影响酸性气体压缩因子计算精度的因素研究

由于酸性气体含有H2S和CO2,严重影响了天然气压缩因子计算准确性。为此将压缩因子计算模型、混合规则以及校正方法相互组合计算了酸性气体的压缩因子,结合试验数据分析了气体组分、压力、混合规则以及校正方法等因素对各计算模型计算精度的影响。研究表明,酸性组分含量较低时,Key混合规则优于SBV混合规则,而DPR模型与GXQ校正方法组合计算结果最佳,平均绝对误差仅为0.6%。酸性组分含量较高时,除DPR模型外,其他模型Key混合规则优于SBV混合规则,且DAK模型、Key混合规则以及WZ校正方法的组合最佳,计算平均绝对误差小于0.5%。HY模型计算平均绝对误差大于5%,不能满足工程计算要求。     

CO_2气藏偏差因子计算模型评价

应用常规酸性气体偏差因子计算模型,结合Key’s、SBV及RK混合准则和酸性气体临界参数校正方法,计算CO2气藏偏差因子,并与实验测定值对比,评价适合CO2气藏偏差因子计算的模型。研究表明:BB经验公式对CO2气藏偏差因子计算适应性最强,误差小于3%,满足工程计算要求;常规酸性气体临界参数校正方法,很难改善CO2气藏偏差因子计算精度。各种模型中,RK+WA精度最高,SBV+CKB误差最大。当压力35 MPa时,各种经验公式计算误差相应增大,但BB+SBV模型精度仍然很高,其中,BB+SBV未校正模型在42.34 MPa时,误差仅2.64%,适合高压CO2气藏偏差因子计算。     

C6+组分对管输天然气相特性的影响

为提高对管输天然气相特性的预测精度,特别是针对含有C6+重组分的管输天然气,开展C6+组分特征化分析.使用PR状态方程,结合气-液闪蒸计算,对管输天然气的相特性进行预测分析.通过对室内实验测定的管输天然气烃露点数据验证表明,用不同C6+组分特征化分析方法对管输天然气相特性进行预测的结果存在一定偏差,但与将C6+组分合并为己烷近似分析相比较,其预测精度更贴近实测的烃露点.     

天然气水合物生成条件预测模型及适用性评价

水合物的生成给天然气的开采和集输等过程带来诸多困难。为了准确预测天然气水合物生成条件,选取Parrish-Prausnitz、Du-Guo和Chen-Guo种模型,采用C＋＋语言编制程序对不含抑制剂时天然气水合物的生成条件进行求解,通过模型预测值与实验值比较,评价各模型预测精度,确定各模型的适用范围。当天然气中不含Cj重烃组分及酸气时,推荐采用Du-Guo模型预测;当天然气有重烃组分,但酸性气体总体积含量低于1％时,推荐采用Parrish-Prausnitz模型预测;当天然气酸性气体总体积含量高于1％时,推荐采用Chen-Guo模型预测。     

一种预测超高压气藏压缩因子的新方法

天然气压缩因子的实验测定费用昂贵且耗时长,而应用经验关联式和状态方程求解又复杂且适用范围受限,计算精度和实用性难以确定。为此,根据Standing Katz天然气压缩因子图版,结合收集到的近1 000个实验数据点,采用最小二乘法拟合建立了高压天然气压缩因子的解析模型。与DPR、DAK、Brills、Ehsan等具有代表性的经验关联式和SRK状态方程的计算结果进行对比,所建模型计算得到的气藏压缩因子在高压及超高压条件下具有较高的精度,可满足工程计算的要求。     

基于密度校正的酸气拟临界参数计算公式

天然气拟临界参数是与天然气有关计算的基础参数,含有酸性气体天然气大多采用Kay混合规则和Wichert-Aziz 校正计算气体拟临界参数。而基于酸气密度计算方法国内还没有见到相关文章。收集了国内外各大气田93个酸性气体气样,结合实验分析结果建立了一个新的适合于酸性天然气拟临界参数计算的密度计算公式。采用新的计算公式计算某含H₂S、CO₂酸性天然气和常规天然气偏差因子,并与Kay混合规则和Wichert-Aziz校正计算模型、Standing方法实验结果进行对比分析,结果表明,该公式对于酸性气体具有较好的代表性,计算精度高于Kay混合规则和Standing 密度计算方法,对于低分子量常规气体,Standing方法计算精度最高。     

物性值法计算天然气压缩因子适应性分析北大核心CSCD

目的随着能量计量工作的不断推进,业内出现了一些采用物性关联技术对天然气发热量、相对密度等参数进行在线测试的仪器设备,其应用须结合GB/T 17747.3-2011《天然气压缩因子的计算第3部分:用物性值进行计算》推荐的“物性值法”计算工况压缩因子,物性值法在国内外的应用较少,对其适应性和准确度进行分析探讨,可为相关新技术研发和选用提供参考,有利于保障和促进新兴测试手段在天然气能量计量中的有效应用。方法以GB/T 17747.2-2011《天然气压缩因子的计算第2部分:用摩尔组成进行计算》推荐的“详细组成法”计算结果为主要参考,选用了国内典型的149个天然气组成以及部分模拟天然气组成数据,理论对比分析了详细组成法和物性值法计算天然气压缩因子的相对偏差,探讨了用物性值法计算天然气压缩因子的适用性。结果①对于绝大多数不同类型的商品天然气,采用物性值法与详细组成法计算得到的压缩因子的相对偏差在±0.10%以内;②当输入的高位发热量、相对密度满足±0.50%以内的准确度时,采用物性值法与详细组成法计算得到的压缩因子的相对偏差可满足GB/T 18603-2014《天然气计量系统技术要求》中对A、B级计量系统压缩因子0.30%的准确度要求;③对于GB/T 17747.3-2011适用范围内重烃含量相对较高的天然气,物性值法与详细组成法计算得到的压缩因子可能出现接近0.50%的相对偏差。结论对于绝大多数不同类型的商品天然气,采用物性值法计算压缩因子的方法是适应的,但对等效C 2+摩尔分数高于10%的天然气可能出现较大偏差,建议在能量计量系统方案选择过程中,结合详细组成数据开展不同方法计算压缩因子的差异比对分析,为选用技术经济性更佳的方案提供技术支持。     

低对比温度下PRSV方程的新温度函数关联式

通过回归C1~C16 16种饱和烷烃的饱和蒸气压值,提出了低对比温度下PRSV方程的新温度函数关联式。新关联式只与温度和偏心因子有关,不需要物质的特征参数,是一种普遍化形式。通过对53种未参加回归的不饱和烃与极性物质饱和蒸气压的计算表明,新温度函数关联式较原PRSV温度函数在低对比温度下计算精度有明显提高。通过计算C1~C16饱和烷烃的饱和液体密度表明,改进的温度函数保持了PR和PRSV方程预测饱和液体密度的精度。     

酸性天然气拟临界参数计算式

天然气拟临界参数是与天然气有关计算的基础参数，含有酸性气体的天然气大多采用Kay规则和Wichert＆Aziz校正计算气体拟临界参数，而基于酸气密度计算方法国内还没有见到相关文章。此次研究收集了国内外各大气田93个酸性气体气样，结合实验分析结果建立了一新的适合于酸性天然气拟临界参数计算的密度计算公式；并采用新的计算公式计算某含H2S、CO2酸性天然气和常规天然气偏差因子，并与Kay＇s混合规则和Wichert＆Aziz校正计算模型、Standing方法、实验结果进行对比分析。研究表明，该公式对于酸性气体具有较好的代表性，计算精度高于Kay＇s混合规则、Standing密度计算方法：而对于低相对分子质量常规气体Standing方法计算精度最高。     

基于对勾函数的新型天然气压缩因子计算关联式

为了解决目前工程上使用的天然气压缩因子计算方法的计算精度不足、计算效率较低、适用范围较小的问题,分析了天然气压缩因子图版特征,利用非线性曲面拟合方法对中低压图版和高压图版的6 988组数据进行拟合,得到了一种对勾函数形式的、适用于0.2≤p_pr≤30.0压力范围的新型天然气压缩因子经验公式,拟合值与图版值的平均绝对误差、平均相对误差和均方根误差分别为0.012 51、0.013 59和0.017 57,拟合效果良好。利用237组中低压天然气压缩因子实测数据和219组高压天然气压缩因子实测数据对该方法进行验证,并与其他5种常用的显式或隐式的计算方法进行对比。验证结果表明,利用该方法得出的计算结果与实测值之间的平均绝对误差、平均相对误差和均方根误差分别为0.018 95、0.015 08和0.024 19,计算精度较高且优于其他5种方法,能够在矿场实践中快速准确地预测出不同条件下的天然气压缩因子。      

New Correlation for Calculation of Hydrocarbon Gas Minimum Miscibility Pressure (MMP) Using Wide Experimental Data

The authors present a new empirically derived correlation for estimating the minimum miscibility pressure (MMP) required for multicontact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding. Only few empirical correlations exist for determining the MMP. These correlations are often used to estimate the MMP without considering the composition of the injected gas. On the other hand these correlations are based on a limited set of experimental data, which are not quite applicable. In addition, in such correlations the complex condensing/vaporizing displacement process is not regarded. In this study, however, the derived correlation investigates the influence of the vaporizing/condensing drive mechanism and oil and gas composition on gas miscibility pressure. MMP has been correlated with temperature, oil composition, and injection gas composition. Their effect on hydrocarbon gas MMP has been documented by using sensitivity analysis by slim tube experimental data. The new correlation is based on regression of widely experimentally measured MMP data in literature and data derived from slim tube experiment in this study. By comparing the calculated MMPs from the improved correlation data with currently used correlations and experimentally measured data, it was found that the new correlation is significantly more accurate than other correlations.     

PVT-generated Correlations of Heavy Oil Properties

Pressure-volume-temperature (PVT) properties are necessary to reservoir engineering calculations in porous media and it is important for calculations in pipeline as well. This work presents a new set of correlation for estimating Iranian Crude oils properties based on some experimental data. Whenever there is no representative experimental PVT data, these correlations can be used for oils of API ranging between 15 and 30. New correlations was developed to calculate oil formation volume factor (Bo), bubble point pressure (Pb), and solution gas oil ratio (Rs). Finally, a comparison is made between these correlations and other published correlations such as Standing, Vazquez and Beggs, Glaso, Farshad et al., Al Marhoun, Petrosky and Farshad, and Hanafi et al. and it is found out that the new correlations are more accurate than the other ones.     

Predicting Natural Gas Hydrate Formation Temperature Using Levenberg-Marquardt Algorithm

In this study, two new correlations are proposed to predict the natural gas hydrate formation temperature as a function of pressure and specific gravity. The first correlation has been developed using Vandermonde matrix and the coefficients of the second correlation have been obtained by Levenberg-Marquardt algorithm. The error analysis shows the good performance of the two new proposed correlations to predict hydrate formation temperature compared to correlations presented earlier and also the experimental values.     

Determining Appropriate Size of Inlet Scrubber and Contactor in TEG Gas Dehydration Systems

Abstract

Most natural gas producers use triethylene glycol (TEG) to remove water from the natural gas stream in order to meet the pipeline quality standards. In designing such a process, it is crucial that the size of the inlet scrubber as well as the gas/glycol contactor is correctly estimated. The aim of this study is therefore to develop simple-to-use correlations for appropriate sizing of the inlet scrubber and contactor covering a wide range of operating conditions of TEG dehydration systems. Results illustrate that a designer may consider these correlations as appropriate estimation tools during the design of a gas dehydration system utilizing TEG solvent.     

Reservoir oil bubblepoint pressures revisited; solution gas–oil ratios and surface gas specific gravities

A large number of recently published bubblepoint pressure correlations have been checked against a large, diverse set of service company fluid property data with worldwide origins. The accuracy of the correlations is dependent on the precision with which the data are measured. In this work a bubblepoint pressure correlation is proposed which is as accurate as the data permit.Certain correlations, for bubblepoint pressure and other fluid properties, require use of stock-tank gas rate and specific gravity. Since these data are seldom measured in the field, additional correlations are presented in this work, requiring only data usually available in field operations. These correlations could also have usefulness in estimating stock-tank vent gas rate and quality for compliance purposes.     

A comparison of correlations used for Venturi wet gas metering in oil and gas industry

Wet gas metering is becoming more and more important for the oil and gas industry, and the Venturi meter is a favorable device for the metering of unprocessed wet natural gas production flows. As we known that the Venturi meter used in wet gas gives the higher measured differential pressure reading than in single gas phase flow, which is called over-reading. In this study, we evaluated eight correlations which are commonly used in high pressure and high Lockhart–Maretinelli parameter, and the performance of the eight correlations under low pressure and low Lockhart–Maretinelli parameter was discussed. Three Venturi meters of 50 mm diameter have been tested in Tianjin University's multiphase flow loop. The Venturi meters, with diameter ratio values of 0.4048, 0.55 and 0.70, have been tested at the working operation pressure of 0.15 MPa, 0.20 MPa, 0.25 MPa and the gas Froude number from 0.6 to 2.0, and the modified Lockhart–Maretinelli parameter from 0.0022 to 0.06, and the ratio of the gas to total mass flowrate from 0.5 to 0.99. Finally, effects of Lockhart–Maretinelli parameter, pressure, gas Froude number and diameter ratio to these correlations are analyzed with new independent data.     

Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network

Natural gas commonly contains water as a contaminant that can condense to water or form gas hydrates, which causes a range of problems during gas production, transportation, and processing. Therefore, the removal of gas moisture is of great importance. A common and popular method for removing water contamination from natural gas is using solid dehydrators. Calcium chloride is a nonregenerative desiccant to dehydrate natural gas. With continual water adsorption, CaCl₂ changes to consecutively higher states of hydration, finally producing a CaCl₂ brine solution. This method does not require heating or moving parts. In addition, it does not react with H₂S or CO₂. These features make this method a popular one for drying natural gas. Nevertheless, precise and simple methods are needed to predict the water content of natural gas dried by calcium chloride dehydrator units. In this study, an intelligent method, called the radial basis function neural network, was incorporated to predict the gas moisture dehydrated by calcium chloride in dehydration units. Modeling was performed under different conditions of a fresh recharge and before recharging. The overall correlation factor of 0.9999 for both the fresh charge and before charging conditions showed that the outputs of the proposed models were in agreement with the experimental data. In addition, the developed models were compared with the previously proposed intelligent models and classic correlations. The comparison showed that the developed model is superior to the previously proposed models and correlations regarding the accuracy of prediction.     

Applying ANFIS-PSO algorithm as a novel accurate approach for prediction of gas density

The accurate estimations of processes in gas engineering need a high degree of accuracy in calculations of gas properties. One of these properties is gas density which is straightly affected by pressure and temperature. In the present work, the Adaptive neuro fuzzy inference system (ANFIS) algorithm joined with Particle Swarm Optimization (PSO) to estimate gas density in terms of pressure, temperature, molecular weight, critical pressure and critical temperature of gas. In order to training and testing of ANFIS-PSO algorithm a total number of 1240 experimental data were extracted from the literature. The statistical parameters, Root mean square error (RMSE), coefficient of determination (R²) and average absolute relative deviation (AARD) were determined for overall process as 0.14, 1 and 0.039 respectively. The determined statistical parameters and graphical comparisons expressed that predicting mode is a robust and accurate model for prediction of gas density. Also the predicting model was compared with three correlations and obtained results showed the better performance of the proposed model respect to the others.     

New Empirical Correlations for Predicting Crude Oil Properties

Abstract

Pressure–volume–temperature (PVT) properties of crude oil are essential parameters used for prediction of fluid flow both in porous media and through transmission pipelines. Whenever laboratory works are absent, the engineer should use regionally developed correlations. A large portion of all crude oil resources is located in the Persian Gulf countries, and they have more or less similar API ranges and acidities, so that any empirical PVT correlation based on data from one region can adequately predict the behavior of others in this large geological basin. In this study, a new set of black oil–type correlations for bubble point pressure (P_b), solution gas–oil ratio (GOR; R_s), and formation volume factor (Bo) is proposed based on more than 400 Iranian crude oil PVT lab data. Moreover, previous works were reviewed, most of which were not suitable to model Iranian crude PVT behavior. Although the new correlations are developed over Iranian crudes, they can be used for prediction over any crude oil with similar compositional properties (API and acidity). Then the accuracy of these correlations is compared with the newly presented set and the superiority of this work for predicting those parameters is demonstrated.     

Phase equilibrium modelling of natural gas hydrate formation conditions using LSSVM approach

The formation of gas hydrates in industries and chemical plants, especially in natural gas production and transmission, is an important factor that can lead to operational and economic risks. Hence, if the hydrate conditions are well addressed, it is possible overcome hydrate-related problems. To that end, evolving an accurate and simple-to-apply approach for estimating gas hydrate formation is vitally important. In this contribution, the least square support vector machine (LSSVM) approach has been developed based on Katz chart data points to estimate natural gas hydrate formation temperature as function of the pressure and gas gravity. In addition, a genetic algorithm has been employed to optimize hyper parameters of the LSSVM. Moreover, the present model has been compared with five popular correlations and was concluded that the LSSVM approach has fewer deviations than these correlations so to estimate hydrate formation temperature. According to statistical analyses, the obtained values of MSE and R² were 0.278634 and 0.9973, respectively. This predictive tool is simple to apply and has great potential for estimating natural gas hydrate formation temperature and can be of immense value for engineers who deal with the natural gas utilities.