一、个人简介
Muhammad Ali，男，1988年7月生，巴基斯坦籍，副教授。现任东北石油大学非常规油气研究院副教授。主要从事地球物理学、岩石物理、地震反演、储层表征、机器学习与深度学习及二氧化碳地质封存等相关科研工作，在地震数据处理与解释、测井与岩石物理建模、地震反演与储层参数预测、机器学习驱动的地下储层表征、CO₂地质封存与运移模拟以及储层地质力学评价等方面开展了系统研究。具有丰富的国际科研与教学经历，先后在中国地质大学（武汉）地球物理与测绘研究所、中国科学院武汉岩土力学研究所开展博士后研究，并曾在巴基斯坦高校从事教学与科研工作。长期致力于地球物理方法与人工智能技术的交叉应用，重点开展复杂地下介质精细表征及油气与CO₂地下储存相关研究。
先后开展地球物理数据分析、岩石物理建模、地震反演、机器学习及CO₂地质封存等方向的科研工作，形成了地球物理数据智能处理、储层综合表征及地下流体运移评价等方面的研究特色。获得中国国家留学基金委（CSC）t奖学金（2017-2021），并荣获中国地质大学（武汉）“优质学术刊物”奖（2020、2021）。开发了用于地球物理数据分析、可视化与解释的综合软件应用程序，实施机器学习算法以增强地球物理数据处理与建模能力，并构建了面向高效数据解释的用户界面。在国内外学术期刊上发表论文60篇，论文累计被引用1962次，H-index为26，i10-index为39。代表性研究成果发表于Geoscience Frontiers、Engineering Geology、Energy、IEEE Transactions on Geoscience and Remote Sensing、Energy & Fuels、Journal of Cleaner Production、Fuel等国内外学术期刊，研究成果主要涉及机器学习驱动的地震解释与反演、储层物性预测、岩石物理参数估算、非常规储层表征、CO₂地质封存及地下资源协同利用等方向。
二、受教育及工作经历
2008年本科毕业于巴基斯坦卡拉奇大学数学专业，2011年获巴基斯坦卡拉奇大学石油技术硕士学位，2015年获巴基斯坦巴赫利亚大学地球与环境科学系地球物理学硕士学位。2021年博士毕业于中国地质大学（武汉）地球物理与测绘研究所，获地球物理学博士学位。2021年8月-2024年6月在中国地质大学（武汉）地球物理与测绘研究所从事博士后研究，2024年7月-2026年7月在中国科学院武汉岩土力学研究所从事博士后研究及助理研究员工作。2026年9月至今任东北石油大学非常规油气研究院副教授。
三、其他学术兼职（或职务）及荣誉
1、国际学术期刊编委（Editorial Board Member）：Petroleum Geoscience（Geological Society of London）、Reservoir Science、Journal of Geo-Energy and Environment、Journal of Geoscience and Earth Observation、Journal of Energy & Resources等；
2、长期担任国际学术期刊审稿人，已完成同行评审215次，涉及37种期刊及科研项目，审稿领域涵盖地球物理、地球科学、石油与天然气工程、岩石力学、人工智能及能源地质等。主要审稿期刊包括《Geoenergy Science and Engineering》（80次）、《Journal of Petroleum Exploration and Production Technology》（39次）、《Journal of Petroleum Science & Engineering》（19次）、《ACS Omega》（8次）、《Neural Computing & Applications》（7次）、《Petroleum Geoscience》（4次）、《Mathematical Geology》（4次）、《Computational Geosciences》（6次）、《Journal of the Geological Society》（3次）、《Journal of Natural Gas Geoscience》（3次）、《Petroleum Science》（3次）、《Applied Soft Computing》（3次）、《Neural Networks》（2次）、《Fuel》（4次）、《Energy Geoscience》（2次）、《Energy Science & Engineering》（2次）、《Engineering Applications of Artificial Intelligence》（3次）、《Geothermics》（2次）、《PLOS ONE》（2次）、《Rock Mechanics and Rock Engineering》（2次）以及IEEE Geoscience and Remote Sensing Letters、IEEE Transactions on Geoscience and Remote Sensing、Acta Geophysica、Applied Intelligence、Engineering Geology、Marine Georesources & Geotechnology、Mathematical Geosciences、Mathematics、Open Geosciences等国际学术期刊；
4、获得中国国家留学基金委（CSC）Fullbright奖学金（2017—2021）；
5、荣获中国地质大学（武汉） “优质学术刊物” 奖（2020年）；
6、荣获中国地质大学（武汉） “优质学术刊物” 奖（2021年）。
四、承担的主要科研项目
1、国家重点研发计划项目 “CO2驱油及封存安全监测技术” （项目编号：2023YFB4104200），参与，项目总经费259万元；
2、中央企业项目 “国家能源集团鄂尔多斯煤制油公司百万吨级CCUS示范项目工程化方案研究” （项目编号：21155-PC-DBD-002），参与，项目总经费300万元；
3、国家重点研发计划 “月球火成与盆地撞击均衡等重大事件演化研究” ，由中国科学院国家天文台牵头（项目编号：2021YFA0715103），2021.12-2026.11，项目总经费271万元，参与；
4、纵向科研项目 “武夷构造廊带电性结构探测研究” ，中国地质科学院矿产资源研究所，第8课题，2018.07-2020.12，项目经费142万元，参与。
五、发表论文情况
1. Ali, M., Wei, N., Jiang, R., Wang, W., Lin, K., Jia, C., 2026. Integrated geostatistical and machine learning reservoir modeling for CO2 plume prediction and storage safety assessment. Int. J. Greenh. Gas Control 155, 104766. https://doi.org/10.1016/j.ijggc.2026.104766 (2区 影响因子 = 5.9)
2. Ali, M., Wei, N., Changxingyue, H., Lin, K., Wang, W., Ehsan, M., Jiang, R., Hussain, W., Ashraf, U “Advancing seismic facies analysis and reservoir characterization: A synthetic data-augmented CNN and transfer learning workflow integrated with real seismic data,” Gas Sci. Eng., vol. 147, p. 205831, 2026, doi: https://doi.org/10.1016/j.jgsce.2025.205831. (2区 影响因子 = 6.67)
3. Hussain, W., Ali, M*., Kasala, E.E., Ali, S., AL-khulaidi, G., Sadiq, I., Nyakilla, E.E., Hussain, S., Abelly, E.N., 2025a. Machine learning-driven classification of hydraulic flow units for enhanced reservoir characterization. Physics of Fluids 37, 037194. https://doi.org/10.1063/5.0259689 (2区 影响因子 = 4.30)
4. Ali, M., Changxingyue, H., Wei, N., Jiang, R., Zhu, P., Hao, Z., Hussain, W., Ashraf, U., 2024. “Optimizing seismic-based reservoir property prediction: a synthetic data-driven approach using convolutional neural networks and transfer learning with real data integration.” Artif Intell Rev 58, 31. https://doi.org/10.1007/s10462-024-11030-8 (1区 影响因子 = 18.8)
5. Ali, M., Zhu, P., Huolin, M., Jiang, R., Zhanga, H., Ashraf, U., Hussain, W., “Data-driven machine learning approaches for precise lithofacies identification in complex geological environments.” Geo-spatial Inf. Sci. https://doi.org/10.1080/10095020.2024.2405635 (1区 影响因子 = 6)
6. Hussain, W., Ali, M*., Sadaf, R., Izhar, H.F.A., Chuanyu, S., “Advanced AI approach for enhanced predictive modeling in reservoir characterization within complex geological environments,” Model. Earth Syst. Environ., 2024, doi: 10.1007/s40808-024-02049-5. (3区 影响因子 = 3.6)
7. Ali, M., Zhu, P., Jiang, R., Huolin, M., Ashraf, U., Zhang, H., Hussain, W., “Data-driven lithofacies prediction in complex tight sandstone reservoirs: a supervised workflow integrating clustering and classification models,” Geomech. Geophys. Geo-Energy Geo-Resources, vol. 10, no. 1, p. 70, Dec. 2024, doi: 10.1007/s40948-024-00787-5. (2区 影响因子= 5)
8. Ali, M., P. Zhu, R. Jiang, M. Huolin, and U. Ashraf, “Improved Prediction of Thin Reservoirs in Complex Structural Regions Using Post-stack Seismic Waveform Inversion: A Case Study in the Junggar Basin,” Can. Geotech. J., vol. 0, no. ja, p. null, Mar. 2024, doi: 10.1139/cgj-2023-0384 (2区 影响因子 = 3.6)
9. Ali, M., Zhu, P., Jiang, R., Huolin, M., Ehsan, M., Hussain, W., Zhang, H., Ashraf, U., “Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based ensemble machine learning,” Appl. Soft Comput., vol. 148, p. 110843, Nov. 2023, doi: 10.1016/j.asoc.2023.110843 (1区 影响因子 = 8.7)
10. Ali, M., Zhu, P., Jiang, R., Huolin, M., Ehsan, M., Hussain, W., Zhang, H., Ashraf, U., “A Novel Machine Learning Approach for Detecting Outliers, Rebuilding Well Logs, and Enhancing Reservoir Characterization,” Nat. Resour. Res., vol. 32, no. 3, pp. 1047–1066, Jun. 2023, doi: 10.1007/s11053-023-10184-6. (1区 影响因子 = 5.9)
11. Ali, M., Zhu, P., Jiang, R., Huolin, M., Ehsan, M., Hussain, W., Zhang, H., Ashraf, U., “Quantitative Characterization of Shallow Marine Sediments in Tight Gas Fields of Middle Indus Basin: A Rational Approach of Multiple Rock Physics Diagnostic Models,” Processes, vol. 11, no. 2, p. 323, Jan. 2023, doi: 10.3390/pr11020323 (3区 影响因子 = 3.5)
12. Ali, M., Jiang, R., Ma, H., Pan, H., Ashraf, U., “Machine learning - A novel approach of well logs similarity based on synchronization measures to predict shear sonic logs,” J. Pet. Sci. Eng., vol. 203, p. 108602, Aug. 2021, doi: 10.1016/j.petrol.2021.108602. (2区 影响因子 = 5.16)
13. Ali, M., Jiang, R., Ma, H., Pan, H., Ashraf, U., “Building a rock physics model for the formation evaluation of the Lower Goru sand reservoir of the Southern Indus Basin in Pakistan,” J. Pet. Sci. Eng., vol. 194, p. 107461, Nov. 2020, doi: 10.1016/j.petrol.2020.107461. (2区 影响因子 = 5.16)
14. Ali, M, M. J. Khan, M. Ali, and S. Iftikhar, “Petrophysical analysis of well logs for reservoir evaluation: a case study of ‘Kadanwari’ gas field, middle Indus basin, Pakistan,” Arab. J. Geosci., vol. 12, no. 6, p. 215, Mar. 2019, doi: 10.1007/s12517-019-4389-x. (4区 影响因子 = 1.8)
15. Wang, W., Wei, N., Wei, F., Jiang, D., Ali, M., Cai, B., Jiang, L., Wu, W., Lin, K., Mei, Y., Wang, L., 2026. Strategic appraisal of energy storage for renewable integration: KPI-MCDM/A evidence mapping, a feasibility-first workflow, and minimum reporting standards. Energy Strateg. Rev. 67, 102357. https://doi.org/10.1016/j.esr.2026.102357 (1区 影响因子 = 12.1)
16. Hussain, W., Ali, M., Mkono, C.N., Hussain, J., Khan, S., Asghar, A., Zheng, H., 2027. A Feature-Augmented Deep Learning Model for Total Organic Carbon (TOC) estimation. Phys. Chem. Earth, Parts A/B/C 145, 104911. https://doi.org/10.1016/j.pce.2026.104911 (2区 影响因子 = 4.6)
17. Wang, Wendong, Mei, Y., Ali, M., Lin, K., Wang, Wenhan, Liu, W., Lei, S., Jiang, L., Wei, N., 2026. Transition Bands in Gas–Liquid Microchannel Flows: Nonlinear Dynamics, Predictive Modeling, and Implications for Microreactor Design and Process Intensification. Ind. Eng. Chem. Res. 65, 21357–21386. https://doi.org/10.1021/acs.iecr.6c03314 (1区 影响因子 = 3.9)
18. Xiao, L., Zheng, H., Ali, Muhammad, Hussain, W., Zhu, M., Iqbal, S.M., Hussain, A., Ali, Mohsin, 2027. PSO-optimized lithology-guided deep learning model for uniaxial compressive strength prediction from well logs. Phys. Chem. Earth, Parts A/B/C 145, 104799. https://doi.org/10.1016/j.pce.2026.104799 (2区 影响因子 = 4.6)
19. Quer, H., Zhu, P., Ali, M., Sayed, M.A., 2026. Extended elastic impedance inversion for enhanced reservoir characterization in the Galhak Oil Formation of the Rawat Basin Sudan. Sci. Rep. https://doi.org/10.1038/s41598-026-68052-1 (3区 影响因子 = 4.9)
20. Wang, W., Wei, N., Jiang, L., Ali, M., Hu, Y., Lin, K., & Liu, X. (2026). Smart Earth for CO2 geological storage (CGS): A geophysical perspective on integrated multi-domain monitoring. Geoscience Frontiers, 102380. https://doi.org/10.1016/j.gsf.2026.102380 (1区 影响因子 = 8.9)
21. Ehsan, M., Chen, R., Ali, M., Manzoor, U., Naqvi, S.M.N., Ali, A., Hussain, M., Abdelrahman, K., Ullah, J., 2025a. Evaluating the Ranikot formation in the middle Indus Basin, Pakistan as a promising secondary reservoir for development. Geomechanics and Geophysics for Geo-Energy and Geo-Resources 11, 32. https://doi.org/10.1007/s40948-025-00950-6 (2区 影响因子 = 4.2)
22. Ehsan, M., Munir, M.N., Chen, R., Ali, M., Abdelrahman, K., Kahal, A.Y., 2026. Mineralogical characterization and rock physics parameter estimation in sandstone reservoir: A template-based approach and cross-plots analysis. Physics and Chemistry of the Earth, Parts A/B/C 143, 104329. https://doi.org/https://doi.org/10.1016/j.pce.2026.104329 (3区 影响因子 = 4.1)
23. Ehsan, M., Raja, G.H.K., Tassaduq, A., Naseem, W., Ali, M., Abdelrahman, K., Kahal, A.Y., 2025b. Comparison of surface and subsurface limestone reservoir properties: key insights derived from the analysis of geological and geophysical datasets. Physics and Chemistry of the Earth, Parts A/B/C 140, 104027. https://doi.org/10.1016/j.pce.2025.104027 (2区 影响因子 = 4.1)
24. Wang, W., Wei, N., Wei, F., Ali, M., Lin, K., Lei, S., 2025. Research on the progress of Carbon Capture, Utilization, and Storage (CCUS) technologies based on bibliometric analysis. J. Clean. Prod. 529, 146826. https://doi.org/10.1016/j.jclepro.2025.146826 (1区 影响因子 = 10)
25. Lin, K., Zhang, Y., Ali, M., Wang, W., 2025d. Synergistic pathways: CO2 storage and subsurface resource utilization in China’s carbon-neutral goal. Journal of CO2 Utilization 98, 103141. https://doi.org/10.1016/j.jcou.2025.103141 (2区 影响因子 = 8.53)
26. Lin, K., Wei, N., Zhang, Y., Liu, S., Ali, M., Wang, W., Chen, Q., Wang, Y., 2025c. Hydro-mechanical interactions in CO2 storage: Critical parameters influencing coal mine at Shenhua’s CCS site. Eng. Geol. 352, 108087. https://doi.org/10.1016/j.enggeo.2025.108087 (1区 影响因子 = 10)
27. Lin, K., Wei, N., Zhang, Y., Ali, M., Chen, Q., Wang, W., Song, Z., Yin, Y., Vo Thanh, H., 2025b. Advances in Machine-Learning-Driven CO 2 Geological Storage: A Comprehensive Review and Outlook. Energy & Fuels 39, 13315–13343. https://doi.org/10.1021/acs.energyfuels.5c02370 (3区 影响因子 = 5.3)
28. Lin, K., Jing, T., Wei, N., Chen, Q., Zhao, W., Zhou, J., Ali, M., Wang, W., Li, X., 2025a. A Comprehensive Analysis of the Mutual Feedback Mechanisms between CO 2 Geological Storage and Underground Coal Mining in the Ordos Basin. Energy & Fuels 39, 5802–5817. https://doi.org/10.1021/acs.energyfuels.4c06309 (3区 影响因子 = 5.3)
29. Liao, Z., Zhu, P., Zhang, H., Li, Zewei, Li, Zi’ang, Ali, M., “A Deep Learning-based Seismic Horizon Tracking Method with Uncertainty Encoding and Vertical Constraint,” IEEE Trans. Geosci. Remote Sens., pp. 1–1, 2024, doi: 10.1109/TGRS.2024.3424467 (1区, 影响因子 = 8.2)
30. Hussain, W., Luo, M., Ali, M., Hussain, H., Ali, S., Iqbal, J., Nyakilla, E.E., Asghar, A., “Rock physics modeling to evaluate clastic reservoirs: a case study from the Lower Goru Formation, Middle Indus Basin, Pakistan,” Environ. Earth Sci., vol. 83, no. 12, p. 383, 2024, doi: 10.1007/s12665-024-11676-1. (4区 影响因子 = 2.8)
31. Hussain, W., Luo, M., Ali, M., Al-khafaji, H.F., Hussain, I., Hussain, M., Alshareef, S., Ahmed, A., 2024. “A Gamma-ray spectroscopy approach to evaluate clay mineral composition and depositional environment : A case study from the lower Goru Formation , Southern Indus Basin , Pakistan,” J. Appl. Geophys., vol. 226, no. May 2023, p. 105414, 2024, doi: 10.1016/j.jappgeo.2024.105414. (4区 影响因子 = 3.2)
32. Ashraf, U., Zhang, H., Anees, A., Ali, M., Mangi, H.N., Zhang, X., “An ensemble-based strategy for robust predictive volcanic rock typing efficiency on a global-scale: A novel workflow driven by big data analytics,” Sci. Total Environ., p. 173425, 2024, doi: https://doi.org/10.1016/j.scitotenv.2024.173425. (1区, 影响因子= 9.8)
33. Zhang, H., Song, X., Zhu, P., Ali, M., Liao, Z., Ruan, D., Li, T., A two-stage convolutional neural network for interactive channel segmentation from 3D seismic data,” IEEE Trans. Geosci. Remote Sens., pp. 1–1, 2024, doi: 10.1109/TGRS.2024.3401867(1区, 影响因子 = 8.2)
34. Ashraf, U., Zhang, H., Thanh, H.V., Anees, A., Ali, M., Duan, Z., Mangi, H.N., Zhang, X., “A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field,” Nat. Resour. Res., May 2024, doi: 10.1007/s11053-024-10350-4. (2区, 影响因子= 5.8)
35. Ashraf, U., Shi, W., Zhang, H., Anees, A., Jiang, R., Ali, M., Mangi, H.N., Zhang, X., 2024. Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods. Sci. Rep. 14, 5659. https://doi.org/10.1038/s41598-024-55250-y (2区 影响因子 = 4.6)
36. Ullah, J., Li, H., Ashraf, U., Heping, P., Ali, M., Ehsan, M., Asad, M., Anees, A., & Ren, T. (2023). “Knowledge-based machine learning for mineral classification in a complex tectonic regime of Yingxiu-Beichuan fault zone, Sichuan basin,”. Geoenergy Sci. Eng., vol. 229, p. 212077, Oct. 2023, doi: 10.1016/j.geoen.2023.212077. (2区 影响因子 = 5.16)
37. Hussain, W., Pan, L., Wang, X., Saqlain, M., Ali, M., & Sadaf, R. “Evaluation of unconventional hydrocarbon reserves using petrophysical analysis to characterize the Yageliemu Formation in the Yakela gas condensate field, Tarim Basin, China,”. Arab. J. Geosci., vol. 15, no. 21, p. 1635, Nov. 2022, doi: 10.1007/s12517-022-10902-z. (4区 影响因子 = 1.8)
38. Hussain, W., Ehsan, M., Pan, L., Wang, X., Ali, M., Din, S. U., Hussain, H., Jawad, A., Chen, S., Liang, H., & Liang, L., “Prospect Evaluation of the Cretaceous Yageliemu Clastic Reservoir Based on Geophysical Log Data: A Case Study from the Yakela Gas Condensate Field, Tarim Basin, China,”. Energies, vol. 16, no. 6, p. 2721, Mar. 2023, doi: 10.3390/en16062721. (2区 影响因子 = 5.16)
39. Hussain, W., Luo, M., Ali, M., Hussain, S. M., Ali, S., Hussain, S., Naz, A. F., & Hussain, S. (2023). Machine learning - a novel approach to predict the porosity curve using geophysical logs data: An example from the Lower Goru sand reservoir in the Southern Indus Basin, Pakistan. Journal of Applied Geophysics, 214, 105067. https://doi.org/https://doi.org/10.1016/j.jappgeo.2023.105067 (4区 影响因子 = 3.2)
40. Latif, M. A. U., Ehsan, M., Ali, M., Ali, A., Ekoa Bessa, A. Z., & Abioui, M. (2023). The assessment of reservoir potential of Permian to Eocene reservoirs of Minwal-Joyamair fields, upper Indus basin, Pakistan. Heliyon, 9(6), e16517. https://doi.org/10.1016/j.heliyon.2023.e16517 (4区 影响因子 = 4)
41. Hussain, M., Liu, S., Ashraf, U., Ali, M., Hussain, W., Ali, N., & Anees, A. (2022). Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type. Energies, 15(12). https://doi.org/10.3390/en15124501 (4区 影响因子 = 3.2)
42. Ullah, J., Luo, M., Ashraf, U., Pan, H., Anees, A., Li, D., Ali, M., & Ali, J., “Evaluation of the geothermal parameters to decipher the thermal structure of the upper crust of the Longmenshan fault zone derived from borehole data,” Geothermics, vol. 98, p. 102268, Jan. 2022, doi: 10.1016/j.geothermics.2021.102268. (2区 影响因子 = 3.9)
43. Abbas, K., Butt, K. M., Xu, D., Ali, M., Baz, K., Kharl, S. H., & Ahmed, M., “Measurements and determinants of extreme multidimensional energy poverty using machine learning,” Energy, vol. 251, p. 123977, Jul. 2022, doi: 10.1016/j.energy.2022.123977. (1区 影响因子 = 9)
44. Ali, J., Ashraf, U., Anees, A., Peng, S., Umar, M. U., Vo Thanh, H., Khan, U., Abioui, M., Mangi, H. N., Ali, M., & Ullah, J., “Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling,” ACS Omega, vol. 7, no. 43, pp. 39375–39395, Nov. 2022, doi: 10.1021/acsomega.2c05759 (3区 影响因子 = 4.1)
45. Ali, N., Chen, J., Fu, X., Hussain, W., Ali, M., Hussain, M., Anees, A., Rashid, M., & Thanh, H. V., “Prediction of Cretaceous reservoir zone through petrophysical modeling: Insights from Kadanwari gas field, Middle Indus Basin,” Geosystems and Geoenvironment, vol. 1, no. 3, p. 100058, Aug. 2022, doi: 10.1016/j.geogeo.2022.100058.
46. Ali, N., Chen, J., Fu, X., Hussain, W., Ali, M., Iqbal, S. M., Anees, A., Hussain, M., Rashid, M., & Thanh, H. V., “Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan,” Geosystems and Geoenvironment, vol. 2, no. 1, p.100123，Feb.2023，doi:10.1016/j.geogeo.2022.100123..https://doi.org/https://doi.org/10.1016/j.geogeo.2022.100123
47. Ashraf, U., Anees., Shi, W., Wang, R., Ali, M., Jiang, R., Vo Thanh, H., Iqbal, I., Zhang, X., & Zhang, H., “Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin,” Front. Earth Sci., vol. 10, Sep. 2022, doi: 10.3389/feart.2022.1014052 (3区 影响因子 = 2.9)
48. Ashraf, U., Zhang, H., Anees, A., Mangi, H. N., Ali, M., Zhang, X., Imraz, M., Abbasi, S. S., Abbas, A., Ullah, Z., Ullah, J., & Tan, S., “A Core Logging, Machine Learning and Geostatistical Modeling Interactive Approach for Subsurface Imaging of Lenticular Geobodies in a Clastic Depositional System, SE Pakistan,” Nat. Resour. Res., vol. 30, no. 3, pp. 2807–2830, Jun. 2021, doi: 10.1007/s11053-021-09849-x. (2区 影响因子 = 5.8)
49. Ashraf, U., Zhang, H., Anees, A., Ali, M., Zhang, X., Shakeel Abbasi, S., & Nasir Mangi, H., “Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion,” Water, vol. 12, no. 11, p. 2972, Oct. 2020, doi: 10.3390/w12112972. (3区 影响因子 = 3.4)
50. Ashraf, U., Zhang, H., Anees, A., Mangi, H. N., Ali, M., Ullah, Z., & Zhang, X., “Application of Unconventional Seismic Attributes and Unsupervised Machine Learning for the Identification of Fault and Fracture Network,” Appl. Sci., vol. 10, no. 11, p. 3864, Jun. 2020, doi 10.3390/app10113864. (4区 影响因子 = 2.7)
51. Ali, M., “КОНТРОЛИРУЕМЫЙ ТЕХПРОЦЕСС ДЛЯ ПРОГНОЗИРОВАНИЯ ЛИТОФАЦИЙ В СЛОЖНЫХ НЕОДНОРОДНЫХ ПЛОТНЫХ ПЕСЧАНЫХ КОЛЛЕКТОРАХ: ОСНОВАННЫЙ НА ДАННЫХ ПОДХОД С ИСПОЛЬЗОВАНИЕМ МОДЕЛЕЙ КЛАСТЕРИЗАЦИИ И КЛАССИФИКАЦИИ,” Geol. Bull. PERM Univ., no. 2021, pp. 342–353, 2023, doi: 10.17072/psu.geol.22.4
52. Ali, M., “A Data-Driven Machine Learning Approach for Predicting Lithofacies in Unconventional and Heterogeneous Tight Sandstone Reservoirs “, China University of Petroleum (Qingdao), Sep 8, 2023.
六、研究方向：
地球物理学与应用地球物理
岩石物理与测井解释
地震反演与地震解释
地球物理机器学习与深度学习
储层表征与岩石物性参数预测
二氧化碳地质封存与地下碳封存
地质力学建模与盖层完整性评价
地球物理数据处理与智能地下表征
非常规油气储层表征与评价
地球物理—地质综合建模
七、在线研究论坛：
· Google Scholar: Muhammad Ali - Google Scholar
· ResearchGate: Muhammad Ali (researchgate.net)
· ResearcherID: Muhammad Ali - Web of Science Core Collection (clarivate. cn)
· Scopus ID: Ali, Muhammad - Author details - Scopus Preview
八、联系方式
通信地址：163318 黑龙江省大庆市高新区火炬街孵化器1号楼东北石油大学非常规油气研究院
E-mail：muhammad_ali@nepu.edu.cn
