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Petroleum Science > DOI: https://doi.org/10.1016/j.petsci.2025.03.026
Experimental and modeling investigation of zero net liquid flow in hilly terrain pipeline Open Access
文章信息
作者:Bo Huang, Qiang Xu, Ying-Jie Chang, Ye-Qi Cao, Hai-Yang Yu, Yu-Wen Li, Lie-Jin Guo
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引用方式:Bo Huang, Qiang Xu, Ying-Jie Chang, Ye-Qi Cao, Hai-Yang Yu, Yu-Wen Li, Lie-Jin Guo, Experimental and modeling investigation of zero net liquid flow in hilly terrain pipeline, Petroleum Science, 2025, https://doi.org/10.1016/j.petsci.2025.03.026.
文章摘要
Abstract: Hilly terrain pipeline is a common form of pipeline in oil and gas storage and transportation industry. Due to the hilly terrain influence, the liquid at the elbow of the gathering pipeline is easy to flow back and accumulate to form slug flow, so it is necessary to remove the accumulated liquid by gas purging. In this paper, experiment is carried out in hilly terrain pipelines. Three flow patterns of stratified flow, slug flow and stratified entrained flow are observed. The process of gas purging accumulated liquid is divided into four stages, namely liquid accumulation, liquid rising; continuous outflow and tail outflow. At the same time, the flow pattern maps of each stage are drawn. The pressure drop signal is analyzed in time domain and frequency domain, and the contour map of pressure drop distribution is drawn. It is found that the ratio of range to average value can well distinguish the occurrence range of each flow pattern. Based on visualization, the transition process of slug flow to stratified flow and stratified entrained flow is studied, and the transition boundary prediction model is established. An image processing method is proposed to convert the image signal into a similarity curve, and PSD analysis is performed to calculate the slug frequency. The normal distribution is used to fit the slug frequency, and the predicted correlation is in good agreement with the experimental data.
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Keywords: Hilly terrain pipeline; Zero net liquid flow; Slug flow; Flow pattern transition; Quantitative image processing