Multi-scale source-sink matching and transportation route optimization for geologic CO₂ sequestration of industrial CO₂ stationary sources in Jiangsu Province

Authors

TIAN Yuchen, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, ChinaFollow
LIU Shiqi, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, ChinaFollow
ZHANG Helong, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
MO Hang, China Nonferrous Metals (Guilin) Geology and Mining Co., Ltd., Guilin 541000, China
WANG Dexi, East China Oil & Gas Company, SINOPEC, Nanjing 210000, China
SANG Shuxun, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
WANG Jun, East China Oil & Gas Company, SINOPEC, Nanjing 210000, China
WANG Wenkai, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
ZHENG Sijian, Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, China; Carbon Neutrality Institute, China University of Mining and Technology, Xuzhou 221116, China

Abstract

Objective CO₂ stationary sources tend to show a dispersed distribution, while carbon sinks are concentrated in specific sedimentary basins. The resulting significant source-sink mismatch in space restricts the large-scale applications of carbon capture, utilization, and storage (CCUS) technology. Therefore, reducing CCUS costs by investigating source-sink matching and optimizing CO₂ transportation routes under a multi-scale spatial framework is the key to the engineering applications of CCUS. Methods Targeting representative industrial CO₂ stationary sources and carbon sinks in Jiangsu Province, this study established source-sink matching models on multiple scales covering hydrocarbon reservoirs, sags, and basins. Based on both the geographic information system (GIS)-based least-cost path planning method and an improved pipeline network optimization strategy, this study explored regional multi-scale source-sink matching. Results and Conclusions By the end of 2023, a total of 269 representative industrial CO₂ stationary sources were identified in Jiangsu Province, with total annual CO₂ emissions reaching 6.26×10⁸ t. Among these sources, different types exhibited different CO₂ emission scales and spatial distributions, with the thermal power sector yielding the highest proportion of CO₂ emissions. In Jiangsu Province, thermal power plants and steel mills are primarily distributed in the southern part of the Yangtze River Delta and cities along the Yangtze River. In contrast, cement plants are concentrated in the southern part of the province, while synthetic ammonia plants show a scattered distribution. Within the study area, deep saline aquifers and oil reservoirs in sags, such as B10, B11, and B6, exhibited great potential for geologic CO₂ sequestration, with the sequestration capacities estimated at about 58.7×10⁸ t and 7.28×10⁸ t, respectively. In combination with the regional demands for carbon emission reduction and the source-sink spatial distribution, this study developed source-sink matching models on multiple scales covering hydrocarbon reservoirs, sags, and basins. The calculation results of these models indicate that the theoretical pipeline lengths on the scales of hydrocarbon reservoirs, sags, and basins were determined at 238.9 km, 398 km, 3 873 km (the Subei Basin), and 4 100 km (the Subei-southern Yellow Sea Basin), respectively. After being optimized by integrating the GIS and the saving algorithm-based strategy, the pipeline lengths on the scales of hydrocarbon reservoirs, sags, and basins were calculated at 243.7 km, 426 km, 1 831 km (the Subei Basin), and 2 121 km (the Subei-southern Yellow Sea Basin), respectively. Such effort contributed to the formation of optimized CO₂ transportation routes matched better with the geographical setting and actual engineering implementation requirements while effectively reducing the construction costs of pipeline networks. The results of this study will provide a theoretical basis and methodological support for building low-cost, highly adaptable CCUS transportation routes in the eastern coastal areas of China.

Keywords

carbon capture, utilization, and storage (CCUS), source-sink matching, pipeline construction, CO2 stationary source, transportation route optimization, Jiangsu Province

DOI

10.12363/issn.1001-1986.25.07.0535

Recommended Citation

TIAN Yuchen, LIU Shiqi, ZHANG Helong, et al. (2026) "Multi-scale source-sink matching and transportation route optimization for geologic CO₂ sequestration of industrial CO₂ stationary sources in Jiangsu Province," Coal Geology & Exploration: Vol. 54: Iss. 1, Article 4.
DOI: 10.12363/issn.1001-1986.25.07.0535
Available at: https://cge.researchcommons.org/journal/vol54/iss1/4

Reference

[1] BAYER P，AKLIN M. The European Union emissions trading system reduced CO₂ emissions despite low prices[J]. Proceedings of the National Academy of Sciences of the United States of America，2020，117(16)：8804−8812.

[2] LANGLOIS–BERTRAND S，MOUSSEAU N，BEAUMIER L. Canadian energy outlook：The state of energy and GHG emissions in Canada (3rd Edition)[R]. Montréal：Institut de l’énergie Trottier，2024.

[3] 蔡博峰，李琦，张贤，等. 中国二氧化碳捕集利用与封存(CCUS)年度报告(2021)：中国CCUS路径研究[R]. 北京：生态环境部环境规划院，中国科学院武汉岩土力学研究所，中国21世纪议程管理中心，2021.

[4] ZHANG Zhi’en，PAN Shuyuan，LI Hao，et al. Recent advances in carbon dioxide utilization[J]. Renewable and Sustainable Energy Reviews，2020，125：109799.

[5] 李阳，赵清民，薛兆杰. “双碳”目标下二氧化碳捕集、利用与封存技术及产业化发展路径[J]. 石油钻采工艺，2023，45(6)：655−660.

LI Yang，ZHAO Qingmin，XUE Zhaojie. Carbon dioxide capture，utilization and storage technology and industrialization development path under the dual carbon goal[J]. Oil Drilling & Production Technology，2023，45(6)：655−660.

[6] 桑树勋，刘世奇，陆诗建，等. 工程化CCUS全流程技术及其进展[J]. 油气藏评价与开发，2022，12(5)：711−725.

SANG Shuxun，LIU Shiqi，LU Shijian，et al. Engineered full flowsheet technology of CCUS and its research progress[J]. Petroleum Reservoir Evaluation and Development，2022，12(5)：711−725.

[7] 刘世奇，莫航，桑树勋，等. 宁夏回族自治区碳捕集、利用与封存源汇匹配与集群部署[J]. 煤炭学报，2024，49(3)：1583−1596.

LIU Shiqi，MO Hang，SANG Shuxun，et al. Source–sink matching and cluster deployment of carbon capture，utilization，and storage in Ningxia Hui Autonomous Region[J]. Journal of China Coal Society，2024，49(3)：1583−1596.

[8] EGBERTS P J P，KEPPEL J F，WILDENBORG A F B，et al. A decision support system for underground CO₂ sequestration[M]//Greenhouse gas control technologies：6th International Conference. Amsterdam：Elsevier，2003：651–655.

[9] NEELE F，HENDRIKS C，BRANDSMA R. DSS and economic evaluations，SESS–518318D30[R]. Utrecht：EU Geo Capacity，2009.

[10] SUN Liang，CHEN Wenying. Study on DSS for CCUS source–sink matching[J]. Energy Procedia，2015，75：2311−2316.

[11] MORBEE J，SERPA J，TZIMAS E. Optimised deployment of a European CO₂ transport network[J]. International Journal of Greenhouse Gas Control，2012，7：48−61.

[12] MIDDLETON R S，KUBY M J，WEI Ran，et al. A dynamic model for optimally phasing in CO₂ capture and storage infrastructure[J]. Environmental Modelling & Software，2012，37：193−205.

[13] VAN DEN BROEK M，BREDERODE E，RAMÍREZ A，et al. Designing a cost–effective CO₂ storage infrastructure using a GIS based linear optimization energy model[J]. Environmental Modelling & Software，2010，25(12)：1754−1768.

[14] LI X C，WEI N，FANG Z M，et al. Early opportunities of carbon capture and storage in China[J]. Energy Procedia，2011，4：6029−6036.

[15] TANG Haotian，ZHANG Shu，CHEN Wenying. Assessing representative CCUS layouts for China’s power sector toward carbon neutrality[J]. Environmental Science & Technology，2021，55(16)：11225−11235.

[16] 沈今阳. 基于汇布井优化的CCUS源汇匹配模型[D]. 大连：大连理工大学，2022.

SHEN Jinyang. CCUS source–sink matching model based on sink well placement optimization[D]. Dalian：Dalian University of Technology，2022.

[17] ZHANG Xian，LI Kai，WEI Ning，et al. Advances，challenges，and perspectives for CCUS source–sink matching models under carbon neutrality target[J]. Carbon Neutrality，2022，1(1)：12.

[18] TIAN Yuchen，LIU Shiqi，ZHENG Sijian，et al. Optimization and potential assessment of CO₂ geological storage caprock in the saline aquifers of the Qingjiang Basin，middle and lower reaches of the Yangtze River[J]. Unconventional Resources，2025，6：100155.

[19] 刘世奇，皇凡生，杜瑞斌，等. CO₂地质封存与利用示范工程进展及典型案例分析[J]. 煤田地质与勘探，2023，51(2)：158−174.

LIU Shiqi，HUANG Fansheng，DU Ruibin，et al. Progress and typical case analysis of demonstration projects of the geological sequestration and utilization of CO₂[J]. Coal Geology & Exploration，2023，51(2)：158−174.

[20] 张贤，杨晓亮，鲁玺，等. 中国二氧化碳捕集利用与封存(CCUS)年度报告(2023)[R]. 北京：中国21世纪议程管理中心，全球碳捕集与封存研究院，清华大学，2023.

[21] ZHOU Wenji，ZHU Bing，LI Qiang，et al. CO₂ emissions and mitigation potential in China’s ammonia industry[J]. Energy Policy，2010，38(7)：3701−3709.

[22] 莫航，刘世奇，桑树勋. 苏北–南黄海盆地工业固定排放源CO₂地质封存源汇匹配研究[J]. 地质论评，2023，69(增刊1)：128−130.

MO Hang，LIU Shiqi，SANG Shuxun. Matching of CO₂ geological sequestration source and sink for industrial fixed emission source in Subei–southern Yellow Sea Basin[J]. Geological Review，2023，69(Sup.1)：128−130.

[23] 科学技术部社会发展科技司，中国21世纪议程管理中心. 中国碳捕集、利用与封存技术发展路线图(2019)[M]. 北京：科学出版社，2019.

[24] 毛永娜. 我国火电厂二氧化碳捕获与封存的布局与优化研究[D]. 成都：成都理工大学，2015.

MAO Yongna. The layout and optimization research of the coal–fired power plant in China on carbon dioxide capture and storage[D]. Chengdu：Chengdu University of Technology，2015.

[25] 杨文洁. 碳捕获使用与埋存(CCUS)技术路径的成本分析研究[D]. 天津：天津科技大学，2020.

YANG Wenjie. Analysis of the cost of carbon dioxide capture utilization and storage (CCUS) technology options[D]. Tianjin：Tianjin University of Science & Technology，2020.

[26] MCCOLLUM D L，OGDEN J M. Techno–economic models for carbon dioxide compression，transport，and storage & correlations for estimating carbon dioxide density and viscosity[R]. 2006：1–88.

[27] 王众，骆毓燕，匡建超，等. 我国大型燃煤电厂CCS源汇匹配与优化研究[J]. 工业工程与管理，2016，21(6)：75−83.

WANG Zhong，LUO Yuyan，KUANG Jianchao，et al. Source–sink matching and optimization of CCS for large coal–fired power plants in China[J]. Industrial Engineering and Management，2016，21(6)：75−83.

[28] 王蓬涛. 碳捕集利用与封存项目源汇匹配方法及其应用研究[D]. 北京：中国矿业大学(北京)，2021.

WANG Pengtao. Carbon dioxide capture，utilization and storage project source–sink matching methods and its application[D]. Beijing：China University of Mining & Technology (Beijing)，2021.

[29] MIT. Carbon management GIS：CO₂ pipeline transport cost estimation[R]. 2009.

[30] PARKER N. Using natural gas transmission pipeline costs to estimate hydrogen pipeline costs[J]. 2003.

[31] 孙亮. 中国CCUS源汇匹配决策支持系统研究[D]. 北京：清华大学，2013.

SUN Liang. Research on China’s CCUS source–sink matching decision support system[D]. Beijing：Tsinghua University，2013.

[32] SUN Lili，LIU Qiang，CHEN Hongju，et al. Source–sink matching and cost analysis of offshore carbon capture，utilization，and storage in China[J]. Energy，2024，291：130137.

[33] CHEN Cong，MA Shouyi，WANG Xi，et al. CCUS source–sink matching model based on sink well placement optimization[J]. Fuel，2024，377：132812.

[34] 刘巍，董明. 碳封存网络的规划模型及求解算法研究[J]. 工业工程与管理，2011，16(6)：128−132.

LIU Wei，DONG Ming. Research on carbon sequestration network planning model and solution algorithm[J]. Industrial Engineering and Management，2011，16(6)：128−132.

[35] HENNING M A，VAN VUUREN J H. Graph and network theory：An applied approach using mathematica[M]. Cham：Springer Cham，2022.

[36] WANG Xiantao，DONG Zhen，WANG Youjun，et al. Optimizing the reference network by minimum spanning tree approach in SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing，2024，62：5224616.

[37] PAVON W，TORRES M，INGA E. Integrating minimum spanning tree and MILP in urban planning：A novel algorithmic perspective[J]. Buildings，2024，14(1)：213.

[38] 莫航. 苏北–南黄海盆地工业固定排放源CO₂地质封存源汇匹配研究[D]. 徐州：中国矿业大学，2024.

MO Hang. Study on matching of source and sink for geological storage of CO₂ from industrial fixed emission sources in Subei–south Yellow Sea Basin[D]. Xuzhou：China University of Mining and Technology，2024.