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Gas Well Optimization With The Production Model And Algorithm Research

Posted on:2006-12-27Degree:MasterType:Thesis
Country:ChinaCandidate:X F DingFull Text:PDF
GTID:2191360182456042Subject:Applied Mathematics
Abstract/Summary:PDF Full Text Request
Through the investigation and the analysis to the domestic and international oil-gas well optimization allocation method, this paper has built several kinetic models of gas well. These kinetic models which are built on the same hydrodynamic system describe the whole information between pressure drop and flow rate in the gas well. One of these kinetic models is the geologic strata pressure with productive time variety, which will change the traditional static optimization allocation. We will build the gas well optimization allocation models which include simple objective and multiple objectives, single objective model, such as the biggest and economic day production model of the gas well, the longest steady productive time model of the gas well, the optimal size model of gas cock in well head, the optimal pipeline size model of well tube, the biggest recovery ratio model and the reasonable gas mining speed model; multiple objective models, such as the biggest yield of day and the longest steady productive time and the biggest total amount of production. And the same time, we have built some models for multi-well system.The paper selects two methods to solve the difference models, because the models are non-linear. One method is generalized reduced gradient algorithm, which could solve the models of pure dry gas well and abnormal high pressure gas well. Another method is modified genetic algorithm, which could solve simple objective models and multiple objectives models of containing water gas well, gas-condensate well and multi-gas well system. At the end of the paper, we give the example analysis in which the practical application of the models and the solving methods is proved.
Keywords/Search Tags:gas well, optimization allocation, model, generalized reduced gradient algorithm, genetic algorithm
PDF Full Text Request
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