IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1639-d1060058.html
   My bibliography  Save this article

An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression

Author

Listed:
  • Gang Hui

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum Beijing, Beijing 102249, China
    Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

  • Fei Gu

    (Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 721002, China)

  • Junqi Gan

    (Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 721002, China)

  • Erfan Saber

    (School of Mechanical and Mining Engineering, The University of Queensland, Saint Lucia, QLD 4072, Australia)

  • Li Liu

    (Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 721002, China)

Abstract

In the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in the absence of a full, multi-data study of reservoir characterization. To forecast Duvernay shale gas production in the vicinity of Fox Creek, Alberta, the multi-scale experimental findings are thoroughly evaluated. The relationship between shale gas production and reservoir parameters is assessed using multiple linear regression (MLR). Three hundred and five core samples from fifteen wells were later examined using the MLR technique to discover the fundamental controlling characteristics of shale potential. Quartz, clay, and calcite were found to comprise the bulk of the Duvernay shale. The average values for the effective porosity and permeability were 3.96% and 137.2 nD, respectively, whereas the average amount of total organic carbon (TOC) was 3.86%. The examined Duvernay shale was predominantly deposited in a gas-generating timeframe. As input parameters, the MLR method calculated the components governing shale productivity, including the production index (PI), gas saturation (S g ), clay content (V cl ), effective porosity (F), total organic carbon (TOC), brittleness index (BI), and brittle mineral content (BMC) (BMC). Shale gas output was accurately predicted using the MLR-based prediction model. This research may be extended to other shale reservoirs to aid in the selection of optimal well sites, resulting in the effective development of shale resources.

Suggested Citation

  • Gang Hui & Fei Gu & Junqi Gan & Erfan Saber & Li Liu, 2023. "An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression," Energies, MDPI, vol. 16(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1639-:d:1060058
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1639/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1639/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ren Jiang & Zhifeng Ji & Wuling Mo & Suhua Wang & Mingjun Zhang & Wei Yin & Zhen Wang & Yaping Lin & Xueke Wang & Umar Ashraf, 2022. "A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir," Energies, MDPI, vol. 15(19), pages 1-20, September.
    2. Song, Xianzhi & Zhang, Chengkai & Shi, Yu & Li, Gensheng, 2019. "Production performance of oil shale in-situ conversion with multilateral wells," Energy, Elsevier, vol. 189(C).
    3. Hui, Gang & Chen, Zhangxin & Wang, Youjing & Zhang, Dongmei & Gu, Fei, 2023. "An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity," Energy, Elsevier, vol. 266(C).
    4. 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.
    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. Beichen Zhao & Binshan Ju & Chaoxiang Wang, 2023. "Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.

    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. Wensong Huang & Ping Wang & Gang Hui & Xiangwen Kong & Yuepeng Jia & Lei Huang & Yufei Bai & Zhiyang Pi & Ye Li & Fuyu Yao & Penghu Bao & Yujie Zhang, 2024. "Unconventional Fracture Networks Simulation and Shale Gas Production Prediction by Integration of Petrophysics, Geomechanics and Fracture Characterization," Energies, MDPI, vol. 17(20), pages 1-16, October.
    2. Hui, Gang & Chen, Zhangxin & Wang, Youjing & Zhang, Dongmei & Gu, Fei, 2023. "An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity," Energy, Elsevier, vol. 266(C).
    3. Xingzhi Liu & Songhang Zhang & Yongkang Xie & Tao Wang, 2024. "Three-Dimensional Heterogeneity of the Pore and Fracture Development and Acoustic Emission Response Characteristics of Coal Rocks in the Yunnan Laochang Block," Energies, MDPI, vol. 17(5), pages 1-19, March.
    4. 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.
    5. Yuxuan Yang & Zhigang Wen & Weichao Tian & Yunpeng Fan & Heting Gao, 2024. "A New Model for Predicting Permeability of Chang 7 Tight Sandstone Based on Fractal Characteristics from High-Pressure Mercury Injection," Energies, MDPI, vol. 17(4), pages 1-16, February.
    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. 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.
    10. 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.
    11. 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).
    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. 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).
    14. 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.
    15. 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.
    16. Zou, Xiaojing & He, Changyu & Guan, Wei & Zhou, Yan & Zhao, Hongyang & Cai, Mingyu, 2023. "Reservoir tortuosity prediction: Coupling stochastic generation of porous media and machine learning," Energy, Elsevier, vol. 285(C).
    17. 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).
    18. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    19. Wang, Guoying & Liu, Shaowei & Yang, Dong & Fu, Mengxiong, 2022. "Numerical study on the in-situ pyrolysis process of steeply dipping oil shale deposits by injecting superheated water steam: A case study on Jimsar oil shale in Xinjiang, China," Energy, Elsevier, vol. 239(PC).
    20. Shi, Yu & Zhang, Yulong & Song, Xianzhi & Cui, Qiliang & Lei, Zhihong & Song, Guofeng, 2023. "Injection energy utilization efficiency and production performance of oil shale in-situ exploitation," Energy, Elsevier, vol. 263(PB).

    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:4:p:1639-:d:1060058. 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.