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

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  • 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
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    References listed on IDEAS

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    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. 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. 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.
    4. 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.
    5. 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.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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).

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