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DC electric field assisted heat extraction evaluation via water circulation in abandoned production well patterns: Semi-analytical and numerical models

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  • Wang, Zhipeng
  • Ning, Zhengfu
  • Guo, Wenting
  • Zhan, Jie
  • Chen, Zhangxin

Abstract

Geothermal energy, recognized for its clean and vast developmental potential, finds untapped promise in many oil fields developed through water injection. However, challenges such as the high costs associated with drilling and development, the expansion of water-clay expansion, and low rock permeability have constrained the broader exploitation of geothermal resources. Here, to address these challenges and harness low-permeability geothermal energy from abandoned wells, this work introduces a novel approach: DC-assisted circulation water injection technology. This study employs the Green function, Newman product, and boundary element methods to develop semi-analytical and numerical models, incorporating a DC electric field to simulate the optimization effect of this field on heat extraction. This work evaluates and optimize the lifetime and economic effects of abandoned wells using the Non Sorting Genetic Algorithm II. Results show that the semi-analytical model monitors the reservoir temperature parameter to optimize the timing for circulation water injection. Additionally, our numerical model demonstrates that, over a span of 5000 days, a network of wells could generate a revenue of $9.158 million. In summary, DC-assisted abandoned wells to develop geothermal reservoirs is not only feasible but also enhances reservoir permeability and porosity through an electrical conductivity effect, significantly improving heat energy recovery.

Suggested Citation

  • Wang, Zhipeng & Ning, Zhengfu & Guo, Wenting & Zhan, Jie & Chen, Zhangxin, 2024. "DC electric field assisted heat extraction evaluation via water circulation in abandoned production well patterns: Semi-analytical and numerical models," Renewable Energy, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:renene:v:228:y:2024:i:c:s0960148124007316
    DOI: 10.1016/j.renene.2024.120663
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