IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v350y2023ics0306261923010930.html
   My bibliography  Save this article

New methods of coalbed methane production analysis based on the generalized gamma distribution and field applications

Author

Listed:
  • Zhang, Xian-min
  • Chen, Bai-yan-yue
  • Zheng, Zhuang-zhuang
  • Feng, Qi-hong
  • Fan, Bin

Abstract

Decline curve analysis is a simple and fast regression method of estimating recoverable reserves and future performance, but it is challenged when applied to unconventional reservoirs. Based on a generalized Gamma distribution in the real number field, a theoretical forecasting model describing the whole-process change of coalbed methane well production was constructed, that is, the gas production can be estimated by the early rising section data and a few declining section data, or even just the early rising section data. Furthermore, a generalized production decline analysis model was proposed, which can match all flow regimes in coalbed methane reservoirs as well as in shale gas and tight gas reservoirs. Particularly, the Arps exponential decline model, Duong model, and SEPD model are some of its special forms. By analyzing the production of typical wells in the Zhengzhuang-Fanzhuang block of China, it has demonstrated that the generalized production forecasting model performs significantly better than the Duong model in terms of data fitting accuracy and prediction accuracy, and in comparison to the Arps model, PLE model, SEPD model and Duong model, the generalized production decline model has also demonstrated greater scientific validity and field applicability for predicting coalbed methane production. The MAPE value between the predicted and actual gas production ranges from 4.46% to 8.82%, with an average of 5.75%, and the RRMSE between them ranges from 6.24% to 9.74%, with an average of 7.53%, which are both significantly lower than those provided by the Arps model, PLE model, SEPD model, and Duong model.

Suggested Citation

  • Zhang, Xian-min & Chen, Bai-yan-yue & Zheng, Zhuang-zhuang & Feng, Qi-hong & Fan, Bin, 2023. "New methods of coalbed methane production analysis based on the generalized gamma distribution and field applications," Applied Energy, Elsevier, vol. 350(C).
  • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923010930
    DOI: 10.1016/j.apenergy.2023.121729
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923010930
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121729?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Ke & Li, Haitao & Wang, Junchao & Jiang, Beibei & Bu, Chengzhong & Zhang, Qing & Luo, Wei, 2017. "Predicting production and estimated ultimate recoveries for shale gas wells: A new methodology approach," Applied Energy, Elsevier, vol. 206(C), pages 1416-1431.
    2. Yang, Run & Liu, Xiangui & Yu, Rongze & Hu, Zhiming & Duan, Xianggang, 2022. "Long short-term memory suggests a model for predicting shale gas production," Applied Energy, Elsevier, vol. 322(C).
    3. Lei Tan & Lihua Zuo & Binbin Wang, 2018. "Methods of Decline Curve Analysis for Shale Gas Reservoirs," Energies, MDPI, vol. 11(3), pages 1-18, March.
    4. You, Xu-Tao & Liu, Jian-Yi & Jia, Chun-Sheng & Li, Jun & Liao, Xin-Yi & Zheng, Ai-Wei, 2019. "Production data analysis of shale gas using fractal model and fuzzy theory: Evaluating fracturing heterogeneity," Applied Energy, Elsevier, vol. 250(C), pages 1246-1259.
    5. Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chengwang Wang & Haifeng Zhao & Zhan Liu & Tengfei Wang & Gaojie Chen, 2024. "A Fully Coupled Gas–Water–Solids Mathematical Model for Vertical Well Drainage of Coalbed Methane," Energies, MDPI, vol. 17(6), pages 1-22, March.

    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. Yang, Run & Liu, Xiangui & Yu, Rongze & Hu, Zhiming & Duan, Xianggang, 2022. "Long short-term memory suggests a model for predicting shale gas production," Applied Energy, Elsevier, vol. 322(C).
    2. Zhou, Wei & Li, Xiangchengzhen & Qi, ZhongLi & Zhao, HaiHang & Yi, Jun, 2024. "A shale gas production prediction model based on masked convolutional neural network," Applied Energy, Elsevier, vol. 353(PA).
    3. Peng Li & Ju Liu & Cuiping Wei, 2019. "A Dynamic Decision Making Method Based on GM(1,1) Model with Pythagorean Fuzzy Numbers for Selecting Waste Disposal Enterprises," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    4. Nguyen, Phong & Carey, J. William & Viswanathan, Hari S. & Porter, Mark, 2018. "Effectiveness of supercritical-CO2 and N2 huff-and-puff methods of enhanced oil recovery in shale fracture networks using microfluidic experiments," Applied Energy, Elsevier, vol. 230(C), pages 160-174.
    5. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    6. Gou, Qiyang & Xu, Shang & Hao, Fang & Yang, Feng & Shu, Zhiguo & Liu, Rui, 2021. "The effect of tectonic deformation and preservation condition on the shale pore structure using adsorption-based textural quantification and 3D image observation," Energy, Elsevier, vol. 219(C).
    7. Jin, Xu & Wang, Xiaoqi & Yan, Weipeng & Meng, Siwei & Liu, Xiaodan & Jiao, Hang & Su, Ling & Zhu, Rukai & Liu, He & Li, Jianming, 2019. "Exploration and casting of large scale microscopic pathways for shale using electrodeposition," Applied Energy, Elsevier, vol. 247(C), pages 32-39.
    8. Li, Dezhi & Huang, Guanying & Zhu, Shiyao & Chen, Long & Wang, Jiangbo, 2021. "How to peak carbon emissions of provincial construction industry? Scenario analysis of Jiangsu Province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Prinisha Manda & Diakanua Bavon Nkazi, 2020. "The Evaluation and Sensitivity of Decline Curve Modelling," Energies, MDPI, vol. 13(11), pages 1-16, June.
    10. Niu, Wente & Lu, Jialiang & Sun, Yuping & Zhang, Xiaowei & Li, Qiaojing & Cao, Xu & Liang, Pingping & Zhan, Hongming, 2024. "Techno-economic integration evaluation in shale gas development based on ensemble learning," Applied Energy, Elsevier, vol. 357(C).
    11. Wang, Lian & Yao, Yuedong & Wang, Kongjie & Adenutsi, Caspar Daniel & Zhao, Guoxiang & Lai, Fengpeng, 2022. "Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs," Energy, Elsevier, vol. 243(C).
    12. Gu, Haolei & Wu, Lifeng, 2024. "Pulse fractional grey model application in forecasting global carbon emission," Applied Energy, Elsevier, vol. 358(C).
    13. Qin, Chao & Jiang, Yongdong & Luo, Yahuang & Zhou, Junping & Liu, Hao & Song, Xiao & Li, Dong & Zhou, Feng & Xie, Yingliang, 2020. "Effect of supercritical CO2 saturation pressures and temperatures on the methane adsorption behaviours of Longmaxi shale," Energy, Elsevier, vol. 206(C).
    14. Gong, Jianming & Qiu, Zhen & Zou, Caineng & Wang, Hongyan & Shi, Zhensheng, 2020. "An integrated assessment system for shale gas resources associated with graptolites and its application," Applied Energy, Elsevier, vol. 262(C).
    15. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    16. Wang, Zheng-Xin & Li, Dan-Dan & Zheng, Hong-Hao, 2020. "Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    17. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
    18. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
    19. Bo Zeng & Shuliang Li & Wei Meng & Dehai Zhang, 2019. "An improved gray prediction model for China’s beef consumption forecasting," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-18, September.
    20. Zhijun Feng & Bo Zeng & Qian Ming, 2018. "Environmental Regulation, Two-Way Foreign Direct Investment, and Green Innovation Efficiency in China’s Manufacturing Industry," IJERPH, MDPI, vol. 15(10), pages 1-22, October.

    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:eee:appene:v:350:y:2023:i:c:s0306261923010930. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.