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Integration analysis of 3D fractures network reconstruction and frac hits response in shale wells

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  • Hou, Bing
  • Zhang, Qixing
  • Liu, Xing
  • Pang, Huiwen
  • Zeng, Yue

Abstract

The frac hits affected a drilling platform in the Changning region, Sichuan Basin, China. The microseismic events of the three wells coincided in the adjacent fracturing stages. When the intermediate well fracturing, the production pressure of two adjacent wells decreases, and gas lift occurs. This study first reconstructed the 3D discrete complex fracture network through microseismic events using the local RANSAC algorithm and quantitatively evaluated the interaction and connectivity of hydraulic fracture networks between wells using kernel density estimation. An integrated multi-physical fields model was established under the dual-porosity/dual permeability concept and embedded discrete fracture models, including poroelastic deformation, desorption/adsorption, Knudsen diffusion, and viscous flow. The finding showed that frac hits during the fracturing operations could change the occurrence frequency, moment magnitude, and spatial distribution of microseismic events. The maximum dip direction of hydraulic fracture changed by 53° because of the pore pressure connection. The stress deflexion induced by the old well exploitation inward deflected between 13° and 45°, which means that the hydraulic fractures preferentially would grow asymmetrically and propagate to the pressure depletion zone. This research integrates geology and engineering and can give direct and practical guidance to oilfield fracturing construction, hydraulic fracture monitoring, and gas production.

Suggested Citation

  • Hou, Bing & Zhang, Qixing & Liu, Xing & Pang, Huiwen & Zeng, Yue, 2022. "Integration analysis of 3D fractures network reconstruction and frac hits response in shale wells," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018084
    DOI: 10.1016/j.energy.2022.124906
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    References listed on IDEAS

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    1. He, Jianming & Li, Xiao & Yin, Chao & Zhang, Yixiang & Lin, Chong, 2020. "Propagation and characterization of the micro cracks induced by hydraulic fracturing in shale," Energy, Elsevier, vol. 191(C).
    2. Zheng, Peng & Xia, Yucheng & Yao, Tingwei & Jiang, Xu & Xiao, Peiyao & He, Zexuan & Zhou, Desheng, 2022. "Formation mechanisms of hydraulic fracture network based on fracture interaction," Energy, Elsevier, vol. 243(C).
    3. Tian, Zhenhua & Wei, Wei & Zhou, Shangwen & Sun, Chenhao & Rezaee, Reza & Cai, Jianchao, 2022. "Impacts of gas properties and transport mechanisms on the permeability of shale at pore and core scale," Energy, Elsevier, vol. 244(PA).
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    Cited by:

    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. 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).
    3. Chen, Guodong & Luo, Xin & Jiao, Jiu Jimmy & Jiang, Chuanyin, 2023. "Fracture network characterization with deep generative model based stochastic inversion," Energy, Elsevier, vol. 273(C).

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