许继凯, 刘刚, 陈雷, 徐睿妤. 基于蚁群算法的油气集输管网井组优化[J]. 油气储运, 2016, 35(11): 1230-1234. DOI: 10.6047/j.issn.1000-8241.2016.11.018
引用本文: 许继凯, 刘刚, 陈雷, 徐睿妤. 基于蚁群算法的油气集输管网井组优化[J]. 油气储运, 2016, 35(11): 1230-1234. DOI: 10.6047/j.issn.1000-8241.2016.11.018
XU Jikai, LIU Gang, CHEN Lei, XU Ruiyu. Well group optimization of oil and gas gathering pipeline network based on ant colony algorithm[J]. Oil & Gas Storage and Transportation, 2016, 35(11): 1230-1234. DOI: 10.6047/j.issn.1000-8241.2016.11.018
Citation: XU Jikai, LIU Gang, CHEN Lei, XU Ruiyu. Well group optimization of oil and gas gathering pipeline network based on ant colony algorithm[J]. Oil & Gas Storage and Transportation, 2016, 35(11): 1230-1234. DOI: 10.6047/j.issn.1000-8241.2016.11.018

基于蚁群算法的油气集输管网井组优化

Well group optimization of oil and gas gathering pipeline network based on ant colony algorithm

  • 摘要: 为了合理确定油气集输管网结构布局,以井站间产量距离和为目标函数,以井站连接关系和计量站站址为优化变量,建立了井组优化模型。采用小窗口蚁群算法求解,将井站连接关系转化为路径选择,根据不同管段对应的产量、距离计算启发因子,以不同路径方案下的产量距离总和作为信息积累的评价指标,通过控制蚂蚁状态转移过程确保井式、集输半径、计量站处理量等约束条件,有效避免了不可行解的产生。实例计算结果表明:基于蚁群算法的优化结果在管网的产量距离和与管道总长度方面,相比已有的遗传算法优化结果均有所改进,有望为今后的油气集输管网优化设计提供技术支持。

     

    Abstract: In order to determine the structural layout of oil and gas gathering pipeline network reasonably, well group optimization model was established with the well-to-station yield distance sum as the objective function, and the well-to-station connection relation and the location of metering station as the optimization variables. And then it was solved by the little-window ant colony algorithm. The well-to-station connection relation was converted into the routing selection. Heuristic factor was calculated using the yield and distance of each pipe section, the yield distance sum corresponding to various routing schemes is taken as the evaluation index of information accumulation. Constraint conditions (e.g. well pattern, gathering radius, treatment capacity of metering station) are satisfied by controlling the state transition process of ants so as to avoid the infeasible solution. The example calculation result shows that the optimization result derived from ant colony algorithm is better than that from the existing genetic algorithm in terms of yield distance of pipeline network and total length of pipeline. It can provide technical support for the optimization design of oil and gas gathering pipeline networks in the future.

     

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