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Global optimization of topological structure for radial pattern gathering pipe network
LIU Gang1, XU Jikai2, GUO Zhigang3, CHEN Lei1, LU Xingguo1, TENG Houxing1, XU Ruiyu1
(1.College of Pipeline and Civil Engineering in China University of Petroleum, Qingdao 266580, China;2.Shandong Province Gas Pipeline Company Limited, Jinan 250101, China;3.Oil Gas Gathering and Transportation General Factory of Shengli Oilfield Branch, SINOPEC, Dongying 257000, China)
Abstract:
The topological structure optimization model of radial patter oil and gas gathering pipe network was built according to its structural characteristics, with the total construction cost of the pipe network as the objective function, and the connection relation of nodes, pipeline parameters and location of stations as the optimization variables. To avoid the deficiency of multilevel optimization, ant colony algorithm and genetic algorithm were combined to solve the optimization model globally. In ant colony algorithm, the determination of connection relation was converted to the routing problem, heuristic factor was expressed as the function of pipe construction cost, and the total construction cost of pipe network corresponding to the routing scheme was used to calculate the pheromone accumulation. In genetic algorithm, the information of station location was stored in chromosomes using gray code, and the well-group scheme and pipe diameters were obtained by ant colony algorithm and were used to calculate the fitness of each chromosome. Meanwhile, the optimal station location, optimal well-group and pipeline parameters were also obtained. The above algorithm was applied to the optimum calculation of the specific gathering pipeline networks in some oil fields. The results show that the global optimization algorithm has better optimum quality and stronger robustness than multilevel optimization, and the optimum results are not affected by initial value.
Key words:  gathering pipe network  topological structure  multilevel optimization  global optimization  ant colony algorithm  genetic algorithm