IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2868-d1102169.html
   My bibliography  Save this article

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Author

Listed:
  • Xin Zhou

    (Research Institute of Petroleum Exploration and Development, No. 20 Xueyuan Road, Haidian District, Beijing 100083, China)

  • Qiquan Ran

    (Research Institute of Petroleum Exploration and Development, No. 20 Xueyuan Road, Haidian District, Beijing 100083, China)

Abstract

Shale gas reservoirs have extremely low porosity and permeability, making them challenging to exploit. The best method for increasing recovery in shale gas reservoirs is horizontal well fracturing technology. Hence, fracturing parameter optimization is necessary to enhance shale gas horizontal fracturing well production. Traditional optimization methods, however, cannot meet the requirements for overall optimization of fracturing parameters. As for intelligent optimization algorithms, most have excellent global search capability but incur high computation costs, which limits their usefulness in real-world engineering applications. Thus, a modified genetic algorithm combined based on the Spearman correlation coefficient (SGA) is proposed to achieve the rapid optimization of fracturing parameters. SGA determines the crossover and mutation rates by calculating the Spearman correlation coefficient instead of randomly determining the rates like GA does, so that it could quickly converge to the optimal solution. Within a particular optimization time, SGA could perform better than GA. In this study, a production prediction model is established by the XGBoost algorithm based on the dataset obtained by simulating the shale gas multistage fracturing horizontal well development. The result shows that the XGBoost model performs well in predicting shale gas fracturing horizontal well production. Based on the trained XGBoost model, GA, SGA, and SGD were used to optimize the fracturing parameters with the 30-day cumulative production as the optimization objective. This process has conducted nine fracturing parameter optimization tests under different porosity and permeability conditions. The results show that, compared with GA and SGD, SGA has faster speed and higher accuracy. This study’s findings can help optimize the fracturing parameters faster, resulting in improving the production of shale gas fracturing horizontal wells.

Suggested Citation

  • Xin Zhou & Qiquan Ran, 2023. "Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir," Energies, MDPI, vol. 16(6), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2868-:d:1102169
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2868/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/6/2868/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kun Ding & Yong Ni & Lingfeng Fan & Tian-Le Sun & Tabasam Rashid, 2022. "Optimal Design of Water Supply Network Based on Adaptive Penalty Function and Improved Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    2. Frank Umbach, 2013. "The unconventional gas revolution and the prospects for Europe and Asia," Asia Europe Journal, Springer, vol. 11(3), pages 305-322, September.
    3. Reymond, Mathias, 2007. "European key issues concerning natural gas: Dependence and vulnerability," Energy Policy, Elsevier, vol. 35(8), pages 4169-4176, August.
    4. Zhenzhen Dong & Lei Wu & Linjun Wang & Weirong Li & Zhengbo Wang & Zhaoxia Liu, 2022. "Optimization of Fracturing Parameters with Machine-Learning and Evolutionary Algorithm Methods," Energies, MDPI, vol. 15(16), pages 1-22, August.
    5. Barnes, Ryan & Bosworth, Ryan, 2015. "LNG is linking regional natural gas markets: Evidence from the gravity model," Energy Economics, Elsevier, vol. 47(C), pages 11-17.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoy, Kyle A. & Wrenn, Douglas H., 2018. "Unconventional energy, taxation, and interstate welfare: An analysis of Pennsylvania's severance tax policy," Energy Economics, Elsevier, vol. 73(C), pages 53-65.
    2. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Dynamic frequency relationships and volatility spillovers in natural gas, crude oil, gas oil, gasoline, and heating oil markets: Implications for portfolio management," Resources Policy, Elsevier, vol. 73(C).
    3. Su, Chi-Wei & Yuan, Xi & Umar, Muhammad & Chang, Tsangyao, 2022. "Dynamic price linkage of energies in transformation: Evidence from quantile connectedness," Resources Policy, Elsevier, vol. 78(C).
    4. Chiappini, Raphaël & Jégourel, Yves & Raymond, Paul, 2019. "Towards a worldwide integrated market? New evidence on the dynamics of U.S., European and Asian natural gas prices," Energy Economics, Elsevier, vol. 81(C), pages 545-565.
    5. Aalto, Pami, 2014. "Energy market integration and regional institutions in east Asia," Energy Policy, Elsevier, vol. 74(C), pages 91-100.
    6. Theodosios Perifanis & Athanasios Dagoumas, 2020. "Price and Volatility Spillovers between Crude Oil and Natural Gas markets in Europe and Japan-Korea," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 432-446.
    7. Yan, Zhaojin & Yang, Guanghao & He, Rong & Yang, Hui & Ci, Hui, 2023. "“Ship-port-country” multi-dimensional research on the fine analysis of China's LNG trade," Journal of Transport Geography, Elsevier, vol. 110(C).
    8. Farag, Markos & Zaki, Chahir, 2021. "On the Determinants of Trade in Natural Gas: A Political Economy Approach," EWI Working Papers 2021-8, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    9. Baba, Amina & Creti, Anna & Massol, Olivier, 2020. "What can be learned from the free destination option in the LNG imbroglio?," Energy Economics, Elsevier, vol. 89(C).
    10. Wang, Tiantian & Qu, Wan & Zhang, Dayong & Ji, Qiang & Wu, Fei, 2022. "Time-varying determinants of China's liquefied natural gas import price: A dynamic model averaging approach," Energy, Elsevier, vol. 259(C).
    11. Misund, Bård & Oglend, Atle, 2016. "Supply and demand determinants of natural gas price volatility in the U.K.: A vector autoregression approach," Energy, Elsevier, vol. 111(C), pages 178-189.
    12. Yuping Jin & Yanbin Yang & Wei Liu, 2022. "Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    13. Mathias Reymond, 2014. "Why ECO countries could become energy-dependent?," 2nd International Conference on Energy, Regional Integration and Socio-Economic Development 7722, EcoMod.
    14. Lars Lindholt & Solveig Glomsrød, 2017. "Phasing out coal and phasing in renewables – good or bad news for arctic gas producers?," Discussion Papers 856, Statistics Norway, Research Department.
    15. Larsen, Erik R. & Osorio, Sebastian & van Ackere, Ann, 2017. "A framework to evaluate security of supply in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 646-655.
    16. Zafirakis, D. & Kaldellis, J.K., 2009. "Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks," Energy Policy, Elsevier, vol. 37(5), pages 1958-1969, May.
    17. Moreno, Blanca & García-Álvarez, María Teresa & Ramos, Carmen & Fernández-Vázquez, Esteban, 2014. "A General Maximum Entropy Econometric approach to model industrial electricity prices in Spain: A challenge for the competitiveness," Applied Energy, Elsevier, vol. 135(C), pages 815-824.
    18. Yanbo Feng & Han Zhu & Xiwen Feng & Qianru Chen & Xiangyu Sun & Zhengrong Li, 2023. "Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm," Energies, MDPI, vol. 16(24), pages 1-15, December.
    19. Lee, Siu Ming & Adam, Radziah & Tuan Lonik, Ku’ Azam, 2020. "The Effects of Tax and Promotion on Rubber Medical Devices Export," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 54(2), pages 29-40.
    20. Lindholt, Lars & Glomsrød, Solveig, 2018. "Phasing out coal and phasing in renewables – Good or bad news for arctic gas producers?," Energy Economics, Elsevier, vol. 70(C), pages 1-11.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2868-:d:1102169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.