高含硫天然气集输管道硫沉积预测方法研究

发布时间：2018-08-24 09:19

【摘要】：随着世界各国对能源需求的不断增长,开发高含硫气田有助于缓解能源紧张的局面,其在整个天然气工业中的地位也将越来越突出。与常规天然气相比,高含硫天然气不仅具有极强的腐蚀性和剧毒性,还具有特殊的PVT性质以及独特的相态变化特征。高含硫天然气在集输过程中,随着压力、温度等条件的变化,溶解在气体中的元素硫可能会在集输管道中以固体颗粒形态析出并发生沉积。硫沉积会造成地面集输管道出现“硫堵”,引起钢材的腐蚀,最终影响气体的正常输送。因此,研究集输管道中的硫沉积对于保障高含硫天然气安全、高效地集输至关重要。本文主要针对高含硫天然气集输管道硫沉积预测方法这一问题展开研究,主要完成了以下工作： (1)研究元素硫在集输管道中沉积的机理有助于弄明白硫沉积发生的本质,同时也是建立集输管道硫沉积预测模型的基础和前提。为此,首先对元素硫的存在形式、密度、黏度、比热和凝固点变化规律等物理性质进行了定性分析和定量研究。然后对计算高含硫天然气物性参数的方法进行了优选,得到：DPR模型结合WA校正法是计算高含硫天然气压缩因子的最佳方法,同时BWRS状态方程计算压缩因子时也具有较高的精度；Dempsey模型结合Standing校正法是计算高含硫天然气黏度的最佳方法。在此基础上,明确了硫化氢是元素硫来源的物质基础,根据化学反应平衡原理确定了元素硫的溶解与沉积主要以物理溶解与沉积为主。元素硫的沉积主要考虑溶解度的变化,而压力、温度和气体组分是影响元素硫溶解度的主要因素。 (2)分析评价了现有典型预测硫在高含硫气体中溶解度方法的适用性和局限性,在此基础上,提出运用遗传算法结合BP神经网络预测硫在高含硫气体中的溶解度。设计了该模型的计算步骤,讨论了该模型的参数设置,并对该模型进行了测试和验证,结果表明遗传BP神经网络预测模型的精度较高。 (3)根据水平管道中气固运移特征和气固两相流动理论,对管道内析出的固体硫颗粒进行了受力分析,应用固体颗粒群临界流速计算模型分析了元素硫固体颗粒在管道中发生沉积的条件。运用FLUENT软件中的RSM模型和DPM模型研究了析出位置不立即发生沉积的硫颗粒在直管段、水平弯管以及阀门等处的运移沉降规律,硫颗粒在直管段中的沉积率随着气流流速的增大而减小,随着颗粒直径的增大而增大；硫颗粒在水平弯管中的沉积率随气流流速、颗粒直径和弯曲比的增大而增大；硫颗粒在阀门处的沉积率随气流流速和颗粒直径的增大而增大,随阀门开度的增大而减小。 (4)基于(1)～(3)的研究成果,结合高含硫天然气集输管道压力温度分布预测模型,建立了高含硫天然气集输管道硫析出位置、硫沉积条件判定以及硫沉积量计算的预测模型,并运用这些模型对国内某高含硫气田集输管道的硫沉积问题进行了分析,结果表明,模型预测结果与实际较为吻合。
[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.
【学位授予单位】：西南石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE86

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