IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v250y2022ics0360544222007150.html
   My bibliography  Save this article

Development of visual prediction model for shale gas wells production based on screening main controlling factors

Author

Listed:
  • Niu, Wente
  • Lu, Jialiang
  • Sun, Yuping
  • Guo, Wei
  • Liu, Yuyang
  • Mu, Ying

Abstract

For shale gas development, clarification of the main controlling factors of production and estimated ultimate recovery (EUR) with high accuracy is indispensable. The selection of 16 critical parameters directed toward the visual output of the objective function were the most influential factors determined through a sensitivity analysis. Based on the fundamental parameters, the distance correlation coefficient was used to clarify the main controlling factors affecting the EUR of shale gas wells in Weiyuan block. Then, visual forecasting models of EUR were established using Response Surface Method (RSM), Multi-layer Feedforward Neural Network (MLFNN) and Least Square Support Vector Machine (LSSVM). Furthermore, the models developed by the three methods are compared and analyzed. The field application results of the model indicated that the model based on the LSSVM has the best field application effect. The proposed model is a serviceable tool for EUR prediction. In addition, the use of the model is efficient and convenient, and only six main controlling factors can be used to achieve the prediction of EUR. The results of this study can be extended as the main controlling factors analysis and the development of EUR visual model of shale gas wells in other blocks.

Suggested Citation

  • Niu, Wente & Lu, Jialiang & Sun, Yuping & Guo, Wei & Liu, Yuyang & Mu, Ying, 2022. "Development of visual prediction model for shale gas wells production based on screening main controlling factors," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007150
    DOI: 10.1016/j.energy.2022.123812
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222007150
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123812?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. Wente Niu & Jialiang Lu & Yuping Sun, 2021. "A Production Prediction Method for Shale Gas Wells Based on Multiple Regression," Energies, MDPI, vol. 14(5), pages 1-11, March.
    2. Chen, Shangbin & Zhu, Yanming & Wang, Hongyan & Liu, Honglin & Wei, Wei & Fang, Junhua, 2011. "Shale gas reservoir characterisation: A typical case in the southern Sichuan Basin of China," Energy, Elsevier, vol. 36(11), pages 6609-6616.
    3. 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.
    4. 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. Niu, Wente & Sun, Yuping & Zhang, Xiaowei & Lu, Jialiang & Liu, Hualin & Li, Qiaojing & Mu, Ying, 2023. "An ensemble transfer learning strategy for production prediction of shale gas wells," Energy, Elsevier, vol. 275(C).

    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. 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).
    2. 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).
    3. Yin, Hong & Zhou, Junping & Xian, Xuefu & Jiang, Yongdong & Lu, Zhaohui & Tan, Jingqiang & Liu, Guojun, 2017. "Experimental study of the effects of sub- and super-critical CO2 saturation on the mechanical characteristics of organic-rich shales," Energy, Elsevier, vol. 132(C), pages 84-95.
    4. 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.
    5. Zhou, Yuanchun & Ma, Mengdie & Gao, Peiqi & Xu, Qiming & Bi, Jun & Naren, Tuya, 2019. "Managing water resources from the energy - water nexus perspective under a changing climate: A case study of Jiangsu province, China," Energy Policy, Elsevier, vol. 126(C), pages 380-390.
    6. Xiaoyan Zou & Xianqing Li & Jizhen Zhang & Huantong Li & Man Guo & Pei Zhao, 2021. "Characteristics of Pore Structure and Gas Content of the Lower Paleozoic Shale from the Upper Yangtze Plate, South China," Energies, MDPI, vol. 14(22), pages 1-29, November.
    7. Qin, Chao & Jiang, Yongdong & Zuo, Shuangying & Chen, Shiwan & Xiao, Siyou & Liu, Zhengjie, 2021. "Investigation of adsorption kinetics of CH4 and CO2 on shale exposure to supercritical CO2," Energy, Elsevier, vol. 236(C).
    8. Chen, Junqing & Jiang, Fujie & Cong, Qi & Pang, Xiongqi & Ma, Kuiyou & Shi, Kanyuan & Pang, Bo & Chen, Dongxia & Pang, Hong & Yang, Xiaobin & Wang, Yuying & Li, Bingyao, 2023. "Adsorption characteristics of shale gas in organic–inorganic slit pores," Energy, Elsevier, vol. 278(C).
    9. 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).
    10. Zhiyao Zhang & Shang Xu & Qiyang Gou & Qiqi Li, 2022. "Reservoir Characteristics and Resource Potential of Marine Shale in South China: A Review," Energies, MDPI, vol. 15(22), pages 1-21, November.
    11. Yuanyuan Tian & Qing Chen & Changhui Yan & Hongde Chen & Yanqing He & Yufeng He, 2022. "A New Adsorption Equation for Nano-Porous Shale Rocks and Its Application in Pore Size Distribution Analysis," Energies, MDPI, vol. 15(9), pages 1-13, April.
    12. Yang, Xu & Zhou, Wenning & Liu, Xunliang & Yan, Yuying, 2020. "A multiscale approach for simulation of shale gas transport in organic nanopores," Energy, Elsevier, vol. 210(C).
    13. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    14. 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.
    15. Dai, Xuguang & Wei, Chongtao & Wang, Meng & Ma, Ruying & Song, Yu & Zhang, Junjian & Wang, Xiaoqi & Shi, Xuan & Vandeginste, Veerle, 2023. "Interaction mechanism of supercritical CO2 with shales and a new quantitative storage capacity evaluation method," Energy, Elsevier, vol. 264(C).
    16. 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).
    17. 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).
    18. Wang, Wenyang & Pang, Xiongqi & Chen, Zhangxin & Chen, Dongxia & Zheng, Tianyu & Luo, Bing & Li, Jing & Yu, Rui, 2019. "Quantitative prediction of oil and gas prospects of the Sinian-Lower Paleozoic in the Sichuan Basin in central China," Energy, Elsevier, vol. 174(C), pages 861-872.
    19. Wang, Sha & Jiang, Xiumin & Han, Xiangxin & Tong, Jianhui, 2012. "Investigation of Chinese oil shale resources comprehensive utilization performance," Energy, Elsevier, vol. 42(1), pages 224-232.
    20. 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).

    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:energy:v:250:y:2022:i:c:s0360544222007150. 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.journals.elsevier.com/energy .

    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.