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Performance evaluation of the Habanero enhanced geothermal system, Australia: Optimization based on tracer and induced micro-seismicity data

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  • Xu, Tianfu
  • Liang, Xu
  • Xia, Yi
  • Jiang, Zhenjiao
  • Gherardi, Fabrizio

Abstract

Understanding of hydraulic heterogeneity of the reservoir is the basis for Enhanced geothermal systems (EGS) optimization. This, however, is challenging, as there are limited well tests available for reservoir characterization. To overcome this challenge, this study developed a methodology for determining hydraulic parameters by integrating the induced micro-seismic data collected during the hydraulic stimulation, and tracer test data in the subsequent trial production period for the first time. The spatio-temporal distribution of induced micro-seismicities is indicative of the hydraulic diffusivity distribution, and is subsequently converted into the heterogeneous distribution of permeability and porosity, by quantitative calibrating models outputs with tracer test observations. This approach was verified and applied to the Habanero EGS, Australia, where the accuracy in calibrating the tracer test responses was improved by over 50%, attributed to the constraints of micro-seismic data. The well placement was then optimized based on new insights of hydraulic parameters in the reservoir. As a result, the electrical power efficiency was increased by 5.59 times in 30 years. The wide existence of tracer and induced micro-seismic data promotes the generality of this methodology to improve the reservoir characterization and well placement optimization for the sustainable development of EGS.

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  • Xu, Tianfu & Liang, Xu & Xia, Yi & Jiang, Zhenjiao & Gherardi, Fabrizio, 2022. "Performance evaluation of the Habanero enhanced geothermal system, Australia: Optimization based on tracer and induced micro-seismicity data," Renewable Energy, Elsevier, vol. 181(C), pages 1197-1208.
  • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:1197-1208
    DOI: 10.1016/j.renene.2021.09.111
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    References listed on IDEAS

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    Cited by:

    1. Chen, Cihai & Deng, Yaping & Ma, Haichun & Kang, Xueyuan & Ma, Lei & Qian, Jiazhong, 2024. "Deep learning-based inversion framework by assimilating hydrogeological and geophysical data for an enhanced geothermal system characterization and thermal performance prediction," Energy, Elsevier, vol. 302(C).
    2. Wang, Ling & Jiang, Zhenjiao & Li, Chengying, 2023. "Comparative study on effects of macroscopic and microscopic fracture structures on the performance of enhanced geothermal systems," Energy, Elsevier, vol. 274(C).
    3. Liang, Xu & Xu, Tianfu & Chen, Jingyi & Jiang, Zhenjiao, 2023. "A deep-learning based model for fracture network characterization constrained by induced micro-seismicity and tracer test data in enhanced geothermal system," Renewable Energy, Elsevier, vol. 216(C).
    4. Feng, Guanhong & Xu, Tianfu & Zhao, Yue'an & Gherardi, Fabrizio, 2022. "Heat mining from super-hot horizons of the Larderello geothermal field, Italy," Renewable Energy, Elsevier, vol. 197(C), pages 371-383.

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