Pipe Diameter Optimization and Two-Phase Flow Pressure Drop in Seabed Pipelines: A Genetic Algorithm Approach

Author:

Khamehchi Ehsan,Dargi Matin,Imeri Matin,Mahdavi Kalatehno Javad,Khaleghi Mohammad Reza

Abstract

Industries such as oil, gas, and petrochemicals encounter a multifaceted challenge when it comes to the transportation of diverse substances through pipelines. The process relies heavily on two-phase flow, particularly in the case of seabed pipelines responsible for conveying oil and natural gas. In our study, the intricate dynamics of pressure fluctuations in horizontal seabed pipelines were comprehensively investigated through the use of simulation software. Nevertheless, the software employed had its limitations as it failed to account for economic considerations. To address this limitation, the integration of a genetic algorithm was undertaken, which encompassed constraints related to the initial investment, thereby contributing to the enhancement of cost-effectiveness in pressure drop calculations. Additionally, a novel parameter was introduced into the Baker model, leading to improvements in both technical efficiency and economic viability. It was observed that augmenting the weight of the initial investment constraint resulted in a reduction in the optimal pipeline diameter, following a third-degree curve. Furthermore, the research findings indicated that a 4% increase in water shear led to a 14.76% decrease in frictional pressure drop within vertical pipes and a 3.5% reduction in horizontal pipes. Conversely, as the gas-to-oil ratio was increased, frictional pressure drop surged by 116.87% in vertical pipes and 81.69% in horizontal pipes. This valuable information can be harnessed to optimize pipeline design and operations in the demanding underwater environments commonly encountered in these industries.

Publisher

Interciencia

Subject

Multidisciplinary

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3