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Numerical simulation and parameter global sensitivity analysis of CO2 geological storage and utilization in oil-water mixed layers
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Yu-jia ZHANG1, Yun YANG1, *, Xu-long GONG2, Hui-xing ZHU3, Long-yu CUI2, Gong-yi WU4, Jian SONG1, Chun ZHU1, Ji-chun WU5
China Environmental Science | 2025, 45(8) : 4667 - 4676
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China Environmental Science | 2025, 45(8): 4667-4676
• Carbon Emission Control •
Numerical simulation and parameter global sensitivity analysis of CO2 geological storage and utilization in oil-water mixed layers
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Yu-jia ZHANG1, Yun YANG1, *, Xu-long GONG2, Hui-xing ZHU3, Long-yu CUI2, Gong-yi WU4, Jian SONG1, Chun ZHU1, Ji-chun WU5
Affiliations
  • 1.Key Laboratory of Groundwater Protection and Utilization—National Key Laboratory Cultivation and Development Site, School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
  • 2.Jiangsu Geological Survey and Research Institute, Nanjing 210002, China
  • 3.College of New Energy and Environment, Jilin University, Changchun 130021, China
  • 4.China Petroleum & Chemical Corporation East China Oil&Gas Branch, Taizhou 225300, China
  • 5.School of Earth Science and Engineering, Nanjing University,Nanjing 210023, China
Published: 2025-08-20
Outline
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In this paper, an oil field in Northern Jiangsu Province is selected as the research object, and a multiphase flow reaction migration model CO2-DSMRT for CO2 displacement and storage is constructed to describe the transformation and fate of supercritical state/gas phase, dissolved water phase, dissolved oil phase and mineral phase CO2 on a century scale. The sensitive parameters affecting the prediction results of the model are identified by Sobol method. Furthermore, PSO-LSSVM method was used to establish an alternative model to characterize the mapping relationship between sensitive parameter input and CO2 multiphase output results, and Monte Carlo stochastic simulation was carried out to quantify the uncertainty of the model. The results show that after 100 years of CO2 injection into the oil-water mixed layer, the gas phase CO2 accounts for 61.2%, the oil-soluble phase CO2 accounts for 17.8%, the water-soluble phase CO2 accounts for 11.2%, and the mineral phase CO2 accounts for 9.8%. The most sensitive parameter of co2-dsmrt model to oil production is permeability, and the most sensitive parameter to reservoir CO2 storage is porosity. The production investment ratio of CO2 storage capacity and oil production under different confidence levels is significantly different. P10scenario is suitable for storage priority strategy, P50 scenario can achieve the balance between storage and economic benefits, and P90 scenario is more suitable for production optimization strategy.

CO2 geological storage and utilization  /  multiphase flow  /  reactive transport  /  global sensitivity analysis  /  uncertainty analysis
张雨佳, 杨蕴, 龚绪龙, 朱慧星, 崔龙玉, 吴公益, 宋健, 朱淳, 吴吉春. 油水混合层CO2地质封存与利用数值模拟及参数全局敏感性分析. 中国环境科学, 2025 , 45 (8) : 4667 -4676 .
Yu-jia ZHANG, Yun YANG, Xu-long GONG, Hui-xing ZHU, Long-yu CUI, Gong-yi WU, Jian SONG, Chun ZHU, Ji-chun WU. Numerical simulation and parameter global sensitivity analysis of CO2 geological storage and utilization in oil-water mixed layers[J]. China Environmental Science, 2025 , 45 (8) : 4667 -4676 .
Year 2025 volume 45 Issue 8
100
14
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Article Info
  • Receive Date:2025-01-03
  • Online Date:2026-10-08
  • Published:2025-08-20
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History
  • Received:2025-01-03
Funding
Affiliations
    1.Key Laboratory of Groundwater Protection and Utilization—National Key Laboratory Cultivation and Development Site, School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
    2.Jiangsu Geological Survey and Research Institute, Nanjing 210002, China
    3.College of New Energy and Environment, Jilin University, Changchun 130021, China
    4.China Petroleum & Chemical Corporation East China Oil&Gas Branch, Taizhou 225300, China
    5.School of Earth Science and Engineering, Nanjing University,Nanjing 210023, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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