高含硫天然氣集輸管道硫沉積預(yù)測(cè)方法研究
[Abstract]:With the increasing demand for energy in the world, the development of high-sulfur gas fields will help to alleviate the energy shortage and play a more and more important role in the natural gas industry. The elemental sulfur dissolved in the gas may precipitate and deposit in the form of solid particles in the gathering and transportation pipeline with the change of pressure and temperature. Sulfur deposition will cause "sulfur plugging" in the surface gathering and transportation pipeline, resulting in corrosion of steel and ultimately affecting the normal transportation of gas. Therefore, it is very important to study the sulfur deposition in gas gathering and transportation pipelines for ensuring the safety and efficient transportation of high sulfur-bearing natural gas.
(1) Studying the mechanism of elemental sulfur deposition in gathering and transportation pipelines is helpful to understand the nature of sulfur deposition, and it is also the basis and prerequisite for establishing a prediction model of sulfur deposition in gathering and transportation pipelines. The DPR model combined with WA correction method is the best method for calculating the compressibility factor of high sulfur natural gas, and the BWRS equation of state has higher accuracy in calculating the compressibility factor; Dempsey model combined with Standing correction method is the best method for calculating high sulfur natural gas. On this basis, it is clear that hydrogen sulfide is the material basis for the source of elemental sulfur. According to the principle of chemical reaction equilibrium, the dissolution and deposition of elemental sulfur are mainly physical dissolution and deposition. The main factors of degree.
(2) The applicability and limitation of the existing typical methods for predicting the solubility of sulfur in high sulfur gas are analyzed and evaluated. On this basis, a genetic algorithm combined with BP neural network is proposed to predict the solubility of sulfur in high sulfur gas. The results show that the prediction accuracy of genetic BP neural network is high.
(3) According to the characteristics of gas-solid migration and the theory of gas-solid two-phase flow in horizontal pipeline, the force of solid sulfur particles precipitated in pipeline is analyzed, and the condition of elemental sulfur particles deposited in pipeline is analyzed by using the critical velocity calculation model of solid particles. The precipitation is studied by using RSM model and DPM model of FLUENT software. The deposition rate of sulfur particles in straight pipe section decreases with the increase of gas flow velocity and increases with the increase of particle diameter. The deposition rate of sulfur particles in horizontal curved pipe increases with gas flow velocity, particle diameter and bending ratio. The deposition rate of sulfur particles increases with the increase of flow velocity and particle diameter, and decreases with the increase of valve opening.
(4) Based on the research results of (1) ~ (3), combined with the pressure and temperature distribution prediction model of high sulfur gas gathering pipeline, the prediction models of sulfur precipitation location, sulfur deposition condition determination and sulfur deposition calculation of high sulfur gas gathering pipeline are established, and these models are used to solve the sulfur deposition problem of a high sulfur gas gathering pipeline in China. The results show that the prediction results are in good agreement with the actual situation.
【學(xué)位授予單位】:西南石油大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2015
【分類號(hào)】:TE86
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