Rapidly Estimating Natural Gas Compressibility Factor

Natural gases containing sour components exhibit different gas compressibility factor(Z) behavior than do sweet gases.Therefore,a new accurate method should be developed to account for these differences.Several methods are available today for calculating the Z-factor from an cquation of state. However,these equations are more complex than the foregoing correlations,involving a large number of parameters,which require more complicated and longer computations.The aim of this study is to develop a simplified calculation method for a rapid estimating Z-factor for sour natural gases containing as much as 90% total acid gas.In this article,two new correlations are first presented for calculating the pseudo- critical pressure and temperature of the gas mixture as a function of the gas specific gravity.Then,a simple correlation on the basis of the standard gas compressibility factor chart is introduced for a quick estimation of sweet gases' compressibility factor as a function of reduced pressure and temperature.Finally,a new corrective term related to the mole fractions of carbon dioxide and hydrogen sulfide is developed.     

Analyzing solubility of acid gas and light alkanes in triethylene glycol

Physical solvents such as ethylene glycol （EG）, diethylene glycol （DEG）, and triethylene glycol （TEG） are commonly used in wet gas dehydration processes with TEG being the most popular due to ease of regeneration and low solvent losses. Unfortunately, TEG absorbs significantly more hydrocarbons and acid gases than EG or DEG. Quantifying this amount of absorption is therefore critical in order to minimize hydrocarbon losses or to optimize hydrocarbon recovery depending on the objective of the process. In this article, a new correlation that fully covers the operating ranges of TEG dehydration units is developed in order to determine the solubility of light alkanes and acid gases in TEG solvent. The influence of several parameters on hydrocarbon and acid gas solubility including temperature, pressure, and solvent content is also examined.     

Predicting hydrate forming pressure of pure alkanes in the presence of inhibitors

An inherent problem with natural gas production or transmission is the formation of gas hydrates, which can lead to safety hazards for production/transportation systems, and substantial economic risks. Hydrate inhibition with different inhibitors such as, methanol, ethylene glycol （EG）, triethylene glycol （TEG）, and sodium chloride solution continues to play a critical role in many operations. An understanding of when the hydrates form in the presence of these hydrate inhibitors, is therefore necessary to overcome hydrate problems. Several thermodynamic models have been proposed for predicting the hydrate formation conditions in aqueous solutions containing methanol/glycols and electrolytes. However, available models have limitations that include the types of liquid, compositions of fluids, and inhibitors used. The aim of this study is to develop a simple-to-use correlation for accurate prediction of hydrate-forming pressures of pure alkanes in the presence of different hydrate inhibitors, where the obtained results illustrate good agreement with the reported experimental data.     

Novel approaches for the prediction of density of glycol solutions

Two new approaches for the accurate prediction of densities of the commonly used glycol solutions in the gasprocessing industry are presented in the article. The first approach is based on developing a simple-to-use polynomial correlation for an appropriate prediction of density of glycol solutions as a function of temperature and weight percent of glycols in water, where the obtained results show very good agreement with the reported experimental data. The second approach, however, is based on the artificial neural networks （ANN） methodology, wherein the results demonstrate the ability of the introduced method to predict reasonably accurate densities of glycols under operating conditions. Comparisons of the two novel approaches indicated that the simple-to-use correlation appears to be superior owing to its simplicity and clear numerical background, wherein the relevant coefficients can be retuned if new and more accurate data are available in the future. The average deviation of the new proposed polynomial correlation results from reported data is 0.64 kg/m^3 whereas the average deviation of artificial neural networks （ANN） methodology from reported data is 1.1 kg/m^3.