, Y, Y Wang, P Xu, Y Xue, Y Chen, D Zhang, BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network, Energy and Buildings, https://doi.org/10.1016/j.enbuild.2025.116190, 2025
310. Cao, Q., Y. Chen, L. Lu, H. Sun, Z. Zeng, X. Yang, and D. Zhang, Generalized Domain Prompt Learning for Accessible Scientific Vision-Language Models, Nexus, DOI: 10.1016/j.ynexs.2025.100069, 2025
309. Jin, Y, Z Zeng, Y Chen, Y Qin, H Xu, and D Zhang, Innovative Short-Term Weather Forecasting System Combining Data-Driven and Dynamic Downscaling Approaches, Artificial Intelligence for the Earth Systems, DOI: https://doi.org/10.1175/AIES-D-24-0125.1, 2025.
308. Wu, W, H Xu, Y Xu, P Luo, Q Zeng, Y Chen, Y Xu, D Zhang, F Mo, Intelligent column chromatography7prediction model based on automation and machine learning, Chem, https://doi.org/10.1016/j.chempr.2025.102598, 2025.
307. Chen, Z, Y Zhang, J Li, G Hui, Y Sun, Y Li, Y Chen, and D Zhang, Artificial intelligence large model for logging curve reconstruction, Petroleum Exploration and Development, 52(3), https://doi.org/10.1016/S1876-3804(25)60607-0, 2025.
306. Yang, Y, D Zhang, H Wang, Z Lun, H Wang, W Hu, M Cui，A New Method to Simulate and Evaluate Asphaltene Plugging Risk in Oil Wells, SPE Journal, 30 (02): 896–912, https://doi.org/10.2118/223951-PA, 2025.
305. Xu, H., W. Fan, A.C. Taylor, D. Zhang, L. Ruan, and R. Shi, Crack-Net: Prediction of Crack Propagation in Composites, Engineering, https://doi.org/10.1016/j.eng.2025.02.022, 2025. (arXiv:2309.13626, 2023)
304. Wang, Z., Y. Chen, W. Fu, M. Du, G. Chen, X. Ma, and D. Zhang, Generative inverse modeling for improved geological CO2 storage prediction via conditional diffusion models, Applied Energy 395, 126071, https://doi.org/10.1016/j.apenergy.2025.126071, 2025.
303. Yu, X, H Xu, Z Mao, H Sun, Y Zhang, Z Chen, D Zhang, and Y Chen, A data-driven framework for discovering fractional differential equations in complex systems, Nonlinear Dynamics, https://doi.org/10.1007/s11071-025-11373-z, 2025
302. Zhang, L., M. Du, X. Bai, Y. Chen, and D. Zhang, Complex-valued physics-informed machine learning for efficient solving of quintic nonlinear Schrödinger equations，Physical Review Research 7 (1), 013164, DOI: https://doi.org/10.1103/PhysRevResearch.7.013164, 2025.
301. Sun, C., Y. Chen, Q. Cao, L. Nie, Z. Zeng and D. Zhang, HSFormer: Multiscale Hybrid Sparse Transformer for Uncertainty-Aware Cloud and Shadow Removal, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2025.3564855, 2025.
300. Tan, H, Z Guo, J Yan, D Zhang, Y Chen, H Zhang, Advancing low-carbon smart cities: Leveraging UAVs-enabled low-altitude economy principles and innovations, Renewable and Sustainable Energy Reviews 222, 115942, https://doi.org/10.1016/j.rser.2025.115942, 2025.
299. Deng, R, Y Wang, P Xu, F Luo, Q Chen, H Zhang, Y Chen, and D Zhang, A High-Precision Photovoltaic Power Forecasting Model Leveraging Low-Fidelity Data through Decoupled Informer with Multi-Moment Guidance, Renewable Energy, 250: 123391, https://doi.org/10.1016/j.renene.2025.123391, 2025.
298. Li, Y., T. Wu, J. Zhao, G. Wang, and D. Zhang, Thermal Evolution of Organic Matter in Low-Maturity Shale: A Multimodal Nanoscale Investigation, Energy & Fuels, https://doi.org/10.1021/acs.energyfuels.5c00360, 2025
297. Wang, N., Y. Chen, and D. Zhang, A comprehensive review of physics-informed deep learning and its applications in geoenergy development, The Innovation Energy, 2(2): 100087, https://doi.org/10.59717/j.xinn-energy.2025.100087, 2025.
296. Xiong, B., Y. Chen, D. Chen, J. Fu, and D. Zhang, Deep probabilistic solar power forecasting with Transformer and Gaussian process approximation, Applied Energy, 382, 125294, https://doi.org/10.1016/j.apenergy.2025.125294, 2025.
295. Dian, F., Y. Tang, P. Wang, Y. Li, C. Lian, A. Striolo, Y. Chen, Z. Lv, J. Li, S. Zhao, J. Bai, L. Zhou, P. Malgaretti, J. Zhu, and D. Zhang，Crossover scaling of structural and mechanical properties in 3D assemblies of non-spherical, frictional particle, Communications Physics, https://doi.org/10.1038/s42005-025-02009-0, 2025.
294. Zheng, S., K. Xu, and D. Zhang, Reactive Precipitation during Overlaying CO2 Dissolution into Brine: the Role of Porous Structure, Advances in Water Resources, 196, https://doi.org/10.1016/j.advwatres.2024.104880, 2025.
293. Nie., L., Y. Chen, and D. Zhang, All-day cloud property and occurrence probability dataset based on satellite remote sensing data, Scientific Data, https://doi.org/10.1038/s41597-025-04659-9, 2025.
292. Wang, Z., Y. Chen, G. Chen, and D. Zhang, Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning, Geoenergy Science and Engineering, 244, https://doi.org/10.1016/j.geoen.2024.213407, 2025.
291. Song, S., T. Mukerji, and D. Zhang, Physics-informed multi-grid neural operator: theory and an application to porous flow simulation, J. Comp. Phys., 520, 113438, https://doi.org/10.1016/j.jcp.2024.113438, 2025.
290. Xu, H., Y. Chen*, and D. Zhang*, Worth of Prior Knowledge for Enhancing Deep Learning, Nexus, 1(1), https://doi.org/10.1016/j.ynexs.2024.100003, 2024. (arXiv:2307.00712)
289. Du, M., Y. Chen*, and D. Zhang*, DISCOVER: Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning, Physics Review Research, 6 (1), 013182, DOI: 10.1103/PhysRevResearch.6.013182, 2024. (arXiv preprint arXiv:2210.02181)
288. Liu, X., X. He, and D. Zhang, Unveiling the role of climate in spatially synchronized locust outbreak risks, Sci. Adv., eadj1164, DOI: 10.1126/sciadv.adj1164, 2024.
287. Wang, N., X.-Z. Kong*, and D. Zhang*, Physics-Informed Convolutional Decoder (PICD): A novel approach for direct inversion of heterogeneous subsurface flow, Geophysical Research Letters, 51, e2024GL108163, https://doi.org/10.1029/2024GL108163, 2024. (arXiv:2401.06905v1)
286. Du, M., Y. Chen, Z. Wang, L. Nie, and D. Zhang, Large language models for automatic equation discovery of nonlinear dynamics, Physics of Fluids 36, 097121, https://doi.org/10.1063/5.0224297, 2024.
285. Sun, C., H. Xu, Y. Chen, and D. Zhang*, AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN, Adv. Intelligent. Systems, DOI: 10.1002/aisy.202400359, 2024. (arXiv:2312.14935v1)
284. Li, X., D. Zhang, and X. Yin, Investigating Proppant Transport in Slickwater Fractures using Direct Numerical Simulations (DNS) and Two-Fluid Models (TFM), Powder Technology 446, 120151, https://doi.org/10.1016/j.powtec.2024.120151, 2024.
283. Fan, D., H. Hou, J. Zeng, B. Yuan, Z. Lv, Y. Chen, Y. Li, S. Huang, A. Striolo, and D. Zhang, Lattice Boltzmann method/computational fluid dynamics-discrete element method applications for transport and packing of non-spherical particles during geo-energy explorations: A review, Physics of Fluids 36, 081302, https://doi.org/10.1063/5.0222339, 2024.
282. Jiang, C., and D. Zhang*, Lithofacies identification from well-logging curves via integrating prior knowledge into deep learning, Geophysics, 89 (1): D31-D41, https://doi.org/10.1190/geo2022-0770.1, 2024.
281. Nie, L., Y. Chen*, D. Zhang∗, X. Liu, and W. Yuan, QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data, International Journal of Applied Earth Observation and Geoinformation, 127: 103584, https://doi.org/10.1016/j.jag.2023.103584, 2024.
280. Li, S., Kang, Z., Wang, M., Zhang, X., Zhao, J., Li, X.-b., Pan, P., Luo, X., Wu, H., Li, D., Zhang, F., Yuan, S., Fan, H., Liao, Q., Hou, B., Zhang, Y., Gao, K., Feng, X.-T., Zhang, D., Geomechanical Perspectives and Reviews on the Development and Evolution of Cross-Scale Discontinuities in the Earth’s Crust: Patterns, Mechanisms and Models, Gas Science and Engineering, https://doi.org/10.1016/j.jgsce.2024.205412, 2024.
279. Zhang, H., P. Zhao, W. Zhang, Z. Zeng, Y. Wu, P. Li, M. Jiang, L. Huang, S. M. Bartell, W. Liu, Y. Chen, D. Zhang, M. Obersteiner, and J. Yan, Promoting sustainable solar-energy development in harmony with global threatened bird ranges, Nexus, 1(2), DOI: https://doi.org/10.1016/j.ynexs.2024.100017, 2024.
278. Chen, D., X. Shi, H. Zhang, X. Song, D. Zhang, Y Chen, and J. Yan, A Phone-Based Distributed Ambient Temperature Measurement System With an Efficient Label-Free Automated Training Strategy, IEEE Transactions on Mobile Computing, DOI: 10.1109/TMC.2024.3399843, 2024.
277. Bai, X.D., H Xu, D Zhang，Discovering hidden physical mechanisms in Bose–Einstein condensates via deep-learning, The European Physical Journal D, https://doi.org/10.1140/epjd/s10053-024-00841-7, 2024.
276. Jin, Y., Z. Zeng*, Y. Chen*, R. Xu, A.D. Ziegler, W. Chen, B. Ye, and D. Zhang*, Geographically constrained resource potential of integrating floating photovoltaics in global existing offshore wind farms, Adv. Appl. Energy, https://doi.org/10.1016/j.adapen.2024.100163, 2024.
275. Nie, L., Y. Chen, M. Du, C. Sun, and D. Zhang，A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing, Remote Sensing of Environment, 304: 114054, https://doi.org/10.1016/j.rse.2024.114054, 2024. (arXiv:2312.00308)
274. Zhang, P., D. Zhang*, and J. Zhao, Control of fracture toughness of kerogen on artificially-matured shale samples: An energy-based nanoindentation analysis, Journal of Gas Science and Engineering, https://doi.org/10.1016/j.jgsce.2024.205266, 2024.
273. Xu, R., and D. Zhang*, Forward prediction and surrogate modeling for subsurface hydrology: A review of theory-guided machine-learning approaches, Computers and Geosciences,10.1016/j.cageo.2024.105611, 2024.
272. Du, M., L. Nie, S. Lou, Y. Chen, and D. Zhang, Physics-constrained robust learning of open-form partial differential equations from limited and noisy data, Physics of Fluids, 057123, doi: 10.1063/5.0204187, 2024. (arXiv:2309.07672)
271. Zhang, H., Zhao, P., Zhang, W., Zeng, Z., Wu, Y., Li, P., Jiang, M., Huang, L., Bartell, S.M., Liu, W., Chen, Y., Zhang, D., Obersteiner, M., Yan, J., Promoting Sustainable Solar Energy Development in Harmony with Global Threatened Bird Ranges, Nexus, 1(1), doi: https:// doi.org/10.1016/j.ynexs.2024.100017, 2024.
270. Xu, H., J. Zeng, and D. Zhang*, Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion, Research, Vol 6, DOI: 10.34133/research.0147, 2023. (arXiv:2208.03322)
269. Xu, H., J. Lin, D. Zhang*, and F. Mo*, Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network, Nature Communications, 14:3095C, https://doi.org/10.1038/s41467-023-38853-3, 2023.
268. Gao, J., Y. Chen*, W. Hu, and D. Zhang*, An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge, Advances in Applied Energy, vol. 10, 100142, https://doi.org/10.1016/j.adapen.2023.100142, 2023.
267. Zheng, Q., X. Yin, and D. Zhang*, State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks, Journal of Energy Storage, 73: 109244, https://doi.org/10.1016/j.est.2023.109244, 2023.
266. Wang, N., H. Chang, and D. Zhang*. Inverse modeling for subsurface flow based on deep learning surrogates and active learning strategies. Water Resources Research, 59, e2022WR033644. https://doiorg/10.1029/2022WR033644, 2023.
265. Li S., and D. Zhang*, Three-Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock, Journal of Geophysical Research: Solid Earth, DOI: 10.1029/2022JB025673, 2023.
264. Wang, N., H. Chang, X. Kong, D. Zhang*, Deep learning based closed-loop optimization of geothermal reservoir production, Renewable Energy, 211: 379-394, https://doi.org/10.1016/j.renene.2023.04.088, 2023. (arXiv:2204.08987)
263. Mo, C., J. Zhao, and D Zhang*, Mode I microscopic cracking process of granite considering the criticality of failure, Journal of Geophysical Research: Solid Earth, 128 (10), e2023JB027040, https://doi.org/10.1029/2023JB027040, 2023.
262. Mao, S., J. Zeng, K. Wu*, and D. Zhang, Lagrangian Numerical Simulation of Proppant Transport in Channel Fracturing, SPE J., 28 (03): 1369–1386, SPE-212828-PA, https://doi.org/10.2118/212828-PA, 2023.
261. Zhang, P., J. Zhao, D. Zhang*, and Z. Xia, Microindentation analysis of mechanical properties evolution during an artificial hydrocarbon generation process, Gas Science & Engineering, https://doi.org/10.1016/j.jgsce.2023.205003, 2023.
260. Zeng, J., H. Xu, Y. Chen, and D. Zhang*, Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data, Comp. Geosci., https://doi.org/10.1007/s10596-023-10244-z, 2023. (arXiv:2106.00009)
259. Zheng, Q., X. Yin, and D. Zhang*, Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks, Journal of Energy Storage, 65: 107176, https://doi.org/10.1016/j.est.2023.107176, 2023. (arXiv:2205.03508)
258. He, T., H. Chang, and D. Zhang*, Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-023-02604-z, 2023. (arXiv:2205.00134)
257. Luo X., D. Zhang*, A cascaded deep learning framework for photovoltaic power forecasting with multi- fidelity inputs, Energy, DOI: 10.1016/j.energy.2023.126636, 2023.
256. Song, S., D. Zhang*, T. Mukerji, and N. Wang, GANSim-surrogate: An integrated framework for stochastic conditional geomodelling, Journal of Hydrology, 620: 129493, https://doi.org/10.1016/j.jhydrol.2023.129493, 2023.
255. Sun, R., K Xu*, T Huang, D Zhang, Methane Diffusion Through Nanopore-Throat Geometry: A Molecular Dynamics Simulation Study, SPE J., 28 (02): 819–830, SPE-212289-PA, https://doi.org/10.2118/212289-PA, 2023.
254. Wang D., Li S., D. Zhang, Z. Pan*, Understanding and predicting proppant bedload transport in hydraulic fracture via numerical simulation, Powder Technology, DOI: 10.1016/j.powtec.2023.118232, 2023.
253. Wang, N., Q. Liao, H. Chang, and D. Zhang*, Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network, Comp. Geosci., https://doi.org/10.1007/s10596-023-10233-2, 2023.
252. Yin, X., D. Zhang*, batP2dFoam: An Efficient Segregated Solver for the Pseudo-2-Dimensional (P2D) Model of Li-Ion Batteries, J. Electrochem. Soc. 170, 030521, DOI: 10.1149/1945-7111/acbfe4, 2023.
251. Li, J., D. Zhang*, T. He, and Q. Zheng, Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model, Geoenergy Science and Engineering, 10.1016/j.geoen.2022.211368, 2023. (arXiv:2205.14301)
250. Chen, Y., and D. Zhang*, Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development, Petroleum Science, 10.1016/j.petsci.2022.12.017, 2023.
249. Jiang, C., D. Zhang, and S. Chen, Handling missing data in well-log curves with a gated graph neural network, Geophysics, 10.1190/geo2022-0028.1, 88 (1), D13-D30, 2023.
251. Xu H., Che Y., Zhang, and D. Zhang*, Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? Advanced Science, DOI:10.1002/advs.202204723, 2022.
250. Li J., D. Zhang*, Wang N., Chang H., Deep Learning of Two-Phase Flow in Porous Media via Theory-Guided Neural Networks, SPE J., DOI: 10.2118/208602-PA, 2022.
249. Chen, Y., Y. Luo, Q. Liu, H. Xu, and D. Zhang*, Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE), Phys. Rev. Research, DOI: 10.1103/PhysRevResearch.4.023174, 2022.
248. Xu, H., J. Lin, Q. Liu, Y. Chen, J. Zhang, Y. Yang, M.C. Young, Y. Xu, D. Zhang*, and F. Mo*, High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniques, CHEM, DOI: 10.1016/j.chempr.2022.08.008, 2022.
247. Du, M., Y. Chen*, D. Zhang*, AutoKE: An automatic knowledge embedding framework for scientific machine learning, IEEE Transactions on Artificial Intelligence, DOI: 10.1109/TAI.2022.3209167, 2022. (arXiv:2205.05390)
246. Xu, H., D. Zhang, F. Mo, High-throughput automated platform for thin layer chromatography analysis, STAR Protocols, 3 (4), 101893, https://doi.org/10.1016/j.xpro.2022.101893, 2022.
245. Zhou, S., and D. Zhang, Adsorbed and free gas occurrence characteristics and controlling factors of deep shales in the southern Sichuan Basin, China, Petroleum Science, DOI: 10.1016/j.petsci.2022.12.006, 2022.
244. Wang, N., H. Chang*, and D. Zhang*, Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network, J. Comp. Phys., https://doi.org/10.1016/j.jcp.2022.111419, 2022.
243. Wang, N., H. Chang*, D. Zhang*, L. Xue, and Y. Chen, Efficient Well Placement Optimization based on Theory-guided Convolutional Neural Network, Journal of Petroleum Science and Engineering, https://doi.org/10.1016/j.petrol.2021.109545, 2022.
242. Bai, X., and D. Zhang*, Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network, Phy. Rev. E, 106: 025305, DOI: 10.1103/PhysRevE.106.025305, 2022.
241. Song, S., T. Mukerji, J. Hou*, D. Zhang*, and X. Lyu, GANSim-3D for conditional geomodeling: Theory and field application. Water Resources Research, 58, e2021WR031865, https://doi.org/10.1029/2021WR031865, 2022.
240. Xu, R., D. Zhang*, and N. Wang, Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network, Journal of Hydrology, DOI: 10.1016/j.jhydrol.2022.128321, 2022.
239. Luo, X., and D. Zhang*, An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation, Sustainable Energy Technologies and Assessment, https://doi.org/10.1016/j.seta.2022.102326, 2022.
238. Tang, P., D. Zhang*, and H. Li, Predicting permeability from 3D rock images based on CNN with physical information, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2022.127473, 2022.
237. Zhang, W., J. Zhao, and D. Zhang*, Construction and validation of the upscaling model of organic-rich shale by considering water-sensitivity effects, Journal of Petroleum Science and Engineering, https://doi.org/10.1016/j.petrol.2022.110718, 2022.
236. Zhang, W., D. Zhang*, and J. Zhao, Experimental study of the influence of geochemical features on the viscoelasticity of organic matter in shale, Marine and Petroleum Geology, https://doi.org/10.1016/j.marpetgeo.2022.105785, 2022.
235. Zhao, J., W. Zhang, and D. Zhang*, Elastic characterization of shale at micro-scale: A comparison between modulus mapping, PeakForce quantitative nanomechanical mapping, and contact resonance method, SPE Journal, SPE-209795-PA, https://doi.org/10.2118/209795-PA, 2022.
234. Mo, C., J. Zhao, and D. Zhang*, Real-time measurement of mechanical behavior of granite during heating-cooling cycle: a mineralogical perspective, Rock Mechanics and Rock Engineering, DOI: 10.1007/s00603-022-02867-y, 2022.
233. Liu, X., X. Li, and D. Zhang*, A statistical thermodynamics-based equation of state and phase equilibrium calculation for confined hydrocarbons in shale reservoirs, Journal of Natural Gas Science & Engineering, https://doi.org/10.1016/j.jngse.2022.104579, 2022.
232. Yang, W., and D. Zhang*, Experimental Study on Multiphase Flow in 3D-Printed Heterogeneous, Filled Vugs, Journal of Petroleum Science & Engineering, https://doi.org/10.1016/j.petrol.2021.109497, 2022.
231. Zheng, Q., and D. Zhang*, Digital rock reconstruction with user-defined properties using conditional generative adversarial networks, Transport in Porous Media, DOI: 10.1007/s11242-021-01728-6, 2022. (arXiv:2012.07719v2)
230. Zheng, Q., and D. Zhang*, RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning, Comp. Geosci., DOI: 10.1007/s10596-022-10144-8, 2022.
229. Tang, P., J. Zeng*, D. Zhang*, and H. Li, Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network, Journal of Petroleum Science and Engineering, 210, 110051, DOI: 10.1016/j.petrol.2021.110051, 2022.
228. Luo, X., D. Zhang*, and X. Zhu, Combining Transfer Learning and Constrained Long Short-Term Memory for Power Generation Forecasting of Newly-Constructed Photovoltaic Plants, Renewable Energy, 185: 1062-1077, https://doi.org/10.1016/j.renene.2021.12.104, 2022.
227. Rong, M., D. Zhang*, and N. Wang, A Lagrangian Dual-based Theory-guided Deep Neural Network, Complex & Intelligent Systems, https://doi.org/10.1007/s40747-022-00738-1, 2022. (arXiv:2008.10159 )
226. Zeng, J., H. Li, S. Li, and D. Zhang*, Evaluating the Transport Performance of Novel-shaped Proppant in Slickwater Fracturing with the Multi-scale Modeling Framework, SPE Journal, SPE-209583-PA, https://doi.org/10.2118/209583-PA, 2022.
225. J. Li , D. Zhang*, Wang, N., and H. Chang, Deep Learning of Two-Phase Flow in Porous Media via Theory-Guided Neural Networks, SPE Journal, https://doi.org/10.2118/208602-PA, 2022.
224. He, T., N. Wang, and D. Zhang*, Theory-guided full convolutional neural network: An efficient surrogate model for inverse problems in subsurface contaminant transport, Adv. Water Resour., https://doi.org/10.1016/j.advwatres.2021.104051, 2021.
223. Wang, N., H. Chang*, and D. Zhang*, Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example, Journal of Geophysical Research – Solid Earth, 126, 10.1029/2020JB020549, 2021.
222. Wang, N., H. Chang*, and D. Zhang*, Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling, Computer Methods in Applied Mechanics and Engineering, https://doi.org/10.1016/j.cma.2021.114037, 2021.
221. Xu, H.*, and D. Zhang*, Robust discovery of partial differential equations in complex situations, Phy. Rev. Research, 3, 033270, DOI: 10.1103/PhysRevResearch.3.033270, 2021. (arXiv:2106.00008)
220. Bai, X., and D. Zhang*, Learning ground states of spin-orbit-coupled Bose-Einstein condensates by theory-guided neural network, Phy. Rev. A, DOI: https://doi.org/10.1103/PhysRevA.104.063316, 2021.
219. Wang, N., H. Chang*, and D. Zhang*, Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network, Computer Methods in Applied Mechanics and Engineering, https://doi.org/10.1016/j.cma.2020.113492, 2021. (arXiv:2004.13560)
218. Chen, Y., D. Huang, D. Zhang*, J. Zeng, N. Wang, H. Zhang, and J. Yan, Theory-guided Hard Constraint Projection (HCP): A Knowledge-based Data-driven Scientific Machine Learning Method, Journal of Computational Physics 445, 110624, https://doi.org/10.1016/j.jcp.2021.110624, 2021. (arXiv:2012.06148)
217. Chen, Y., and D. Zhang*, Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory, Advances in Applied Energy, https://doi.org/10.1016/j.adapen.2020.100004, 2021. (Preprint available at http://www.enerarxiv.org/page/thesis.html?id=2022)
216. Li, S., and D. Zhang*, Development of 3-D Curved Fracture Swarms in Shale Rock Driven by Rapid Fluid Pressure Buildup: Insights from Numerical Modeling, Geophy. Res. Lett., 48(8), e2021GL092638, https://doi.org/10.1029/2021GL092638, 2021.
215. Xu, H., D. Zhang*, and N. Wang,Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data, Journal of Computational Physics 445, 110592, https://doi.org/10.1016/j.jcp.2021.110592, 2021.
214. Zeng, J., P. Tang, H. Li, and D. Zhang*, Simulating particle settling in inclined narrow channels with the unresolved CFD-DEM method, Phys. Rev. Fluids, 6, 034302, 10.1103/PhysRevFluids.6.034302, 2021.
213. Wang, N., H. Chang*, and D. Zhang*, Efficient Uncertainty Quantification and Data Assimilation via Theory-guided Convolutional Neural Network, SPE Journal, SPE-203904-PA, https://doi.org/10.2118/203904-PA, 2021.
212 Liao, Q., G. Lei, D. Zhang*, and S. Patil, Estimation of Macrodispersivity in Bounded Formations by Circulant Embedding and Analysis of Variance, Water Resour. Res., 57, e2020WR029385. https://doi. org/10.1029/2020WR029385, 2021.
211. Zheng, S., S. Li*, and D. Zhang*, Fluid and heat flow in enhanced geothermal systems considering fracture geometrical and topological complexities: An extended embedded discrete fracture model, Renewable Energy, 10.1016/j.renene.2021.06.127, 2021.
210. Zhang, W., D. Zhang*, and J. Zhao, Experimental investigation of water sensitivity effects on microscale mechanical behavior of shale, International Journal of Rock Mechanics and Mining Sciences, https://doi.org/10.1016/j.ijrmms.2021.104837, 2021. (Preprint available online at ESSOAr: https://doi.org/10.1002/essoar.10502272.2)
209. Xu, R., D. Zhang*, M. Rong, and N. Wang, Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow, Journal of Computational Physics, 110318, doi: https://doi.org/10.1016/j.jcp.2021.110318, 2021.
208. Xu, R., N. Wang, and D. Zhang*, Solution of Diffusivity Equations with Local Sources/Sinks and Surrogate Modeling Using Weak Form Theory-guided Neural Network, Adv. Water Resour., doi: https://doi.org/10.1016/j.advwatres.2021.103941, 2021.
207. He, T., and D. Zhang*, Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network, Journal of Hydrology, 601: 126626, https://doi.org/10.1016/j.jhydrol.2021.126626, 2021. (arXiv:2006.13305)
206. Jiang, C., and D. Zhang*, Lithology identification from well log curves via neural networks with additional geological constraint, Geophysics, 86 (5): IM85–IM100, DOI: https://doi.org/10.1190/geo2020-0676.1, 2021.
205. Xu, H., H. Chang*, and D. Zhang*, DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data, Commun. Comp. Phys., 29(3): 698-728, 10.4208/cicp.OA-2020-0142, 2021. (arXiv:1908.04463)
204. Zhang, Y., D. Zhang*, Q. Wen, W. Zhang, and S. Zheng, Development and evaluation of a novel fracture diverting agent for high temperature reservoirs, Journal of Natural Gas Science & Engineering, https://doi.org/10.1016/j.jngse.2021.104074, 2021.
203. Zeng, J., H. Li*, and D. Zhang, Direct numerical simulation of proppant transport in hydraulic fractures with the immersed boundary method and multi-sphere modeling, Applied Mathematical Modelling, 91: 590-613, 10.1016/j.apm.2020.10.005, 2021.
202. Xu, H., D. Zhang*, and J. Zeng, Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data, Phys. Fluids, 33, 037132, 10.1063/5.0042868, 2021. (arXiv:2005.07916)
201. Luo, X., D. Zhang*, and X. Zhu, Deep Learning Based Forecasting of Photovoltaic Power Generation by incorporating domain knowledge, Energy, https://doi.org/10.1016/j.energy.2021.120240, 2021. (Preprint available at www.enerarxiv.org/page/thesis.html?id=1878)
200. Zhang J, Kuang W H, Zhang X, Mo C K, Zhang D X*, Global review of induced earthquakes in oil and gas production fields. Reviews of Geophysics and Planetary Physics, 52(3): 239-265, DOI: 10.19975/j.dqyxx.2020-027, 2021. (张捷，况文欢，张雄，莫程康，张东晓. 全球油气开采诱发地震的研究现状与对策. 地球与行星物理论评，52（3）：239-265, 2021.)
199. Zhang, D., Y. Yu, S. Li, Y. Chen, and J. Xu, Staging optimization of multi-stage perforation fracturing based on unsupervised machine learning, Journal of China University of Petroleum (Edition of Natural Science), 45(4): 59-66, 2021. (张东晓, 尉玉龙, 李三百, 陈云天，徐加放。基于无监督机器学习的多段射孔压裂的分段优化，中国石油大学学报(自然科学版), 45(4): 59-66，2021.)
198. Chen, Y., and D. Zhang*, Well log generation via ensemble long short-term memory (EnLSTM) network, Geophy. Res. Lett., DOI:10.1029/2020GL087685, 2020
197. Li, S., A. Firoozabadi*, and D. Zhang*, Hydromechanical Modeling of Nonplanar Three-Dimensional Fracture Propagation Using an Iteratively Coupled Approach, Journal of Geophysical Research – Solid Earth, 10.1029/2020JB020115, 2020. (Preprint available online at ESSOAr: doi.org/10.1002/essoar.10503101.1)
196. Zhao, J., W. Zhang, R. Wei, Y. Wang, and D. Zhang*, Influence of geochemical features on the mechanical properties of organic matter in shale, Journal of Geophysical Research – Solid Earth, 10.1029/2020JB019809, 2020. (Preprint available online at ESSOAr: doi.org/10.1002/essoar.10502590.1)
195. Xu, H., H. Chang*, and D. Zhang*, DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm, Journal of Computational Physics, DOI: 10.1016/j.jcp.2020.109584, 2020. (arXiv:2001.07305)
194. Wang, N., D. Zhang*, H. Chang, and H. Li, Deep Learning of Subsurface Flow via Theory-guided Neural Network, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2020.124700, 2020.
193. Zhao, L., H. Li*, J. Meng, and D. Zhang, Efficient uncertainty quantification for permeability of three-dimensional porous media through image analysis and pore-scale simulations, Phys. Rev. E, 102, 023308, https://doi.org/10.2118/203904-PA, 2020.
192. Wu, T., J. Zhao, W. Zhang, and D. Zhang*, Nanopore Structure and Nanomechanical Properties of Organic-Rich Terrestrial Shale: An Insight into Technical Issues for Hydrocarbon Production, Nano Energy, 69: 104426, https://doi.org/10.1016/j.nanoen.2019.104426, 2020
191. Zhao, J., and D. Zhang*, Dynamic microscale crack propagation in shale, Engineering Fracture Mechanics, 10.1016/j.engfracmech.2020.106906, 2020.
190. Chen, Y., and D. Zhang*, Physics-constrained deep learning of geomechanical logs, IEEE Transactions on Geoscience and Remote Sensing, 10.1109/TGRS.2020.2973171, 2020.
189. Li, X., X. Li, D. Zhang*, and R. Yu, A dual-grid, implicit, and sequentially coupled geomechanics-and-composition model for fractured reservoir simulation, SPE Journal, 25(04):2098-2118. DOI: 10.2118/201210-PA, 2020.
188. Lei, G., Q. Liao*, D. Zhang, S. Patil, A mechanistic model for permeability in deformable gas hydrate-bearing sediments, Journal of Natural Gas Science & Engineering, doi.org/10.1016/j.jngse.2020.103554, 2020.
187. Yang, W., D. Zhang*, and G. Lei, Experimental study on multiphase flow in fracture-vug medium using 3D Printing Technology and Visualization Techniques, Journal of Petroleum Science and Engineering, 10.1016/j.petrol.2020.107394, 2020. (Preprint available online at ESSOAr: 10.1002/essoar.10502278.1)
186. Li, S. Z. Kang, X.-T. Feng, Z. Pan, X. Huang, and D. Zhang*, Three‐dimensional hydrochemical model for dissolutional growth of fractures in karst aquifers, Water Resources Research, 56, e2019WR025631, https://doi.org/10.1029/2019WR025631, 2020.
185. Wu, T., D. Zhang*, & X. Li, A radial differential pressure decay method with micro-plug samples for determining the apparent permeability of shale matrix, Journal of Natural Gas Science & Engineering, 74: 103126, 10.1016/j.jngse.2019.103126, 2020.
184. Chen, Y., and D. Zhang*, Physics-constrained indirect supervised learning, Theoretical and Applied Mechanics Letters, 10(3): 155-160, http://dx.doi.org/10.1016/j.taml.2020.01.019, 2020.
183. Yang, W., J. Zeng, and D. Zhang*, Contrasting phase field method and pairwise force smoothed particle hydrodynamics method in simulating multiphase flow through fracture-vug medium, Journal of Natural Gas Science and Engineering, https://doi.org/10.1016/j.jngse.2020.103424, 2020.
182. Liao, Q., G. Lei, Z. Wei, D. Zhang*, and S. Patil, Efficient Analytical Upscaling Method for Elliptic Equations in Three-dimensional Heterogeneous Anisotropic Media, Journal of Hydrology, 10.1016/j.jhydrol.2020.124560, 2020.
181. Zhang, Y., Q. Wen, and D. Zhang*, A novel targeted-plugging and fracture-adaptable gel used as a diverting agent in fracturing, Energy Science & Engineering, 10.1002/ese3.513, 2020.
180. Teng, Y., and D. Zhang*, Comprehensive study and comparison of equilibrium and kinetic models in simulation of hydrate reaction in porous media, Journal of Computational Physics, 10.1016/j.jcp.2019.109094, 2020.
179. Chang, H., and D. Zhang*, Identification of Physical Processes via Combined Data-driven and Data-assimilation Methods, Journal of Computational Physics, DOI: 10.1016/j.jcp.2019.05.008, 393: 337-350, 2019.
178. Chang, H., and D. Zhang*, Machine Learning Subsurface Flow Equations from Data, Comp. Geosci., DOI: 10.1007/s10596-019-09847-2, 2019.
177. Chen, Y., H. Chang, J. Meng, and D. Zhang*, Ensemble Neural Networks (ENN): A Gradient-free Stochastic Method, Neural Networks, DOI: 10.1016/j.neunet.2018.11.009, 110, pp. 170-185, 2019.
176. Liu, X., and D. Zhang*, A Review of Phase Behavior Simulation of Hydrocarbons in Confined Space: Implications for Shale Oil and Shale Gas, Journal of Natural Gas Science and Engineering, 10.1016/j.jngse.2019.102901, 2019.
175. Zhao, J., D. Zhang*, T. Wu, H. Tang, Q. Xuan, Z. Jiang, and C. Dai, Multiscale approach for mechanical characterization of organic-rich shale and its application, International Journal of Geomechanics, 19(1): 04018180, DOI: 10.1061/ (ASCE)GM.1943-5622.0001281, 2019.
174. Li, S., X. Feng, D. Zhang*, and H. Tang, Coupled Thermo-hydro-mechanical Analysis of Stimulation and Production for Fractured Geothermal Reservoirs, Applied Energy, DOI: 10.1016/j.apenergy.2019.04.036 , 247: 40-59, 2019.
173. Lei, G., Q. Liao*, and D. Zhang, A new analytical model for flow in acidized fractured-vuggy porous media, Scientific Reports, 9(1), doi.org/10.1038/s41598-019-44802-2, 2019.
172. Liao, Q.*, L. Gang, D. Zhang, and S. Patil, Analytical Solution for Upscaling Hydraulic Conductivity in Anisotropic Heterogeneous Formations, Adv. Water Resour., DOI: 10.1016/j.advwatres.2019.04.011, 128: 97-116, 2019.
171. Yao, M., H. Chang, X. Li, and D. Zhang*, An Integrated Approach for the History Matching of Multiscale-Fractured Reservoir, SPE Journal, 24 (04): 1508–1525,doi.org/10.2118/195589-PA, 2019.
170. Zhou, S., D. Zhang*, H. Wang, and X. Li, A modified BET Equation to Investigate Supercritical Methane Adsorption Mechanisms in Shale, Marine & Petroleum Geology, DOI: 10.1016/j.marpetgeo.2019.04.036, 105: 284–292, 2019.
169. Liao, Q., L. Zeng, H. Chang, and D. Zhang, Efficient History Matching using the Markov-Chain Monte Carlo Method by Means of the Transformed Adaptive Stochastic Collocation Method, SPE Journal, 24 (04): 1468–1489, DOI:10.2118/194488-PA, 2019.
168. Zeng, J., H. Li*, and D. Zhang, Numerical Simulation of Proppant Transport in Propagating Fractures with the Multi-phase Particle-in-cell Method, Fuel, 245: 316-335, doi.org/10.1016/j.fuel.2019.02.056, 2019.
167. Li, S., and D. Zhang*, How Effective is Carbon Dioxide as an Alternative Fracturing Fluid? SPE J, SPE-194198-PA, https://doi.org/10.2118/194198-PA, 2019.
166. Tan, Y., Z. Pan*, X-T Feng, D. Zhang, L. D. Connell, and S. Li, Laboratory Characterisation of Fracture Compressibility for Coal and Shale Gas Reservoir Rocks: A Review, Int’l J. Coal Geol., 204:1-17, 10.1016/j.coal.2019.01.010, 2019.
165. Teng, Y., and D. Zhang*, Long-term Viability of Carbon Sequestration in Deep-sea Sediments, Science Advances, 4, DOI: 10.1126/sciadv.aao6588, 2018.
164. Yao, M., H. Chang, X. Li, and D. Zhang*, Tuning Fractures with Dynamic Data, Water Resources Research, 54, https://doi.org/10.1002/2017WR022019, 2018.
163. Li, S., and D. Zhang*, A Fully Coupled Model For Hydraulic-Fracture Growth During Multiwell-Fracturing Treatments: Enhancing Fracture Complexity, SPE PRODUCTION & OPERATIONS, SPE-182674-PA, 33 (02): 235-250，https://doi.org/10.2118/182674-MS, 2018.
162. Zhang, D., Y. Chen*, and J. Meng, Synthetic Well Logs Generation via Recurrent Neural Networks, Petroleum Exploration and Development, 45(4): 629-639, https://doi.org/10.1016/S1876-3804(18)30068-5, 2018. [Chinese version: 张东晓, 陈云天, 孟晋. 基于循环神经网络的测井曲线生成方法[J]. 石油勘探与开发, 2018, 45(4): 598-607.]
161. Li, X., X. Li*, and D. Zhang, Generalized Prism Grid: a pillar-based unstructured grid for simulation of reservoirs with complicated geological geometries, Comput. Geosci., 22: 6, 1561-1581, 10.1007/s10596-018-9774-0, 2018.
160. Jiang, Z., L. Zhao, and D. Zhang*, Study of Adsorption Behavior in Shale Reservoirs under High Pressure, J. Natural Gas Sci. & Eng., DOI: 10.1016/j.jngse.2017.11.009, 49: 275-285, 2018.
159. Tang, H., S. Li, and D. Zhang*, The Effect of Heterogeneity on Hydraulic Fracturing in shale, Journal of Petroleum Science and Engineering, 162: 292-308, https://doi.org/10.1016/j.petrol.2017.12.020, 2018.
158. Chang, H., and D. Zhang*, History Matching of Stimulated Reservoir Volume of Shale-Gas Reservoirs Using an Iterative Ensemble Smoother, SPE Journal, SPE-189436-PA, DOI: 10.2118/189436-PA, 2018.
157. Wu, T, X. Li, J. Zhao, and D. Zhang*, Multiscale Pore Structure and its Effect on Gas Transport in Organic-rich Shale, Water Resources Research, DOI: 10.1002/2017WR020780, 53(7): 5438–5450, 2017.
156. Chen, Y., J. Su, D. Zhang*, C. Liu, An adsorbed gas Estimation model for Shale Gas Reservoirs via Statistical Learning, Applied Energy, DOI: 10.1016/j.apenergy.2017.04.029, 197: 327-341, 2017.
155. Yang, T., X. Li, and D. Zhang*, Where Gas is Produced from a Shale Formation: A Simulation Study, J. Natural Gas Sci. & Eng., DOI: 10.1016/j.jngse.2017.06.015, 45: 860-870, 2107.
154. Lei, G., D. Zhang, W. Yang, and H. Wang, Mathematical Model for Wells Drilled in Large-Scale Partially Filled Cavity in Fractured-Cavity Reservoirs (缝洞型油藏井钻遇大尺度部分充填溶洞数学模型), Earth Science, 42(8): 1413-1420, doi: 10.3799/dqkx.2017.519, 2017.
153. Jiang, Z., D. Zhang*, J. Zhao, and Y. Zhou, Experimental Investigation of the Pore Structure of Triassic Terrestrial Shale in the Yanchang Formation, Ordos Basin, China, J. Natural Gas Sci. & Eng., DOI: 10.1016/j.jngse.2017.08.002, 2017.
152. Zhang, Z., H. Li*, and D. Zhang, Reservoir Characterization and Production Optimization using the Ensemble-based Optimization Method and Multi-layer Capacitance-resistive Models, J. Petrol. Sci. Eng., DOI: 10.1016/j.petrol.2017.06.020, 2017.
151. Liao, Q.*, D. Zhang, and H. Tchelepi, Nested sparse grid collocation method with delay and transformation for subsurface flow and transport problems, Adv. Water Resour., DOI: 10.1016/j.advwatres.2017.03.020, 104: 158-173, 2017.
150. Li, S., D. Zhang*, and X. Li., A New Approach to the Modeling of Hydraulic-Fracturing Treatments in Naturally Fractured Reservoirs, SPE Journal, Doi: 10.2118/181828-PA, 22(4): 1064-1081, 2017.
149. Chang, H.*, Q. Liao, and D. Zhang, Surrogate Model based Iterative Ensemble Smoother for Subsurface Flow Data Assimilation, Adv. Water Resour., DOI: 10.1016/j.advwatres.2016.12.001, 100: 96-108, 2017.
148. Liao, Q.*, D. Zhang, and H. Tchelepi, A Two-stage Adaptive Stochastic Collocation Method on Nested Sparse Grids for Multiphase Flow in Randomly Heterogeneous Porous Media, J. Comp. Phys., DOI: 10.1016/j.jcp.2016.10.061, 330: 828-845, 2017.
147. Wu, T., and D. Zhang*, Impact of Adsorption on Gas Transport in Nanopores, Scientific Reports, 6:23629, DOI: 10.1038/srep23629, 2016.
146. Li, X., Y. Xue, M. Zou, D. Zhang, A. Cao*, and H. Duan*, Direct Oil Recovery from Saturated Carbon Nanotube Sponges, ACS Appl. Mater. Interfaces, DOI: 10.1021/acsami.6b01623, 8(19): 12337–12343, 2016.
145. Li, S., X. Li, and D. Zhang*, A Fully Coupled Thermo-Hydro-Mechanical, Three-Dimensional Model for Hydraulic Stimulation Treatments, J. Natural Gas Sci. & Eng., DOI: 10.1016/j.jngse.2016.06.046, 34: 64-84, 2016.
144. Dai, C., L. Xue*, D. Zhang, and A. Guadagnini, Data-worth Analysis through Probabilistic Collocation-based Ensemble Kalman Filter, J. Hydrol., DOI: 10.1016/j.jhydrol.2016.06.037, 540: 488–503, 2016.
143. Zeng, J., H. Li*, and D. Zhang, Numerical Simulation of Proppant Transport in Hydraulic Fracture with Upscaling CFD-DEM Method, J. Natural Gas Sci. & Eng., doi:10.1016/j.jngse.2016.05.030, 33: 264-277, 2016.
142. Liao, Q., and D. Zhang*, Probabilistic Collocation Method for Strongly Nonlinear Problems: 3. Transform by Time, Water Resour. Res., 52, doi:10.1002/2015WR017724, 2016.
141. Zhang, D.*, T. Yang, T. Wu, X. Li, and J. Zhao, Recovery Mechanisms and Key Issues in Shale Gas Development (in Chinese), Chinese Science Bulletin, doi:10.1360/N972015-00300, 61(1): 62-71, 2016.(页岩气开发机理和关键问题，科学通报)
140. Chang, H., Q. Liao, and D. Zhang*, Benchmark Problems for Subsurface Flow Uncertainty Quantification, J. Hydrol., doi:10.1016/j.jhydrol.2015.09.040, 531:168-186, 2015.
139. Li, X., and D. Zhang*, Li, S., A Multi-continuum Multiple Flow Mechanism Simulator for Unconventional Oil and Gas Recovery, J. Natural Gas Sci. & Eng., doi: 10.1016/j.jngse.2015.07.005, 652-669, 2015.
138. Lu, L., and D. Zhang*, Assisted History Matching for Fractured Reservoirs using Hough Transform based Parameterization, SPE Journal, http://dx.doi.org/10.2118/176024-PA, 20(5): 942-961, 2015.
137. Chen, Y., Q. Kang, Q. Cai*, M. Wang*, and D. Zhang, Lattice Boltzmann Simulation of Particle Motion in Binary Immiscible Fluids, Communications in Computational Physics, 18(3): 757-786, DOI: 10.4208/cicp.101114.150415a, 2015.
136. Zhang, D.*, and T. Yang, Environmental Impacts of Hydraulic Fracturing in Shale Gas Development in the United States, Petroleum Exploration and Development, 42(6): 876-883, https://doi.org/10.1016/S1876-3804(15)30085-9, 2015. (页岩气开发水力压裂技术的环境影响, 石油勘探与开发, 2015)
135. Chang, H.*, and D. Zhang, Jointly Updating the Mean Size and Spatial Distribution of Facies in Reservoir History Matching, Comp. Geosci., DOI: 10.1007/s10596-015-9478-7, 19(4): 727-746, 2015.
134. Liao, Q., and D. Zhang*, Data Assimilation for Strongly Nonlinear Problems by Transformed Ensemble Kalman Filter, SPE Journal, 20(1): 202-221, DOI: 10.2118/173893-PA, 2015.
133. Yang, T., X. Li, and D. Zhang*, Quantitative Dynamic Analysis of Gas Desorption Contribution to Production in Shale Gas Reservoirs, J. Uncon. Oil & Gas Resour., doi:10.1016/j.juogr.2014.11.003, 9:18-30, 2015.
132. Dai, C., H. Li, D. Zhang*, and L. Xue, Efficient Data-Worth Analysis for the Selection of Surveillance Operation in a Geologic CO2 Sequestration System, Greenhouse Gases: Science and Technology, DOI: 10.1002/ghg.1492, 5(5):513-529, 2015.
131. Zhang, Z., H. Li*, and D. Zhang, Water Flooding Performance Prediction by Multi-Layer Capacitance-Resistive Models Combined with the Ensemble Kalman Filter, J. Petrol. Sci. Eng., 1-19, 10.1016/j.petrol.2015.01.020, https://doi.org/10.1016/j.petrol.2015.01.020, 2015.
130. Liao, Q., and D. Zhang*, Constrained Probabilistic Collocation Method for Uncertainty Quantification of Geophysical Models, Comp. Geosci., DOI: 10.1007/s10596-015-9471-1, 19(2): 311-326, 2015.
129. Li, W., G. Lin, and D. Zhang*, An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling, Journal of Computational Physics, 258C: 752-772, https://doi.org/10.1016/j.jcp.2013.11.019, 2014.
128. Xue, L.*, and D. Zhang, A Multimodel Data Assimilation Framework via the Ensemble Kalman Filter, Water Resour. Res., 50(5): 4197-4219, DOI: 10.1002/2013WR014525, 2014.
127. Liao, Q., and D. Zhang*, Probabilistic Collocation Method for Strongly Nonlinear Problems: 2. Transform by Displacement, Water Resour. Res., DOI: 10.1002/2014WR016238, 2014.
126. Xue, L.*, D. Zhang, A. Guadagnini, and S.P. Neuman, Multimodel Bayesian Analysis of Groundwater Data Worth, Water Resour. Res., DOI: 10.1002/2014WR015503, 2014.
125. Ping, J., and D. Zhang*, History Matching of Channelized Reservoirs with Vector-based Level Set Parameterization, SPE Journal, 19(3): 514-529, https://doi.org/10.2118/169898-PA, 2014.
124. Dai, C., H. Li, and D. Zhang*, Efficient and Accurate Global Sensitivity Analysis for Reservoir Simulations Use of the Probabilistic Collocation Method, SPE Journal, 19(4): 621-634, https://doi.org/10.2118/167609-PA, 2013.
123. Chang, H.*, and D. Zhang, History Matching of Statistically Anisotropic Fields Using the Karhunen-Loeve Expansion-based Global Parameterization Technique, Comp. Geosci., 18(2): 265-282, DOI: 10.1007/s10596-014-9409-z, 2014.
122. Li, X. and D. Zhang*, A Backward Automatic Differentiation Framework for Reservoir Simulation, Comp. Geosci., 10.1007/s10596-014-9441-z, 18:1009-1022, DOI: 10.1016/j.piutam.2014.01.027, 2014.
121. Chang, H.*, and D. Zhang, History Matching of Facies Distribution with Varying Mean Lengths or Different Principle Correlation Orientations, J Petrol. Sci. Eng., DOI: 10.1016/j.petrol.2014.09.029, 124: 275-292, 2014.
120. Zhang, D.*, and J. Song, Mechanisms for Geological Carbon Sequestration, Procedia IUTAM, 10: 319-327, https://doi.org/10.1016/j.piutam.2014.01.027, 2014.
119. Ping, J., and D. Zhang*, History Matching of Fracture Distributions by Ensemble Kalman Filter Combined with Vector Based Level Set Parameterization, Journal of Petroleum Science and Engineering, https://doi.org/10.1016/j.petrol.2013.04.018, 2013.
118. Song, J., and D. Zhang*, Comprehensive Review of Caprock-Sealing Mechanisms for Geologic Carbon Sequestration, Environmental Science and Technology, 47: 9-22, https://doi.org/10.1021/es301610p, 2013.
117. Sun A.Y.*, M. Zeidouni, J.-P. Nicot, Z. Lu, and D. Zhang, Assessing Leakage Detectability at Geologic CO2 Sequestration Sites using the Probabilistic Collocation Method, Adv. Water Resour, DOI: 10.1016/j.advwatres.2012.11.017, 2013.
116. Wei, Z., and D. Zhang*, A Fully Coupled Multiphase Multicomponent Flow and Geomechanics Model for Enhanced Coalbed-Methane Recovery and CO2 Storage, SPE Journal, DOI: 10.2118/163078-PA, 18(3): 448-467, 2013.
115 .Li, H.*, and D. Zhang, Stochastic Representation and Dimension Reduction for Non-Gaussian Random Fields: Review and Reflection, Stochastic Environmental Research and Risk Assessment, DOI: 10.1007/s00477-013-0700-7, 27:1621–1635, 2013.
114. Zhang*, D., T. Yang, An Overview of Shale-Gas Production, ACTA PETROLEI SINICA, 2013, 34 (4): 792-801. DOI: 10.7623/syxb201304023. (张东晓, 杨婷云. 页岩气开发综述. 石油学报, 2013, 34 (4): 792－801.)
113. Xie, X.*, and D. Zhang, A Partitioned Update Scheme for State-Parameter Estimation of Distributed Hydrologic Models based on the Ensemble Kalman Filter, Water Resour. Res., VOL. 49, 7350–7365, DOI: 10.1002/2012WR012853, 2013.
112. Liao, Q., and D. Zhang*, Probabilistic Collocation Method for Strongly Nonlinear Problems: 1. Transform by location, Water Resour. Res., 49(12), 7911-7928, DOI: 10.1002/2013WR014055, 2013.
111. Jahangiri, H.R., and D. Zhang*, Ensemble Based Co-optimization of Carbon Dioxide Sequestration and Enhanced Oil Recovery, International Journal of Greenhouse Gas Control, 22-33, https://doi.org/10.1016/j.ijggc.2012.01.013, 2012.
110. Zeng, L., L. Shi*, D. Zhang, and L. Wu, A Sparse Grid Based Bayesian Method for Contaminant Source Identification, Adv. Water Resour., 37:1-9, https://doi.org/10.1016/j.advwatres.2011.09.011, 2012.
109. Shi, L., L. Zeng*, D. Zhang, and J. Yang, Multiscale-Finite-Element-Based Ensemble Kalman Filter for Large-Scale Groundwater Flow, J. Hydrology, 468: 22-34, https://doi.org/10.1016/j.jhydrol.2012.08.003, 2012.
108. Li, Z., D. Zhang, and X. Li*, Tracking Colloid Transport in Real Pore Structures: Comparisons with Correlation Equations and Experimental Observations, Water Resour. Res., 48, https://doi.org/10.1029/2012WR011847, 2012.
107. Zeng, L., H. Chang, and D. Zhang*, A Probabilistic Collocation Based Kalman Filter for History Matching, SPE Journal, SPE-140737-PA, 294-306, DOI:10.2118/140737-PA, 2011.
106. Li, H., P. Sarma, and D. Zhang*, A Comparative Study of the Probabilistic-Collocation and Experimental Design Methods for Petroleum-Reservoir Uncertainty Quantification, SPE Journal, SPE-140738-PA, 429-439, https://doi.org/10.2118/140738-PA, 2011.
105.Jahangiri, H.R., and D. Zhang*, Effect of Spatial Heterogeneity on Plume Distribution and Dilution during CO2 Sequestration, International Journal of Greenhouse Gas Control, DOI: 10.1016/j.ijggc.2010.10.003, 5:281-293, 2011.
104. Jafroodia N.*, and D. Zhang, New Method for Reservoir Characterization and Optimization Using CRM-EnOpt Approach, J Pet. Sci. Eng., doi:10.1016/j.petrol.2011.02.011, 77: 155-171, 2011.
103.Chen, Y., Q. Kang, Q.D. Cai*, and D. Zhang, Lattice Boltzmann Method on Quadtree Grids, Physical Review E, 83, 026707, https://doi.org/10.1103/PhysRevE.83.026707, 2011.
102. Xie, X.H.*, and D. Zhang, Data Assimilation for Distributed Hydrological Catchment Modeling via Ensemble Kalman Filter, Adv. Water Resources, doi:10.1016/j.advwatres.2010.03.012, 33: 678–690, 2010. (Selected as one of the “Top 100 Most Cited Chinese Papers Published in International Journals”)
101. Zhang, D.*, L. Shi, H. Chang, and J. Yang, A Comparative Study of Numerical Approaches to Risk Assessment of Contaminant Transport, Stochastic Environmental Research and Risk Assessment, DOI: 10.1007/s00477-010-0400-5, 2010.
100. Wei, Z., and D. Zhang*, Coupled Fluid-Flow and Geomechanics for Triple-Porosity/Dual-Permeability Modeling of Coalbed Methane Recovery, International Journal of Rock Mechanics and Mining Sciences, 47: 1242–1253, https://doi.org/10.1016/j.ijrmms.2010.08.020, 2010.
99. Chang, H., D. Zhang*, and Z. Lu, History Matching of Facies Distribution with the EnKF and Level Set Parameterization, J. Comp. Phys., 229:8011-8030, DOI: 10.1016/j.jcp.2010.07.005, 2010.
98. Li, X.*, Z. Li, and D. Zhang*, Role of Low Flow and Backward Flow Zones on Colloid Transport in Pore Structures Derived from Real Porous Media, Environmental Science & Technology, 44(13), 4936-4942, DOI: 10.1021/es903647g, 2010.
97. Chen, C., and D. Zhang*, Pore-Scale Simulation of Density-Driven Convection in Fractured Porous Media during Geological CO2 Sequestration, Water Resour. Res., 46, W11527, DOI: 10.1029/2010WR009453, 2010.
96. Li, Z., D. Zhang, and X. Li*, Tracking Colloid Transport in Porous Media Using Discrete Flow Fields and Sensitivity of Simulated Colloid Deposition to Space Discretization, Environmental Science & Technology, 44(4), 1274-1280, DOI: 10.1021/es9027716, 2010.
95. Chang, H., Y. Chen, and D. Zhang*, Data Assimilation of Coupled Fluid Flow and Geomechanics via Ensemble Kalman Filter, SPE Journal, SPE-118963-MS, 15(2): 382-394, https://doi.org/10.2118/118963-MS, 2010.
94. Zeng, L., and D. Zhang*, A Stochastic Collocation Based Kalman Filter for Data Assimilation, Computational Geosciences, DOI: 10.1007/s10596-010-9183-5, 2010.
93. Wei, Z.*, and D. Zhang, Coupled Fluid Flow and Geomechanics in Coalbed Methane Recovery Study, Mod. Phy. Lett. B, 24(13): 1291-1294, DOI: 10.1142/S0217984910023451, 2010.
92. Chen, C., A. Packman, D. Zhang, and J. -F. Gaillard, A Multi-scale Investigation of Interfacial Transport, Pore Fluid Flow, and Fine Particle Deposition in a Sediment Bed, Water Resour. Res., vol. 46, W11560, DOI: 10.1029/2009WR009018, 2010.
91. Shi, L., D. Zhang*, L. Lin, and J. Yang, A Multiscale Probabilistic Collocation Method for Subsurface Flow in Heterogeneous Media, Water Resour. Res., VOL. 46, W11562, DOI: 10.1029/2010WR009066, 2010.
90. Chen, Y., D. Oliver, and D. Zhang, Efficient Ensemble-based Closed-Loop Production Optimization, SPE Journal, 10.2118/112873-PA, 14(4): 634-645, DOI:10.2118/112873-PA, 2009.
89. Chen, Y.*, D. Oliver, and D. Zhang, Data Assimilation for Nonlinear Problems by Ensemble Kalman Filter with Reparameterization, J. Petroleum Science & Engineering, 66(1-2): 1-14, https://doi.org/10.1016/j.petrol.2008.12.002, 2009.
88. Rapaka, S.*, R. Pawar, P. Stauffer, D. Zhang, S. Chen, Onset of Convection Over a Transient Base-State in Anisotropic and Layered Porous Media, J. Fluid Mech., 641: 227-244, DOI: 10.1017/S0022112009991479, 2009.
87. Shi, L.*, J. Yang, D. Zhang, and H. Li, Probabilistic Collocation Method for Unconfined Flow in Heterogeneous Media, J. Hydrology, 365(1-2):4-10, DOI: 10.1016/j.jhydrol.2008.11.012, 2009.
86. Li, W., Z. Lu, D. Zhang*, Stochastic Analysis of Unsaturated Flow with Probabilistic Collocation Method, Water Resour. Res., 45, W08425, DOI: 10.1029/2008WR007530, 2009.
85. Li, H., and D. Zhang*, Efficient and Accurate Quantification of Uncertainty for Multiphase Flow with Probabilistic Collocation Method, SPE Journal, DOI: 10.2118/114802-PA, 665－679, 2009.
84. Chang, H.*, and D. Zhang*, A Comparative Study of Stochastic Collocation Methods for Flow in Spatially Correlated Random Fields, Commun. Comput. Phys., 6(3): 509-535, https://doi.org/2009-CiCP-7690, 2009.
83. Shi, L.S.*, J.Z. Yang, and D. Zhang, A Stochastic Approach to Nonlinear Unconfined Flow Subject to Multiple Random Fields, Stochastic Environmental Research and Risk Assessment, 23: 823-835, DOI: 10.1007/s00477-008-0261-3, 2009.
82. Shi, L.*, J. Yang, and D. Zhang, Evaluating the Uncertainty of Darcy Velocity with Sparse Grid Collocation Method, Science in China Series E: Technological Sciences, 52(11): 3270-3278, DOI: 10.1007/s11431-009-0353-4, 2009.
81. Lu, G., D. J. DePaolo, Q. Kang, and D. Zhang, Lattice Boltzmann Simulation of Snow Crystal Growth in Clouds, J. Geophys. Res., 114, D07305, DOI: 10.1029/2008JD011087, 2009.
80. Chen, C., and D. Zhang*, Lattice Boltzmann Simulation of the Rise and Dissolution of Two-Dimensional Immiscible Droplets, Physics of Fluids, 21, 103301, DOI: 10.1063/1.3253385, 2009.
79. Feng, X.T.*, W.X. Ding, and D. Zhang, Multi-Crack Interaction in Limestone Subject to Stress and Flow of Chemical Solutions, International Journal of Rock Mechanics and Mining Sciences, 46(1): 159-171, DOI: 10.1016/j.ijrmms.2008.08.001, 2009.
78. Liu, G.*, Y. Chen, and D. Zhang, Investigation of Flow and Transport Processes at the MADE Site Using Ensemble Kalman Filter, Adv. Water Resour., doi:10.1016/j.advwatres.2008.03.006, 31: 975–986, 2008.
77. Rapaka, S., S. Chen, R. Pawar, P.H. Stauffer, and D. Zhang, Non-modal Growth of Perturbations in Density-driven Convection in Porous Media, J Fluid Mech., vol. 609, pp. 285–303, DOI: 10.1017/S0022112008002607, 2008.
76. Gainis, B., H. Klie*, M.F. Wheeler, T. Wildey, I. Yotov, and D. Zhang, Stochastic Collocation and Mixed finite elements for Flow in Porous Media, Comput. Methods Appl. Mech. Engrg., 197: 3547–3559, DOI: 10.1016/j.cma.2008.03.025, 2008.
75. Ding, Y., T. Li*, D. Zhang, and P. Zhang, Adaptive Stroud stochastic collocation method for flow in random porous media via Karhunen-Loève expansion, Communications in Comp. Phys., 4(1):102-123, 2008.
74. Ding, G., J.J. Jiao*, and D. Zhang, Modelling Study on the Impact of Deep Building Foundations on the Groundwater System, Hydrol. Process., 22(12): 1857-1865, DOI: 10.1002/hyp.6768, 2008.
73. Lu, Z., Zhang, D., and B. Robinson, Explicit Analytical Solutions for One-Dimensional Steady State Flow in Layered, Heterogeneous Unsaturated Soils under Random Boundary Conditions, Water Resour. Res., 43, W09413, DOI:10.1029/2008WR006813, 2008.
72. Li, H., and D. Zhang*, Probabilistic Collocation Method for Flow in Porous Media: Comparisons with Other Stochastic Methods, Water Resour. Res., 43, W09409, DOI:10.1029/2006WR005673, 2007.
71. Kang, Q., P.C. Lichtner, and D. Zhang, An Improved Lattice Boltzmann Model for Multi-Component Reactive Transport in Porous Media at the Pore Scale, Water Resour. Res., 43, W12S14, DOI:10.1029/2006WR005551, 2007.
70. Zhang, D.*, Z. Lu, and Y. Chen, Dynamic Reservoir Data Assimilation with an Efficient, Dimension-Reduced Kalman Filter, SPE Journal, 12(1), 108-117, DOI: 10.2118/95277-PA, 2007.'69.Liu, G., Z. Lu, and D. Zhang*, Stochastic Uncertainty Analysis for Solute Transport in Randomly Heterogeneous Media Using a Karhunen-Loève-Based Moment Equation Approach, Water Resour. Res., 43, W07427, DOI:10.1029/2006WR005193, 2007.
68.Xu, X., S. Chen, and D. Zhang*, Reply to comment of Nield, Adv. Water Resour., 30 (3): 698-699, DOI: 10.1016/j.advwatres.2006.08.002,2007.
67.Lu, Z., and D. Zhang, Stochastic Simulations for Flow in Nonstationary Randomly Heterogeneous Porous Media Using a KL-based Moment-equation Approach, SIAM Multiscale Modeling and Simulation, 6(1), 228-245, DOI. 10.1137/060665282, 2007.
66. Chen, Y., and D. Zhang*, Data Assimilation for Transient Flow in Geologic Formations via Ensemble Kalman Filter, Adv. Water Resour., doi:10.1016/j.advwatres.2005.09.007, 29, 1107–1122, 2006. (ISI Highly Cited Paper)
65. Xu, X., S. Chen, and D. Zhang*, Convective Stability Analysis of the Long-Term Storage of Carbon Dioxide in Deep Saline Aquifers, Adv. Water Resour., doi:10.1016/j.advwatres.2005.05.008, 29(3):397-407, 2006.
64. Kang, Q.*, P.C. Lichtner, and D. Zhang, Lattice-Boltzmann Pore-Scale Model for Multi-component Reactive Transport in Porous Media, J. Geophy. Res., VOL. 111, B05203, DOI: 10.1029/2005JB003951, 2006.
63. Chen, M., A. Keller, D. Zhang, Z. Lu, and G.A. Zyvoloski, A Stochastic Analysis of Transient Two-Phase Flow in Heterogeneous Porous Media, Water Resour. Res., 42(3), W03425, DOI: 10.1029/2005WR004257, 2006.
62. Lu, Z., and D. Zhang*, Accurate, Efficient Quantification of Uncertainty for Flow in Heterogeneous Reservoirs using the KLME Approach, SPE Journal, 11(2), 239-247, DOI: 10.2118/93452-MS, 2006.
61. Liu, G., D. Zhang, and Z. Lu, Stochastic Uncertainty Analysis for Unconfined Flow Systems, Water Resour. Res., VOL. 42, W09412, DOI: 10.1029/2005WR004766, 2006.
60. Kang, Q., D. Zhang, and S. Chen, Displacement of a Three-Dimensional Immiscible Droplet in a Duct, J. Fluid Mech., 545: 41-66, DOI: 10.1017/S0022112005006956, 2005.
59. Kang, Q., I.N. Tsimpanogiannis*, D. Zhang, and P. Lichtner, Numerical Modeling of Pore-scale Phenomena during CO2 Sequestration in Oceanic Sediments, Fuel Processing Technology, 86:1647-1665, https://doi.org/10.1016/j.fuproc.2005.02.001, 2005.
58. Chen, M., D. Zhang, A. Keller, and Z. Lu, A Stochastic Analysis of Steady State Two-Phase Flow in Heterogeneous Media, Water Resour. Res., Vol. 41, W01006, DOI: 10.1029/2004WR003412, 2005.
57. Lu, Z., and D. Zhang, Analytical Solutions of Statistical Moments for Transient Flow in Two-Dimensional Bounded, Randomly Heterogeneous Media, Water Resour. Res., Vol.41, W01016, DOI: 10.1029/2004WR003389, 2005.
56. Zhang, D.*, and Z. Lu, An Efficient, High-Order Perturbation Approach for Flow in Random Porous Media via Karhunen-Loève and Polynomial Expansions, J. of Computational Physics, 194(2), 773-794, doi: 10.1016/j.jcp.2003.09.015, 2004.
55.Kang, Q., D. Zhang, and P. Lichtner, and I. Tsimpanogiannis, Lattice Boltzmann Model for Crystal Growth from Supersaturated Solution, Geophysical Research Letters, DOI: 10.1029/2004GL021107, 31, L21604(1-5), 2004.
54. Lu, Z.*, and D. Zhang*, A Comparative Study on Uncertainty quantification for Flow in Randomly Heterogeneous Media Using Monte Carlo Simulations, the Conventional and KL-based Moment-equation Approaches, SIAM Journal on Scientific Computing, 26(2), 558-577, doi:10.1137/S1064827503426826, 2004.
53. Kang, Q.*, D. Zhang, and S. Chen, Immiscible Displacement in a Channel: Simulations of Fingering in Two Dimensions, Adv. Water Resources, 27(1), 13-22, https://doi.org/10.1016/j.advwatres.2003.10.002, 2004.
52. Lu, Z.*, and D. Zhang, Conditional Simulations of Flow in Randomly Heterogeneous Porous Media Using a KL-based Moment-equation Approach, Adv. Water Resources, 27:859-874, https://doi.org/10.1016/j.advwatres.2004.08.001, 2004.
51. Zhang, D., and Q. Kang, Pore Scale Simulation of Solute Transport in Fractured Porous Media, Geophysical Research Letters, vol.31(6), DOI:10.1029/2004GL019886, 2004.
50. Lu, Z.*, and D. Zhang, Analytical Solutions to Steady State Unsaturated Flow in Layered, Randomly Heterogeneous Soils via Kirchhoff Transformation, Adv. Water Resources, 27:775-784, DOI:10.1016/j.advwatres.2004.05.007, 2004.
49. Zhang, Y.K.*, and D. Zhang, Forum: The State of Stochastic Hydrology, Stochastic Environmental Research and Risk Assessment, 18(4):265, DOI:10.1007/s00477-004-0190-8, 2004.
48. Zhang, D.*, and Z. Lu, Stochastic Delineation of Well Capture Zones, Stochastic Environmental Research and Risk Assessment, 18(1), 39-46, DOI:10.1007/s00477-003-0159-z, 2004.
47. Hu, B.X.*, J. Wu, and Zhang, D., A Numerical Method of Moments for Solute Transport in Physically and Chemically Nonstationary Formations: Linear Equilibrium Sorption with Random Kd, Stochastic Environmental Research and Risk Assessment, 18(1), 22-30, DOI: 10.1007/s00477-003-0161-5,2004.
46. Sun, A., and D. Zhang, A Solute Flux Approach to Transport through Bounded, Unsaturated Heterogeneous Porous Media, Vadose Zone Journal, 3:513-526,https://doi.org/10.2136/vzj2004.0513, 2004.
45. Yang, J., D. Zhang*, and Z. Lu, Stochastic Analysis of Saturated-Unsaturated Flow in Heterogeneous Media by Combining Karhunen-Loève Expansion and Perturbation Method, J. Hydrology, vol.294, 18-38, https://doi.org/10.1016/j.jhydrol.2003.10.023,2004.
44. Kang, Q., D. Zhang*, and S. Chen, Simulation of Dissolution and Precipitation in Porous Media, J. Geophysical Research-Solid Earth, 108(B10), 2505, DOI: 10.1029/2003JB002504, 2003.
43. Lu, Z.*, and D. Zhang, On Importance Sampling Monte Carlo Approach to Uncertainty Analysis of Flow and Transport in Porous Media, Adv. Water Resources, 26(11), 1177-1188, DOI: 10.1016/S0309-1708(03)00106-4, 2003.
42. Li, L., H.A. Tchelepi*, and D. Zhang, Perturbation-based Moment Equation Approach for Flow in Heterogeneous Porous Media: Applicability Range and Analysis of High-Order Terms, J. of Computational Physics, doi: 10.1016/S0021-9991(03)00186-4, 188(1), pp 296 - 317, 2003.
41.Lu, Z., and D. Zhang, On Stochastic Study of Well Capture Zones in Bounded, Randomly Heterogeneous Media, Water Resour. Res., 39(4), DOI: 10.1029/2002WR001633, 2003.
40.Lu, Z., and D. Zhang, Solute Spreading in Nonstationary Flows in Bounded Heterogeneous Unsaturated-Saturated Media, Water Resour. Res., 39(3), 1049, DOI: 10.1029/2001WR000908, 2003.
39. Wu, J., B.X. Hu*, D. Zhang, and C. Shirley, A Three-Dimensional Numerical Method of Moments for Groundwater Flow and Solute Transport in a Nonstationary Conductivity Field, Adv. Water Resources, 26(11), 1149-1169, DOI: 10.1016/j.advwatres.2003.08.002,2003.
38. Wu J., B.X. Hu*, and D. Zhang, Applications of Nonstationary Stochastic Theory to Solute Transport in Multi-scale Geological Media, Journal of Hydrology, 275:208-228,https://doi.org/10.1016/S0022-1694(03)00044-1, 2003.
37. Hu, B.X.*, J. Wu, A.K. Panorska, D. Zhang, and C. He, Stochastic Study on Groundwater Flow and Solute Transport in Porous Medium with Multi-Scale Heterogeneity, Adv. Water Resources, 26:541-560, DOI: 10.1016/S0309-1708(03)00003-4, 2003.
36. Kang, Q., D. Zhang, and S. Chen, Displacement of a Two-Dimensional Immiscible Droplet in a Channel, Physics of Fluids, 14(9), 3203-3214, DOI: 10.1063/1.1499125,2002.
35. Kang, Q., D. Zhang, S. Chen, and X. He, Lattice Boltzmann Simulations of Chemical Dissolution in Porous Media, Physical Review E, 65(3), DOI: 10.1103/PhysRevE.65.036318, 2002.
34. Lu, Z., and D. Zhang*, On Stochastic Modeling of Flow in Multimodal Heterogeneous Formations, Water Resour. Res., 38(10), DOI: 10.1029/2001WR001026, 2002.
33. Kang, Q., D. Zhang, and S. Chen, Unified Lattice Boltzmann Method for Flow in Multiscale Porous Media, Physical Review E, 66(11), 056307, DOI: 10.1103/PhysRevE.66.056307, 2002.
32. Zhang, D.*, and Z. Lu, Stochastic Analysis of Flow in a Heterogeneous Unsaturated-Saturated System, Water Resour. Res., 38(2), DOI: 10.1029/2001WR000515, 2002.
31.Valentine, G., D. Zhang, and B.A. Robinson, Modeling Complex, Nonlinear Geological Processes, Annual Review of Earth and Planetary Sciences, 30: 35-64, DOI: 10.1146/annurev.earth.30.082801.150140, 2002.
30. Lu, Z., and D. Zhang*, Stochastic Analysis of Transient Flow in Heterogeneous, Variably Saturated Porous Media: The van Genuchten-Mualem Constitutive Model, Vadose Zone Journal, 1(1), 137-149, DOI: 10.2113/1.1.137,2002.
29. Lu, G., and D. Zhang*, Nonstationary Stochastic Analysis of Flow in a Heterogeneous Semiconfined Aquifer, Water Resour. Res., 38(8), DOI: 10.1029/2001WR000546, 2002.
28. Hu, B.X., H. Huang, and D. Zhang, Stochastic Analysis of Solute Transport in Heterogeneous, Dual-Permeability Media, Water Resour. Res., 38(9), DOI: 10.1029/2001WR000442, 2002.
27. Zhang, D.*, R. Zhang, S. Chen, and W.E. Soll, Pore Scale Study of Flow in Porous Media: Scale Dependency, REV, and Statistical REV, Geophysical Research Letters, 27(8), 1195-1198, DOI: 10.1029/1999GL011101, 2000.
26. Zhang, D.*, R. Andricevic, A.Y. Sun, X.B. Hu, and G. He, Solute Flux Approach to Transport Through Spatially Nonstationary Flow in Porous Media, Water Resour. Res., 36(8), 2107-2120, DOI: 10.1029/2000WR900085, 2000.
25. Zhang, D.*, and A.Y. Sun, Stochastic Analysis of Transient Saturated Flow through Heterogeneous Fractured Porous Media: A Double-Permeability Approach, Water Resour. Res., 36(4), 865-874, DOI: 10.1029/2000WR900003, 2000.
24. Zhang, D.*, L. Li, and H.A. Tchelepi, Stochastic Formulation for Uncertainty Analysis of Two-Phase Flow in Heterogeneous Reservoirs, SPE Journal, 5(1), 60-70, DOI: 10.2118/59802-PA, 2000.
23. Sun, A.Y., and D. Zhang*, Prediction of Solute Spreading During Vertical Infiltration in Unsaturated, Bounded Heterogeneous Porous Media, Water Resour. Res., 36(3), 715-723, DOI: 10.1029/1999WR900344, 2000.
22. Zhang, D.*, Nonstationary Stochastic Analysis of Transient Unsaturated Flow in Randomly Heterogeneous Media, Water Resour. Res., Vol.35, No.4, DOI: 10.1029/1998WR900126, 1999.
21. Zhang, D.*, and C.L. Winter, Moment-Equation Approach to Single Phase Fluid Flow in Heterogeneous Reservoirs, SPE Journal, Vol.4, No.2, DOI: 10.2118/56842-PA, 1999.
20. Harter, T., and D. Zhang, Water Flow and Solute Spreading in Heterogeneous Soils with Spatially Variable Water Content, Water Resour. Res., Vol.35, No.2, DOI: 10.1029/1998WR900027, 1999.
19. Zhang, D.*, and H. Tchelepi, Stochastic Analysis of Immiscible Two-Phase Flow in Heterogeneous Media, SPE Journal, 4(4), 380-388, DOI:10.2118/59250-PA, 1999.
18. Zhang, D.*, Quantification of Uncertainty for Fluid Flow in Heterogeneous Petroleum Reservoirs, Physica D, 133, 488-497,https://doi.org/10.1016/S0167-2789(99)00073-1, 1999.
17. Zhang, D.*, Numerical Solutions to Statistical Moment Equations of Groundwater Flow in Nonstationary, Bounded, Heterogeneous Media, Water Resour. Res., Vol.34, No.3, DOI: 10.1029/97WR03607,1998.
16. Zhang, D.*, and C.L. Winter, Nonstationary Stochastic Analysis of Steady-State Flow through Variably Saturated, Heterogeneous Media, Water Resour. Res., Vol.34, No.5, https://doi.org/10.1029/2001WR000546, 1998.
15. Zhang, D.*, T. Wallstrom, and L. Winter, Stochastic Analysis of Steady-State Unsaturated Flow in Heterogeneous Media: Comparison of Brooks-Corey and Gardner-Russo Models, Water Resour. Res., Vol.34, No.6, DOI: 10.1029/98WR00317, 1998.
14. Xin, J., and D. Zhang*, Stochastic Analysis of Biodegradation Fronts in One-Dimensional Heterogeneous Porous Media, Adv. Water Resources, Vol.22, No.2, DOI: 10.1016/S0309-1708(98)00007-4, 1998.
13. Zhang, D.*, Conditional Stochastic Analysis of Multiphase Transport in Randomly Heterogeneous, Variably Saturated Media, Transport in Porous Media, Vol.27, No.3, DOI: 10.1023/A:1006570219058, 1997.
12. Zhang, Y.-K., and D. Zhang, Time-Dependent Dispersion of Nonergodic Solute Transport in Two-Dimensional Heterogeneous Porous Media, ASCE Journal of Hydrologic Engineering, Vol.2, No.2, DOI: 10.1061/(ASCE)1084-0699(1997)2:2(91), 1997.
11. Hsu, K.-C., D. Zhang, and S.P. Neuman, Higher-Order Effects on Flow and Transport in Randomly Heterogeneous Porous Media, Water Resour. Res., Vol.32, No.3, DOI: 10.1029/95WR03492, 1996.
10. Zhang, D., and S.P. Neuman, Effect of Local Dispersion on Solute Transport in Randomly Heterogeneous Porous Media, Water Resour. Res., Vol.32, No.9, DOI: 10.1029/96WR01335, 1996.
9. Zhang, D., and S.P. Neuman, Head and Velocity Covariances Under Quasi-Steady State Flow and Their Effects on Advective Transport, Water Resour. Res., Vol.32, No.1, DOI: 10.1029/95WR02766, 1996.
8. Zhang, Y.-K., D. Zhang, and J. Lin, Non-ergodic Solute Transport in Three-Dimensional Heterogeneous Isotropic Aquifers, Water Resour. Res., Vol.32, No.9, https://doi.org/10.1029/96WR01467 , 1996.
7. Zhang, D., and S.P. Neuman, Eulerian-Lagrangian Analysis of Transport Conditioned on Hydraulic Data: 1. Analytical-Numerical Approach, Water Resour. Res., Vol.31, No.1, DOI: 10.1029/94WR02234, 1995.
6. Zhang, D., and S.P. Neuman, Eulerian-Lagrangian Analysis of Transport Conditioned on Hydraulic Data: 2. Effects of Log Transmissivity and Hydraulic Head Measurements, Water Resour. Res., Vol.31, No.1, DOI: 10.1029/94WR02235, 1995.
5. Zhang, D., and S.P. Neuman, Eulerian-Lagrangian Analysis of Transport Conditioned on Hydraulic Data: 3. Spatial Moments, Travel Time Distribution, Mass Flow Rate and Cumulative Release Across a Compliance Surface, Water Resour. Res., Vol.31, No.1, DOI: 10.1029/94WR02236, 1995.
4. Zhang, D., and S.P. Neuman, Eulerian-Lagrangian Analysis of Transport Conditioned on Hydraulic Data: 4. Uncertain Initial Plume State and Non-Gaussian Velocities, Water Resour. Res., Vol.31, No.1, DOI:10.1029/94WR02237, 1995.
3. Zhang, D., Impacts of Local Dispersion and First-Order Decay on Solute Transport in Randomly Heterogeneous Porous Media, Transport in Porous Media, Vol.21, No.2, DOI: 10.1007/BF00613752, 1995.
2. Zhang, D., and S.P. Neuman, Comment on “A Note on Head and Velocity Covariances in Three-Dimensional Flow through Heterogeneous Anisotropic Porous Media” by Y. Rubin and G. Dagan, Water Resour. Res., Vol.31, No.12, https://doi.org/10.1029/92WR02223, 1992. (Published during M.Sc. studies)
1. Zhang, D., Rotating Sense Determination in Planar Gear Train by Use of Complex Number, Journal of Northeastern University, P. R. China, No.1, 1988. (Published during undergraduate studies)