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  • 标题:State‐of‐the‐art brief review on sanding problem of offshore natural gas hydrates sediments
  • 本地全文:下载
  • 作者:Jinze Song ; Jianhuang Fu ; Youming Xiong
  • 期刊名称:Energy Science & Engineering
  • 电子版ISSN:2050-0505
  • 出版年度:2022
  • 卷号:10
  • 期号:1
  • 页码:253-273
  • DOI:10.1002/ese3.1006
  • 语种:English
  • 出版社:John Wiley & Sons, Ltd.
  • 摘要:Abstract As the natural gas hydrate (NGH) resources show a bright future, more and more commercial and technical focuses have been devoted to this area. The low productivity, the sanding problem, and the poor economic performance are vital problems hindering the long‐term commercial exploration and development of NGS sediments. Among all these problems, the sanding problem can aggravate the low productivity and the poor economic efficiency problems. Therefore, it is necessary to investigate and tackle the sanding problem for the safe, long‐term, and large‐scale commercial development of offshore NGH resources. The sanding problem of the NGH sediment is highly related to the mechanical behavior of sediment. The main influencing factors are discussed, which are hydrate saturation, effective confining pressure, sand content, hydrate distribution, and multiple‐physical fields. This article summarizes the current research achievements systematically to determine how these factors affect sand production in NGH sediments. Besides the macrolevel about mechanical behaviors of NGH sediment, the sand motion modeling is also included in this review. The source of sand production is the free‐moving sand which comes from formation deformation. The review compares most commonly used yield criteria and then recommends a proper one for NGH sediments. The paper subsequently discusses the common investigation methods for the sanding problem in this area, including numerical simulation and experiments. The design and effect of sand control techniques have also been reviewed and discussed. According to the review results, the paper concludes the current research drawbacks and generates suggestions for future research. The novel methods of sand control investigation are machine learning and optimization methods.
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