IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10645-d1536773.html
   My bibliography  Save this article

Stochastic Risk Assessment Framework of Deep Shale Reservoirs by a Deep Learning Method and Random Field Theory

Author

Listed:
  • Tao Wang

    (SINOPEC Key Laboratory of Geology and Resources in Deep Stratum, Beijing 100083, China
    SINOPEC Petroleum Exploration and Production Research Institute, Beijing 102206, China
    Shandong Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China
    State Key Laboratory for Geomechanics and Deep Underground Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Shuangjian Li

    (SINOPEC Key Laboratory of Geology and Resources in Deep Stratum, Beijing 100083, China
    SINOPEC Petroleum Exploration and Production Research Institute, Beijing 102206, China)

  • Jian Gao

    (SINOPEC Key Laboratory of Geology and Resources in Deep Stratum, Beijing 100083, China
    SINOPEC Petroleum Exploration and Production Research Institute, Beijing 102206, China)

  • Xuepeng Zhang

    (Shandong Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China)

  • Miao Chen

    (Shandong Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

Risk assessment of deep shale reservoirs is very important for subsurface energy development. However, due to complex geological environments and physicochemical interactions, shale reservoir fabric parameters exhibit variability. Moreover, the actual investigation and testing information is very limited, which is a typical small-sample problem. In this paper, the heterogeneity and statistical characteristics of deep shale reservoirs are clarified by the measured mechanical parameters. A deep learning method for deep shale reservoirs with limited survey data information is proposed. The variability of deep shale reservoirs is characterized by random field theory. A variable stiffness method and stochastic analysis method are developed to evaluate the risk of deep shale reservoirs. The detailed workflow of the stochastic risk assessment framework is presented. The frequency distribution and failure risk of deep shale reservoirs are calculated and analyzed. The risk assessment of deep shale reservoirs under different model parameters is discussed. The results show that a stochastic risk assessment framework of deep shale reservoirs, using a deep learning method and random field theory, is scientifically reasonable. The scatter plots of the elasticity modulus (EM), cohesive force (CF), and Poisson ratio (PR) distribute along the 45-degree line. The different distributed variables of EM, CF, and PR have a positive correlation. The statistical properties of the measurement data and deep learning data are approximately the same. The principal stress of deep shale follows the normal distribution with significance level 0.1. Under positive copula conditions, the maximum failure probability is 5.99%. Under negative copula conditions, the maximum failure probability is 4.58%. Different copula functions under positive and negative copula conditions have different failure probabilities. For the exponential correlation structure, the minimum failure probability is 3.46%, while the maximum failure probability is 6.19%. The mean failure probability of the EM, CF, and PR of deep shale reservoirs is 4.85%. Different random field-related structures and parameters have different influences on the failure risk.

Suggested Citation

  • Tao Wang & Shuangjian Li & Jian Gao & Xuepeng Zhang & Miao Chen, 2024. "Stochastic Risk Assessment Framework of Deep Shale Reservoirs by a Deep Learning Method and Random Field Theory," Sustainability, MDPI, vol. 16(23), pages 1-20, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10645-:d:1536773
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10645/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/23/10645/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei, Zijian & Sheng, J.J., 2022. "Changes of pore structures and permeability of the Chang 73 medium-to-low maturity shale during in-situ heating treatment," Energy, Elsevier, vol. 248(C).
    2. Xie, Weidong & Wang, Meng & Chen, Si & Vandeginste, Veerle & Yu, Zhenghong & Wang, Hua, 2022. "Effects of gas components, reservoir property and pore structure of shale gas reservoir on the competitive adsorption behavior of CO2 and CH4," Energy, Elsevier, vol. 254(PB).
    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. Liu, Bo & Mohammadi, Mohammad-Reza & Ma, Zhongliang & Bai, Longhui & Wang, Liu & Xu, Yaohui & Hemmati-Sarapardeh, Abdolhossein & Ostadhassan, Mehdi, 2023. "Pore structure evolution of Qingshankou shale (kerogen type I) during artificial maturation via hydrous and anhydrous pyrolysis: Experimental study and intelligent modeling," Energy, Elsevier, vol. 282(C).
    2. Wei, Jianguang & Fu, Lanqing & Zhao, Guozhong & Zhao, Xiaoqing & Liu, Xinrong & Wang, Anlun & Wang, Yan & Cao, Sheng & Jin, Yuhan & Yang, Fengrui & Liu, Tianyang & Yang, Ying, 2023. "Nuclear magnetic resonance study on imbibition and stress sensitivity of lamellar shale oil reservoir," Energy, Elsevier, vol. 282(C).
    3. Li, Bo & Yu, Hao & Xu, WenLong & Huang, HanWei & Huang, MengCheng & Meng, SiWei & Liu, He & Wu, HengAn, 2023. "A multi-physics coupled multi-scale transport model for CO2 sequestration and enhanced recovery in shale formation with fractal fracture networks," Energy, Elsevier, vol. 284(C).
    4. 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).
    5. Wang, Han & Zhang, Mingshan & Xia, Xuanzhe & Tian, Zhenhua & Qin, Xiangjie & Cai, Jianchao, 2024. "Lattice Boltzmann prediction of CO2 and CH4 competitive adsorption in shale porous media accelerated by machine learning for CO2 sequestration and enhanced CH4 recovery," Applied Energy, Elsevier, vol. 370(C).
    6. Pan, Bin & Yin, Xia & Yang, Zhengru & Ghanizadeh, Amin & Debuhr, Chris & Clarkson, Christopher R. & Gou, Feifei & Zhu, Weiyao & Ju, Yang & Iglauer, Stefan, 2024. "Real-time imaging of oil shale pyrolysis dynamics at nanoscale via environmental scanning electron microscopy," Applied Energy, Elsevier, vol. 363(C).
    7. Wu, Jian & Gan, Yixiang & Shi, Zhang & Huang, Pengyu & Shen, Luming, 2023. "Pore-scale lattice Boltzmann simulation of CO2-CH4 displacement in shale matrix," Energy, Elsevier, vol. 278(PB).
    8. Liu, Bo & Mohammadi, Mohammad-Reza & Ma, Zhongliang & Bai, Longhui & Wang, Liu & Xu, Yaohui & Hemmati-Sarapardeh, Abdolhossein & Ostadhassan, Mehdi, 2023. "Pore structure characterization of solvent extracted shale containing kerogen type III during artificial maturation: Experiments and tree-based machine learning modeling," Energy, Elsevier, vol. 283(C).
    9. Micheal, Marembo & Yu, Hao & Meng, SiWei & Xu, WenLong & Huang, HanWei & Huang, MengCheng & Zhang, HouLin & Liu, He & Wu, HengAn, 2023. "Gas production from shale reservoirs with bifurcating fractures: A modified quadruple-domain model coupling microseismic events," Energy, Elsevier, vol. 278(C).
    10. Wei, Jianguang & Li, Jiangtao & Zhang, Ao & Shang, Demiao & Zhou, Xiaofeng & Niu, Yintao, 2023. "Influence of shale bedding on development of microscale pores and fractures," Energy, Elsevier, vol. 282(C).
    11. Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).
    12. Huang, Xudong & Kang, Zhiqin & Zhao, Jing & Wang, Guoying & Zhang, Hongge & Yang, Dong, 2023. "Experimental investigation on micro-fracture evolution and fracture permeability of oil shale heated by water vapor," Energy, Elsevier, vol. 277(C).
    13. Yang, Hongmin & Kang, Ningning & Chen, Xiangjun & Liu, Yuan, 2023. "Exploring the inhibitory effect of H2O on CO2/CH4 adsorption in coal: Insights from experimental and simulation approaches," Energy, Elsevier, vol. 284(C).
    14. Huang, HanWei & Yu, Hao & Xu, WenLong & Lyu, ChengSi & Micheal, Marembo & Xu, HengYu & Liu, He & Wu, HengAn, 2023. "A coupled thermo-hydro-mechanical-chemical model for production performance of oil shale reservoirs during in-situ conversion process," Energy, Elsevier, vol. 268(C).
    15. Wang, Chao & Liu, Bo & Mohammadi, Mohammad-Reza & Fu, Li & Fattahi, Elham & Motra, Hem Bahadur & Hazra, Bodhisatwa & Hemmati-Sarapardeh, Abdolhossein & Ostadhassan, Mehdi, 2024. "Integrating experimental study and intelligent modeling of pore evolution in the Bakken during simulated thermal progression for CO2 storage goals," Applied Energy, Elsevier, vol. 359(C).
    16. Wang, Zengding & Liu, Tengyu & Liu, Shanchao & Jia, Cunqi & Yao, Jun & Sun, Hai & Yang, Yongfei & Zhang, Lei & Delshad, Mojdeh & Sepehrnoori, Kamy & Zhong, Junjie, 2024. "Adsorption effects on CO2-oil minimum miscibility pressure in tight reservoirs," Energy, Elsevier, vol. 288(C).
    17. Gao, Zheng & Li, Bobo & Li, Jianhua & Jia, Lidan & Wang, Zhonghui, 2023. "Adsorption characteristics and thermodynamic analysis of shale in northern Guizhou, China: Measurement, modeling and prediction," Energy, Elsevier, vol. 262(PA).
    18. Cui, Ziang & Sun, Mengdi & Mohammadian, Erfan & Hu, Qinhong & Liu, Bo & Ostadhassan, Mehdi & Yang, Wuxing & Ke, Yubin & Mu, Jingfu & Ren, Zijie & Pan, Zhejun, 2024. "Characterizing microstructural evolutions in low-mature lacustrine shale: A comparative experimental study of conventional heat, microwave, and water-saturated microwave stimulations," Energy, Elsevier, vol. 294(C).
    19. Wu, Jian & Shen, Luming & Huang, Pengyu & Gan, Yixiang, 2023. "Selective adsorption and transport of CO2–CH4 mixture under nano-confinement," Energy, Elsevier, vol. 273(C).
    20. Xu, WenLong & Yu, Hao & Micheal, Marembo & Huang, HanWei & Liu, He & Wu, HengAn, 2023. "An integrated model for fracture propagation and production performance of thermal enhanced shale gas recovery," Energy, Elsevier, vol. 263(PA).

    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:jsusta:v:16:y:2024:i:23:p:10645-:d:1536773. 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.