IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v266y2023ics0360544222033989.html
   My bibliography  Save this article

An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity

Author

Listed:
  • Hui, Gang
  • Chen, Zhangxin
  • Wang, Youjing
  • Zhang, Dongmei
  • Gu, Fei

Abstract

The controlling factors of unconventional shale productivity by comprehensive analysis of mineralogy, petrophysics, geochemistry, and geomechanics have not been well understood. The comprehensive datasets from 1182 core samples of key wells from the Duvernay shale at Crooked Lake, Alberta, are gathered to evaluate the fundamental parameters controlling unconventional shale gas production. By integrating reservoir parameters and shale productivity, a machine learning-based approach is used to identify the fundamental elements that affect shale productivity. Four machine learning approaches are evaluated, where Extra Trees has led to the highest coefficient of determination R2 of 0.817. Factors that mostly contribute to shale productivity are found to be the production index, formation pressure, effective porosity, total organic carbon, gas saturation, and shale thickness. Case studies demonstrate that the average accordance rate between the predicted and actual production of three new wells reaches 92.3%, thereby shedding light on the site selection of hydraulic fracturing wells for the efficient development of unconventional resources.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033989
    DOI: 10.1016/j.energy.2022.126512
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222033989
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.126512?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. Sui, Lili & Ju, Yang & Yang, Yongming & Yang, Yong & Li, Aishan, 2016. "A quantification method for shale fracability based on analytic hierarchy process," Energy, Elsevier, vol. 115(P1), pages 637-645.
    2. McGlade, Christophe & Speirs, Jamie & Sorrell, Steve, 2013. "Methods of estimating shale gas resources – Comparison, evaluation and implications," Energy, Elsevier, vol. 59(C), pages 116-125.
    3. 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.
    4. 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).
    5. Ciupăgeanu, Dana-Alexandra & Lăzăroiu, Gheorghe & Barelli, Linda, 2019. "Wind energy integration: Variability analysis and power system impact assessment," Energy, Elsevier, vol. 185(C), pages 1183-1196.
    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. Shakouri, Sina & Mohammadzadeh-Shirazi, Maysam, 2023. "Modeling of asphaltic sludge formation during acidizing process of oil well reservoir using machine learning methods," Energy, Elsevier, vol. 285(C).
    2. 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).
    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. 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).
    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. 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).
    7. Priya Bijalwan & Ashulekha Gupta & Anubhav Mendiratta & Amar Johri & Mohammad Asif, 2024. "Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms," Economies, MDPI, vol. 12(1), pages 1-19, January.
    8. 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).
    9. Hui, Gang & Chen, Zhangxin & Schultz, Ryan & Chen, Shengnan & Song, Zhaojie & Zhang, Zhaochen & Song, Yilei & Wang, Hai & Wang, Muming & Gu, Fei, 2023. "Intricate unconventional fracture networks provide fluid diffusion pathways to reactivate pre-existing faults in unconventional reservoirs," Energy, Elsevier, vol. 282(C).
    10. 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.

    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. 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).
    2. 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.
    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. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    5. Flavio R. Arroyo M. & Luis J. Miguel, 2019. "The Trends of the Energy Intensity and CO 2 Emissions Related to Final Energy Consumption in Ecuador: Scenarios of National and Worldwide Strategies," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    6. 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.
    7. Tunstall, Thomas, 2015. "Iterative Bass Model forecasts for unconventional oil production in the Eagle Ford Shale," Energy, Elsevier, vol. 93(P1), pages 580-588.
    8. Montgomery, J.B. & O’Sullivan, F.M., 2017. "Spatial variability of tight oil well productivity and the impact of technology," Applied Energy, Elsevier, vol. 195(C), pages 344-355.
    9. Jiang, Xingwen & Chen, Mian & Li, Qinghui & Liang, Lihao & Zhong, Zhen & Yu, Bo & Wen, Hang, 2022. "Study on the feasibility of the heat treatment after shale gas reservoir hydration fracturing," Energy, Elsevier, vol. 254(PB).
    10. Kuchler, Magdalena & Höök, Mikael, 2020. "Fractured visions: Anticipating (un)conventional natural gas in Poland," Resources Policy, Elsevier, vol. 68(C).
    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. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    13. 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).
    14. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    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. 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.
    17. 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.
    18. Vélez-Henao, Johan-Andrés & García-Mazo, Claudia-Maria & Freire-González, Jaume & Vivanco, David Font, 2020. "Environmental rebound effect of energy efficiency improvements in Colombian households," Energy Policy, Elsevier, vol. 145(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. Tzen-Ying Ling & Wei-Kai Hung & Chun-Tsu Lin & Michael Lu, 2020. "Dealing with Green Gentrification and Vertical Green-Related Urban Well-Being: A Contextual-Based Design Framework," Sustainability, MDPI, vol. 12(23), pages 1-24, November.

    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:266:y:2023:i:c:s0360544222033989. 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.