IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p2765-d365647.html
   My bibliography  Save this article

The Evaluation and Sensitivity of Decline Curve Modelling

Author

Listed:
  • Prinisha Manda

    (Oil and Gas Production and Processing Research Unit, School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg 2000, South Africa)

  • Diakanua Bavon Nkazi

    (Oil and Gas Production and Processing Research Unit, School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg 2000, South Africa)

Abstract

The development of prediction tools for production performance and the lifespan of shale gas reservoirs has been a focus for petroleum engineers. Several decline curve models have been developed and compared with data from shale gas production. To accurately forecast the estimated ultimate recovery for shale gas reservoirs, consistent and accurate decline curve modelling is required. In this paper, the current decline curve models are evaluated using the goodness of fit as a measure of accuracy with field data. The evaluation found that there are advantages in using the current DCA models; however, they also have limitations associated with them that have to be addressed. Based on the accuracy assessment conducted on the different models, it appears that the Stretched Exponential Decline Model (SEDM) and Logistic Growth Model (LGM), followed by the Extended Exponential Decline Model (EEDM), the Power Law Exponential Model (PLE), the Doung’s Model, and lastly, the Arps Hyperbolic Decline Model, provide the best fit with production data.

Suggested Citation

  • Prinisha Manda & Diakanua Bavon Nkazi, 2020. "The Evaluation and Sensitivity of Decline Curve Modelling," Energies, MDPI, vol. 13(11), pages 1-16, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2765-:d:365647
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/2765/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/11/2765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaoyang Zhang & Xiaodong Wang & Xiaochun Hou & Wenli Xu, 2017. "Rate Decline Analysis of Vertically Fractured Wells in Shale Gas Reservoirs," Energies, MDPI, vol. 10(10), pages 1-24, October.
    2. 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.
    3. Yuan, Jiehui & Luo, Dongkun & Feng, Lianyong, 2015. "A review of the technical and economic evaluation techniques for shale gas development," Applied Energy, Elsevier, vol. 148(C), pages 49-65.
    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. Ruud Weijermars, 2022. "Gaussian Decline Curve Analysis of Hydraulically Fractured Wells in Shale Plays: Examples from HFTS-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)," Energies, MDPI, vol. 15(17), pages 1-23, September.
    2. Catalin Popescu & Sorin Alexandru Gheorghiu, 2021. "Economic Analysis and Generic Algorithm for Optimizing the Investments Decision-Making Process in Oil Field Development," Energies, MDPI, vol. 14(19), pages 1-24, September.
    3. Taha Yehia & Ahmed Naguib & Mostafa M. Abdelhafiz & Gehad M. Hegazy & Omar Mahmoud, 2023. "Probabilistic Decline Curve Analysis: State-of-the-Art Review," Energies, MDPI, vol. 16(10), pages 1-20, May.

    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. Huang, Liang & Ning, Zhengfu & Wang, Qing & Zhang, Wentong & Cheng, Zhilin & Wu, Xiaojun & Qin, Huibo, 2018. "Effect of organic type and moisture on CO2/CH4 competitive adsorption in kerogen with implications for CO2 sequestration and enhanced CH4 recovery," Applied Energy, Elsevier, vol. 210(C), pages 28-43.
    2. Jia Liu & Jianguo Wang & Chunfai Leung & Feng Gao, 2018. "A Fully Coupled Numerical Model for Microwave Heating Enhanced Shale Gas Recovery," Energies, MDPI, vol. 11(6), pages 1-28, June.
    3. Zhou, Junping & Tian, Shifeng & Zhou, Lei & Xian, Xuefu & Yang, Kang & Jiang, Yongdong & Zhang, Chengpeng & Guo, Yaowen, 2020. "Experimental investigation on the influence of sub- and super-critical CO2 saturation time on the permeability of fractured shale," Energy, Elsevier, vol. 191(C).
    4. Xiaoqian Guo & Qiang Yan & Anjian Wang, 2017. "Assessment of Methods for Forecasting Shale Gas Supply in China Based on Economic Considerations," Energies, MDPI, vol. 10(11), pages 1-14, October.
    5. Xuhua Gao & Junhong Yu & Xinchun Shang & Weiyao Zhu, 2023. "Investigation on Nonlinear Behaviors of Seepage in Deep Shale Gas Reservoir with Viscoelasticity," Energies, MDPI, vol. 16(17), pages 1-23, August.
    6. Xuechen Li & Xinfang Ma & Fengchao Xiao & Fei Wang & Shicheng Zhang, 2020. "Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction," Energies, MDPI, vol. 13(22), pages 1-22, November.
    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. Kim, Tae Hong & Cho, Jinhyung & Lee, Kun Sang, 2017. "Evaluation of CO2 injection in shale gas reservoirs with multi-component transport and geomechanical effects," Applied Energy, Elsevier, vol. 190(C), pages 1195-1206.
    9. 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).
    10. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
    11. Lin Tan & Lingzhi Xie & Bo He & Yao Zhang, 2024. "Multi-Fracture Propagation Considering Perforation Erosion with Respect to Multi-Stage Fracturing in Shale Reservoirs," Energies, MDPI, vol. 17(4), pages 1-21, February.
    12. Zou, Youqin & Yang, Changbing & Wu, Daishe & Yan, Chun & Zeng, Masun & Lan, Yingying & Dai, Zhenxue, 2016. "Probabilistic assessment of shale gas production and water demand at Xiuwu Basin in China," Applied Energy, Elsevier, vol. 180(C), pages 185-195.
    13. 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).
    14. Taha Yehia & Ahmed Naguib & Mostafa M. Abdelhafiz & Gehad M. Hegazy & Omar Mahmoud, 2023. "Probabilistic Decline Curve Analysis: State-of-the-Art Review," Energies, MDPI, vol. 16(10), pages 1-20, May.
    15. Li, Yanbin & Li, Yun & Wang, Bingqian & Chen, Zhuoer & Nie, Dan, 2016. "The status quo review and suggested policies for shale gas development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 420-428.
    16. Yang, Yan & Wang, Limao & Fang, Yebing & Mou, Chufu, 2017. "Integrated value of shale gas development: A comparative analysis in the United States and China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1465-1478.
    17. 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).
    18. Wang, Yan & Zhong, Dong-Liang & Li, Zheng & Li, Jian-Bo, 2020. "Application of tetra-n-butyl ammonium bromide semi-clathrate hydrate for CO2 capture from unconventional natural gases," Energy, Elsevier, vol. 197(C).
    19. Louis Delannoy & Pierre-Yves Longaretti & David. J. Murphy & Emmanuel Prados, 2021. "Assessing Global Long-Term EROI of Gas: A Net-Energy Perspective on the Energy Transition," Energies, MDPI, vol. 14(16), pages 1-16, August.
    20. 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).

    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:13:y:2020:i:11:p:2765-:d:365647. 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.