Optimization of gas injection scheme for underground gas storage considering dynamic changes in reservoir pressure

YU Pengfei; HOU Lei; WANG Xueting; LIU Qian; WANG Mincong

Oil & Gas Storage and Transportation > 2025 >

YU Pengfei, HOU Lei, WANG Xueting, et al. Optimization of gas injection scheme for underground gas storage considering dynamic changes in reservoir pressure[J]. Oil & Gas Storage and Transportation, 2025, 44(4): 1−12.

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Optimization of gas injection scheme for underground gas storage considering dynamic changes in reservoir pressure

YU Pengfei^{1, 2,},
HOU Lei^{1, 2, ,},
WANG Xueting^{1, 2},
LIU Qian^{1, 2},
WANG Mincong^{1, 2}

1.
College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing)
2.
National Energy Underground Gas Storage Research and Development Center

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Received Date: November 12, 2024
Revised Date: January 08, 2025
Available Online: March 30, 2025

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Abstract

Abstract

Objective Underground gas storage (UGS) facilities are crucial infrastructures in the "production-supply-storage-sale-consumption" industry chain for natural gas. They play a significant role in stabilizing supply, balancing fluctuations in both supply and demand, and ensuring emergency gas supply. However, traditional operational strategies often lead to high energy consumption during gas injection, primarily due to their reliance on subjective decision-making, which fails to fully utilize historical data and lacks intelligent decision support. Therefore, optimizing gas injection into these underground storages is essential for maintaining their efficient and safe operation.
Methods The correlations among reservoir pressures, storage capacities, and single-well gas injection capacities were established through fitting, with the utilization of geological development data and injection-production operational data collected over repeated cycles. An integrated dynamic pressure calculation method for the gas injection process in UGS facilities was developed, incorporating these established relationships. This method subsequently enabled the dynamic updating of pressure calculation results for each link of the gas injection process. For gas injection optimization, the updated pressure levels serve as key input parameters and dynamic boundary conditions for objective function calculation and constraints establishment. Then a gas injection optimization method was formulated, taking the time of open gas injection wells as the decision variable and the energy consumption for gas injection and wellhead oil pressure stability as the optimization objectives, while defining constraints such as the planned total gas injection volume and gas injection period. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was employed to solve the Pareto frontier solution set. The economic efficiency and safety of various solutions were comprehensively evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify the optimal open well schemes and gas injection volumes.
Results The proposed method was utilized to formulate a gas injection scheme for an established gas storage facility, with the gas injection period and planned total gas injection volume set at 30 days and 1,300×10⁴ m³, respectively. The corresponding total energy consumption for gas injection was 8.002×10⁴ kW, while the standard deviation of wellhead oil pressure was 1.712 MPa. These results reflect reductions of 51.6％ and 81.6％, respectively, compared to the initial scheme, underscoring the effectiveness of the optimization.
Conclusion The integrated dynamic pressure calculation method for the gas injection process in UGS facilities allows for the comprehensive coordination of dynamically changing pressure relationships among reservoirs, wellbores, and surface pipeline networks. This approach effectively addresses the shortcomings of traditional methods that often overlook dynamic fluctuations in reservoir pressure and provides reliable support for enhancing the scientific rigor and practicality of gas injection optimization. Future research should focus on examining the dynamic coupling relationships among reservoir pressures, storage capacities, and single-well gas injection capacities, aiming for further improvement and optimization to address complex geological conditions or incomplete on-site data. Such efforts will promote advancements in underground gas storage facilities, leading to increased efficiency, safety, and intelligence.

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[1]	丁国生,丁一宸,李洋,唐立根,武志德,完颜祺琪,等. 碳中和战略下的中国地下储气库发展前景[J]. 油气储运,2022,41(1):1−9. DOI: 10.6047/j.issn.1000-8241.2022.01.001. DING G S, DING Y C, LI Y, TANG L G, WU Z D, WANYAN Q Q, et al. Prospects of underground gas storage in China under the strategy of carbon neutrality[J]. Oil & Gas Storage and Transportation, 2022, 41(1): 1−9. doi: 10.6047/j.issn.1000-8241.2022.01.001
[2]	李建君. 中国地下储气库发展现状及展望[J]. 油气储运,2022,41(7):780−786. DOI: 10.6047/j.issn.1000-8241.2022.07.004. LI J J. Development status and prospect of underground gas storage in China[J]. Oil & Gas Storage and Transportation, 2022, 41(7): 780−786. doi: 10.6047/j.issn.1000-8241.2022.07.004
[3]	文韵豪,王秋晨,巴玺立,李庆,班兴安,李昱江. 国内外智能化储气库现状及展望[J]. 油气与新能源,2022,34(6):60−64. DOI: 10.3969/j.issn.2097-0021.2022.06.008. WEN Y H, WANG Q C, BA X L, LI Q, BAN X A, LI Y J. Insights and forecast on domestic and international intelligent gas storage[J]. Petroleum and New Energy, 2022, 34(6): 60−64. doi: 10.3969/j.issn.2097-0021.2022.06.008
[4]	谭羽非. 天然气地下储气库技术及数值模拟[M]. 北京:石油工业出版社,2007:16−19. TAN Y F. Natural gas underground storage technology and numerical simulation[M]. Beijing: Petroleum Industry Press, 2007: 16−19.
[5]	段明雪. 地下储气库地面管网优化运行研究[D]. 北京:中国石油大学(北京),2018. DUAN M X. Optimization and operation of underground storage reservoir ground pipe network[D]. Beijing: China University of Petroleum (Beijing), 2018.
[6]	张刚雄,李彬,郑得文,丁国生,魏欢,钱品淑,等. 中国地下储气库业务面临的挑战及对策建议[J]. 天然气工业,2017,37(1):153−159. DOI: 10.3787/j.issn.1000-0976.2017.01.020. ZHANG G X, LI B, ZHENG D W, DING G S, WEI H, QIAN P S, et al. Challenges to and proposals for underground gas storage (UGS) business in China[J]. Natural Gas Industry, 2017, 37(1): 153−159. doi: 10.3787/j.issn.1000-0976.2017.01.020
[7]	LI D P, LIU W, FU P, LI L, BAN F S, LI Q H, et al. Stability evaluation of salt cavern hydrogen storage and optimization of operating parameters under high frequency injection production[J]. Gas Science and Engineering, 2023, 119(Part A): 205119. DOI: 10.1016/j.jgsce.2023.205119.
[8]	黄兴. 文96储气库地面注采系统运行优化研究[D]. 成都:西南石油大学,2016. HUANG X. Research on the optimization of the operation of the ground injection and extraction system of Wen96 gas storage reservoirs[D]. Chengdu: Southwest Petroleum University, 2016.
[9]	吴柯欣,田园,杨颖. 阶梯电价条件下储气库注气期压缩机启停方案优化研究[J]. 石化技术,2017,24(12):75−77. DOI: 10.3969/j.issn.1006-0235.2017.12.057. WU K X, TIAN Y, YANG Y. Optimization of start-up/shutdown scheme of compressor in gas storage period of gas storage at tiered pricing for electricity[J]. Petrochemical Industry Technology, 2017, 24(12): 75−77. doi: 10.3969/j.issn.1006-0235.2017.12.057
[10]	YU W C, GONG J, SONG S F, HUANG W H, LI Y C, ZHANG J, et al. Gas supply reliability analysis of a natural gas pipeline system considering the effects of underground gas storages[J]. Applied Energy, 2019, 252: 113418. DOI: 10.1016/j.apenergy.2019.113418.
[11]	CHEN Q, WU C C, ZUO L L, MEHRTASH M, WANG Y X, BU Y R, et al. Multi-objective transient peak shaving optimization of a gas pipeline system under demand uncertainty[J]. Computers & Chemical Engineering, 2021, 147: 107260. DOI: 10.1016/j.compchemeng.2021.107260.
[12]	EPARU C N, PRUNDUREL A P, DOUKEH R, STOICA D B, GHEȚIU I V, SUDITU S, et al. Optimizing underground natural gas storage capacity through numerical modeling and strategic well placement[J]. Processes, 2024, 12(10): 2136. DOI: 10.3390/pr12102136.
[13]	CURIN N, KETTLER M, KLEISINGER-YU X, KOMARIC V, KRABICHLER T, TEICHMANN J, et al. A deep learning model for gas storage optimization[J]. Decisions in Economics and Finance, 2021, 44(2): 1021−1037. DOI: 10.1007/s10203-021-00363-6.
[14]	BRKIĆ V, ZELENIKA I, MIJIĆ P, MEDVED I. Underground gas storage process optimisation with respect to reservoir parameters and production equipment[J]. Energies, 2021, 14(14): 4324. DOI: 10.3390/en14144324.
[15]	KANAANI M, SEDAEE B, ASADIAN-PAKFAR M, GILAVAND M, ALMAHMOUDI Z. Development of multi-objective co-optimization framework for underground hydrogen storage and carbon dioxide storage using machine learning algorithms[J]. Journal of Cleaner Production, 2023, 386: 135785. DOI: 10.1016/j.jclepro.2022.135785.
[16]	周军,彭井宏,罗莎,孙建华,梁光川,彭操. 考虑安全稳定运行的大型枯竭气藏储气库注采优化[J]. 特种油气藏,2021,28(6):76−82. DOI: 10.3969/j.issn.1006-6535.2021.06.010. ZHOU J, PENG J H, LUO S, SUN J H, LIANG G C, PENG C. Optimization of gas injection and production in gas storage based on large depleted gas reservoir with consideration of safe and stable operation[J]. Special Oil & Gas Reservoirs, 2021, 28(6): 76−82. doi: 10.3969/j.issn.1006-6535.2021.06.010
[17]	马新华,郑得文,魏国齐,丁国生,郑少婧. 中国天然气地下储气库重大科学理论技术发展方向[J]. 天然气工业,2022,42(5):93−99. DOI: 10.3787/j.issn.1000-0976.2022.05.010. MA X H, ZHENG D W, WEI G Q, DING G S, ZHENG S J. Development directions of major scientific theories and technologies for underground gas storage[J]. Natural Gas Industry, 2022, 42(5): 93−99. doi: 10.3787/j.issn.1000-0976.2022.05.010
[18]	糜利栋,曾大乾,刘华,郭艳东,李彦峰,李遵照,等. 天然气地下储气库智能化建设关键技术及其发展趋势[J]. 石油与天然气地质,2024,45(2):581−592. DOI: 10.11743/ogg20240220. MI L D, ZENG D Q, LIU H, GUO Y D, LI Y F, LI Z Z, et al. Key technologies and development trends for intelligent construction of underground gas storage facilities[J]. Oil & Gas Geology, 2024, 45(2): 581−592. doi: 10.11743/ogg20240220
[19]	杨颖,李世兵,陈子玮,陈家文,李鹏. 电驱式压缩机在地下储气库注气期经济运行的优化方案[J]. 天然气勘探与开发,2017,40(3):102−106. DOI: 10.12055/gaskk.issn.1673-3177.2017.03.016. YANG Y, LI S B, CHEN Z W, CHEN J W, LI P. Optimization measures to maintain economical operation of electrically driven compressor during gas injection in underground gas storage[J]. Natural Gas Exploration and Development, 2017, 40(3): 102−106. doi: 10.12055/gaskk.issn.1673-3177.2017.03.016
[20]	周军,彭井宏,孙建华,肖瑶,梁光川. 基于模糊综合评价法的气藏型储气库注采方案优选研究[J]. 石油科学通报,2021,6(3):494−504. DOI: 10.3969/j.issn.2096-1693.2021.03.040. ZHOU J, PENG J H, SUN J H, XIAO Y, LIANG G C. Study of the optimization of an injection and production scheme of gas storage in depleted gas reservoirs based on a fuzzy comprehensive evaluation method[J]. Petroleum Science Bulletin, 2021, 6(3): 494−504. doi: 10.3969/j.issn.2096-1693.2021.03.040
[21]	刘鹤. 储气库压力系统一体化研究[D]. 北京:中国石油大学(北京),2013. LIU H. Research on the integration of pressure system in underground gas storage[D]. Beijing: China University of Petroleum (Beijing), 2013.
[22]	温凯,何蕾,虞维超,宫敬,陈树仁. 枯竭油气藏型储气库地层压力的计算方法[J]. 油气储运,2017,36(7):781−788. DOI: 10.6047/j.issn.1000-8241.2017.07.006. WEN K, HE L, YU W C, GONG J, CHEN S R. Calculation methods on formation pressure of underground gas storage rebuilt from depleted oil and gas reservoir[J]. Oil & Gas Storage and Transportation, 2017, 36(7): 781−788. doi: 10.6047/j.issn.1000-8241.2017.07.006
[23]	葛家理,宁正福,刘月田,姚约东. 现代油藏渗流力学原理[M]. 北京:石油工业出版社,2001:36−58. GE J L, NING Z F, LIU Y T, YAO Y D. Principles of modern reservoir seepage mechanics[M]. Beijing: Petroleum Industry Press, 2001: 36−58.
[24]	刘慧,丁心鲁,张士杰,方云贵,郝晓波,郑玮鸽. 地下储气库注气过程一体化压力及地层参数计算方法[J]. 石油钻探技术,2022,50(6):64−71. DOI: 10.11911/syztjs.2022047. LIU H, DING X L, ZHANG S J, FANG Y G, HAO X B, ZHENG W G. Integrated calculation method of pressure and formation parameters in gas injection process of underground gas storage[J]. Petroleum Drilling Techniques, 2022, 50(6): 64−71. doi: 10.11911/syztjs.2022047
[25]	李长俊,黄泽俊,贾文龙,等. 天然气管道输送[M]. 北京:石油工业出版社,2023:71−75. LI C J, HUANG J J, JIA W L, et al. Natural gas pipeline transportation[M]. Beijing: Petroleum Industry Press, 2023: 71−75.
[26]	张帅,何应付,伦增珉,计秉玉. 李-凯斯勒方程的牛顿-二分求解法[J]. 科学技术与工程,2022,22(18):7859−7865. DOI: 10.3969/j.issn.1671-1815.2022.18.017. ZHANG S, HE Y F, LUN Z M, JI B Y. Hybrid newton-bisection algorithm for solving Lee-Kesler equation[J]. Science Technology and Engineering, 2022, 22(18): 7859−7865. doi: 10.3969/j.issn.1671-1815.2022.18.017
[27]	KENNEDY J, EBERHART R. Particle swarm optimization[C]. Perth: Proceedings of ICNN’95-International Conference on Neural Networks, 1995: 1942−1948.
[28]	COELLO C A C, PULIDO G T, LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256−279. DOI: 10.1109/TEVC.2004.826067.
[29]	TIAN Y, CHENG R, ZHANG X Y, JIN Y C. PlatEMO: a MATLAB platform for evolutionary multi-objective optimization [educational forum][J]. IEEE Computational Intelligence Magazine, 2017, 12(4): 73−87. DOI: 10.1109/MCI.2017.2742868.
[30]	ABIDO M A. Multiobjective particle swarm optimization for environmental/economic dispatch problem[J]. Electric Power Systems Research, 2009, 79(7): 1105−1113. DOI: 10.1016/j.jpgr.2009.02.005.
[31]	SAHOO N C, GANGULY S, DAS D. Simple heuristics-based selection of guides for multi-objective PSO with an application to electrical distribution system planning[J]. Engineering Applications of Artificial Intelligence, 2011, 24(4): 567−585. DOI: 10.1016/j.engappai.2011.02.007.
[32]	MOHD ZAIN M Z B, KANESAN J, CHUAH J H, DHANAPAL S, KENDALL G. A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization[J]. Applied Soft Computing, 2018, 70: 680−700. DOI: 10.1016/j.asoc.2018.06.022.
[33]	邢毓华,任甜甜. 改进MOPSO在微电网优化调度中的应用研究[J]. 太阳能学报,2024,45(6):191−200. DOI: 10.19912/j.0254-0096.tynxb.2023-0197. XING Y H, REN T T. Application research of improved MOPSO in microgrid optimal dispatch[J]. Acta Energiae Solaris Sinica, 2024, 45(6): 191−200. doi: 10.19912/j.0254-0096.tynxb.2023-0197
[34]	林扬,杨哲,袁壮,苟成冬,李传坤,王春利. 基于改进TOPSIS的石化装置实时状态评估[J]. 石油炼制与化工,2024,55(3):89−96. DOI: 10.3969/j.issn.1005-2399.2024.03.026. LIN Y, YANG Z, YUAN Z, GOU C D, LI C K, WANG C L. Real-time state evaluation of petrochemical plants based on improved TOPSIS[J]. Petroleum Processing and Petrochemicals, 2024, 55(3): 89−96. doi: 10.3969/j.issn.1005-2399.2024.03.026
[35]	WANG Y M, LIU P D, YAO Y Y. BMW-TOPSIS: a generalized TOPSIS model based on three-way decision[J]. Information Sciences, 2022, 607: 799−818. DOI: 10.1016/j.ins.2022.06.018.
[36]	贺玉晓,杨璐,杜颖,任玉芬,徐华山,韩旭,等. 基于改进组合赋权-TOPSIS模型的农村污水处理设施效果评价[J]. 环境科学学报,2024,44(4):421−428. DOI: 10.13671/j.hjkxxb.2023.0409. HE Y X, YANG L, DU Y, REN Y F, XU H S, HAN X, et al. Effect evaluation of rural sewage treatment facilities based on improved combination weighting-TOPSIS model[J]. Acta Scientiae Circumstantiae, 2024, 44(4): 421−428. doi: 10.13671/j.hjkxxb.2023.0409

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