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

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  • 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
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    References listed on IDEAS

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    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.
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    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.
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    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.

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