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Evaluation and comparison of gas production potential of the typical four gas hydrate deposits in Shenhu area, South China sea

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  • Huang, Li
  • Yin, Zhenyuan
  • Wan, Yizhao
  • Sun, Jianye
  • Wu, Nengyou
  • Veluswamy, Hari Prakash

Abstract

The field production test of gas hydrate conducted in South China Sea has been proven to be successful, but the production still cannot satisfy the requirement for commercial production. In order to achieve a higher production efficiency, the numerical simulation code Tough + Hydrate was employed to estimate the detailed production potential of four typical coring sites XX01∼XX04 in Shenhu area, which have distinct reservoir conditions and are considered as the focus for production tests. Our simulation results show that the hydrate deposit at Site XX03 featured with the highest permeability has the most promising gas production, despite the least thickness 11.56 m of the hydrate-bearing layer. In addition, we have also investigated the impact of the controllable engineering facts on the production, which suggests that the lower depressurization pressure and longer perforated interval would contribute to a higher gas production. And when the lower water production is considered, the bottom half perforated interval would be a relatively more promising design for the production. This research gives a forward sight for production prediction and our findings may provide a theoretical guideline in selection and design of the production targets.

Suggested Citation

  • Huang, Li & Yin, Zhenyuan & Wan, Yizhao & Sun, Jianye & Wu, Nengyou & Veluswamy, Hari Prakash, 2020. "Evaluation and comparison of gas production potential of the typical four gas hydrate deposits in Shenhu area, South China sea," Energy, Elsevier, vol. 204(C).
  • Handle: RePEc:eee:energy:v:204:y:2020:i:c:s0360544220310628
    DOI: 10.1016/j.energy.2020.117955
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    11. Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(C).
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