Optimization model for pressurization position of shale gas surface gathering pipeline network
LI Ze-long
HUANG Si-yu
LUO Yuan-yuan
DUAN Ji-miao
ZHOU Jun
Abstract:Combination pressurization of shale gas gathering systems is currently the most popular pressurization method.To optimize the pressurization position of shale gas gathering pipeline network,a mixed integer nonlinear pro-gramming model(MINLP)is developed based on the existing shale gas field pressurization model with the minimum total cost of compressor as the objective function and constraints including pipeline pressure and flow constraints,compressor constraints,throttling constraints,pressure balance constraints,gas well pressurization and platform pressurization unique-ness constraints,and gas well flow constraint.Taking a shale gas block as an example,the general algebraic modeling system(GAMS)is used to solve the optimization model to obtain the corresponding combined pressurization scheme.And the following conclusions were reached.Firstly,the optimization model can be programmed to solve for a preferred com-bination of pressurization schemes among existing pressurization methods based on the actual production of the shale gas blocks.Secondly,when gas well pressurization and platform pressurization are considered simultaneously,the optimized pressurization location is more concentrated and the number of compressors is smaller,reflecting the advantages of the combination pressurization method in the shale gas gathering systems pressurization.Thirdly,different combinations of pressurization methods have approximately the same total compressor operating power,and the operating power at the site is always greater than other pressurization positions.Fourth,when the compressor inlet and outlet pressure difference is bigger and the processing capacity is larger,the rated power of the configured compressor also increases,the purchase cost is higher and the total cost of the unit is also higher.
Keywords:shale gasgathering systemscombination pressurizationoptimizationMINLP
Publication Date:2025-04-30
Online Publishing Date:2025-08-15(First online date of this platform, not the publication date of the document)
Pages:9( 767-775 )
