特石管线超稠油管道暖管技术研究
详细信息    查看全文 | 推荐本文 |
  • 英文篇名:Research on Pipeline Heating Technology of Teshi Super Heavy Oil Pipeline
  • 作者:赵明 ; 王红 ; 刘伟 ; 王宇 ; 罗超 ; 李文文 ; 蒋旭
  • 英文作者:ZHAO Ming;WANG Hong;LIU Wei;WANG Yu;LUO Chao;LI Wen-wen;JIANG Xu;Liaohe Oilfield Economic and Trade Real Estate Property Company;
  • 关键词:超稠油 ; 暖管 ; 基本粒子群算法
  • 英文关键词:super heavy oil;;warm pipe;;basic particle swarm optimization algorithm
  • 中文刊名:GDGS
  • 英文刊名:Pipeline Technique and Equipment
  • 机构:辽河油田经济贸易置业总公司;
  • 出版日期:2019-03-15
  • 出版单位:管道技术与设备
  • 年:2019
  • 期:No.156
  • 基金:辽河油田公司科技计划项目(LHSY-ZYGS-2017-JS-8867)
  • 语种:中文;
  • 页:GDGS201902003
  • 页数:4
  • CN:02
  • ISSN:21-1312/TH
  • 分类号:13-16
摘要
辽河油田特石管线输送的原油为超稠油,超稠油的输送需要高温加热,一旦发生泄漏停输会造成凝管。凝管后的再启动需要对管道进行预先暖管处理。为了降低暖管成本,研究了特石管线的暖管机理,提出了4种有效的暖管方案,并用基本粒子群算法对暖管方案进行了优化计算,得出热油反向解堵暖管方案费用最低的结论。
        The crude oil transported by Liaohe Oilfield Teshi pipeline is super heavy oil. The transportation of super heavy oil needs high temperature heating. Once leakage occurs, the clotting pipe can be stopped. The restarting of the condensing pipe requires the preheating of the pipe. To reduce the cost of heating pipes, the warm pipe mechanism of Teshi pipelines was researched, and four effective schemes of pipe heating were proposed. The basic particle swarm optimization algorithm was used to optimize the warm pipe project. The conclusion was drawn that the hot pipe project with reverse hot plugging consumes the least amount of money.
引文
[1] 王凤军,李文彩,王丽华,等.泡沫保温输油管道投产暖管时间计算[J].油气田地面工程,2007,26(10):22-23.
    [2] 张玮康,王为民,闫陵江,等.特石管线超稠油黏温特性实验分析[J].管道技术与设备,2010(6):14-15.
    [3] 陈晓霞.坨—鞍线稠油管道的暖管技术[J].石油工程建设,2009,35(1):49-51.
    [4] 张金亮,王为民,申龙涉,等.辽河油田超稠油流变特性的试验研究[J].油气田地面工程,2006,25(7):11.
    [5] 宋江峰.高稠油管道站内改造工程施工要点分析[J].中国科技信息,2011(15):46.
    [6] LI B B,WANG L,LIU B.An effective PSO-Based hybrid algorithm for multiobjective permutation flow shop scheduling[J].IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2008,38(4):818-831.
    [7] 夏惠,杨秀,杨帆,等.结合PSO与序列运算理论的微电网的优化配置[J].电网与清洁能源,2017,33(4):40-47.
    [8] CHENG Z L,FAN L,ZHANG Y L.Multi-agent decision support system for missile defense based on improved PSO algorithm[J].系统工程与电子技术:英文版,2017,28(3):514-525.
    [9] 李炳宇,萧蕴诗,汪镭.PSO算法在工程优化问题中的应用[J].计算机工程与应用,2004,40(18):74-76.
    [10] REYES-SIERRA M,COELLO C A C.Improving PSO-based multi-objective optimization using crowding,mutation and 6-dominance[J].Lecture Notes in Computer Science,2005,3410:505-519.