Sour Gas Mixtures Research Articles

Thermodynamic modeling of sour gas mixture (CH4/C3H8/H2S) hydrate formation in the presence of thermodynamic inhibitors mono ethylene glycol, NaCl, and CaCl2 using the ion pair concept

Thermodynamic modeling of hydrate equilibrium conditions for sour gas mixture (CH4/C3H8/H2S) in the presence of pure water and thermodynamic inhibitor(s) (MEG, NaCl, and CaCl2) was investigated in this study. Hydrate and vapor phases were evaluated using the van der Waals and Plattueew model, and the Peng-Robinson EoS along with the classic van der Waals mixing rule, respectively. As a different approach, by combining non-synonymous salt ions and forming ion pairs, they can be considered as molecular species. So ion pair concept was used to investigate liquid phase and calculate water activity coefficient in this phase. For short-range and long-range forces, the NRTL molecular model and the Pitzer-Debye-Hückel equation was used, respectively. The results of experimental data in the previous work were compared with the results of the proposed model and PVTsim Nova 3.0 software, and it was discovered that they have an acceptable agreement together. The average deviation in pressure (AARDP %) related to the model is 0.36% to 4.34% and 3.41% to 5.68% related to the software.

Improving the prediction of elemental sulfur solubility in sour gas mixtures by modifying cubic equations of state

So far, several correlations have been developed to forecast the solubility of sulfur in various mixtures of acid gases with different concentrations of hydrogen sulfide. These correlations are correlative methods requiring experimental sulfur solubility data to tune their model parameters. Besides, correlative methods have many restrictions and are useable only for a limited range of operating conditions. Another approach for predicting sulfur solubility is applying equations of state. The predictive ability of equations of state has made them a powerful tool to more accurately perform the equilibrium phase calculations of acid gas mixtures in a vaster operating range. The major problem of the equations of state is the inaccurate characterization of their parameters for elemental sulfur due to its chemical complexity. In this study, the performance of three cubic equations of state is evaluated for forecasting the solubility of elemental sulfur in different acid gas mixtures. The main idea which is supposed to be put forward for rectifying defects of cubic equations of state is to model the trend of errors propagated in predicting the solubility of sulfur. Indeed, the effect of sulfur chemical reactions is ignored, and a generalized correction function is developed to compensate for deviations arising from the simplification assumptions. Subsequently, the results predicted by modified equations of state are compared to experimental sulfur solubility data and previously existing methods. Finally, a case study is performed on a sour gas reservoir sample to predict precipitation P-T curve and to calculate sulfur solubility changes with the pressure and temperature reduction throughout the gas well. Predicted results revealed the proposed correction function could significantly decrease the solubility calculations obtained from the modified equations of state under 5.6%, which is a good achievement compared to their original versions.

Measurements and Modeling of the Hydrate Phase Equilibrium Condition of Sour Gas Mixtures in the Presence of Salts and Alcohols

Measurements and Modeling of the Hydrate Phase Equilibrium Condition of Sour Gas Mixtures in the Presence of Salts and Alcohols

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods.

The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model’s performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

Experimental Study on the Elemental Sulfur Solubility in Sour Gas Mixtures

The investigation of elemental sulfur solubility plays critical roles on sour gas reservoir development. In this paper, the solubility of elemental sulfur was directly measured by static method with gas samples from well M1 of a sour gas reservoir in Sichuan Basin. The results show that the solubility of elemental sulfur ranges from 0.001 g/cm3 to 0.968 g/cm3 at 40–98.9 MPa and 15–49.8 MPa. The elemental sulfur solubility increases with increasing temperature and pressure, especially when the pressure is greater than 30 MPa. Moreover, the H2S content in sour gas mixtures is also an important factor affecting elemental sulfur solubility. The elemental sulfur solubility increases with increasing H2S content of the sour gas mixtures. The experimental data in this paper display a consistent trend with the reported experimental data. Based on the experimental results, the chrastil-type model, Robert’s model and Hu’s model were investigated and compared. The results show that the chrastil-type model by fitting experimental data in this paper has less error and higher accuracy in calculating elemental sulfur solubility in M gas reservoir. The chrastil-type models proposed in the literature, meanwhile, are only based on the regression of specific gas components and experimental conditions, which lead to a large error in the calculation of elemental sulfur solubility of sour gas samples in this research. The research results provide important basic data and technical support for the development of M gas reservoir.

Research on flow of sour gas mixture in deep well annulus

Pressure control of oil and gas wells in high-sulfur natural gas reservoirs (well control) is critical in oil and gas drilling. Well control accidents have caused great losses of life and property. From 1960 to 1996, more than 1,120 blowout accidents occurred in the Gulf of Mexico and neighboring state capitals. Studying the principles of annulus flow with sour gas can help in early monitoring of overflow and provide theoretical guidance in actual drilling. Research of gas properties and phase behavior can reveal the reasons that sour gas is prone to blowout. The fluid flow principles of the annulus are analyzed with different contents of sour gas. In this study, a multiphase flow model of sour gas in the annulus is established based on sour gas solubility and critical parameters. The model is used to calculate and analyze the changes in sour gas properties and wellbore flow parameters with well depth. It was found that when high-content sour gas overflows, the gas expands violently near the wellhead, and the risk of blowout increases.

Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods

Accurate determination of sulfur solubility in pure hydrogen sulfide (H2S) and sour gas mixtures has a leading role and a fundamental importance in handling and addressing sulfur deposition issues. In this study, rigorous paradigms based on two artificial neural network (ANN) types, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN), optimized by Levenberg–Marquardt (LM) algorithm were proposed as machine learning (ML) modeling tools to predict the solubility of sulfur in sour gas mixtures and pure H2S. Besides, explicit and simple-to-use correlations were established using gene expression programming (GEP). The paradigms derived from the methods aforementioned were developed using widespread experimental database. The obtained results indicated that the outcomes gained from the proposed MLP, CFNN and GEP-based correlations are in a high coherence and agreement with the experimental data. In addition, it was found that among the all suggested schemes, CFNN models are the most accurate paradigms for estimating the solubility of sulfur in sour gas mixtures and pure H2S with root mean square error (RMSE) of 0.0232 and 3.8101, respectively. Furthermore, a comparison between the performance of CFNN and the prior alternatives demonstrated that the CFNN models predict the solubility of sulfur in sour gas mixtures and pure H2S more accurately. Moreover, based on the trend analysis, it was concluded that the predictions of CFNN follow the real tendency of sulfur solubility in pure H2S and sour gas mixtures with respect to the input parameters. Besides, the sensitivity analysis dictated that pressure and temperature have the most significant impact on sulfur solubility calculation in pure H2S and sour gas mixtures. The results reported in this investigation revealed that implication of the considered soft computing approaches in the estimation of sulfur solubility in sour gas mixtures and pure H2S can lead to the generation of more reliable predictive paradigms which can be integrated in other related applications. Lastly, the findings of this study can help for effective prediction of the solubility of sulfur in sour gas mixtures and pure H2S while simulating different natural gas processes.

New results of H2S oxidation at high pressures. Experiments and kinetic modeling

The present study deals with the oxidation of H2S at high pressures. In some local scenarios, combustion is seen as an alternative possibility for the use of sour gas mixtures containing H2S. Therefore, further research is needed in terms of H2S oxidation characteristics and kinetics, which might be useful for existing processes like the Claus process. Experiments have been performed in the present work in a flow reactor under diluted conditions at different manometric pressures (0.6, 10, 20 and 40 bar). The influence of oxygen concentration (λ = 1 and λ = 6), temperature (450–1100 K) and gas residence time (371/T(K)-9280/T(K), in seconds) have been studied. At a given pressure, increasing oxygen concentration shifts the onset of H2S conversion to lower temperatures and makes the H2S consumption more abrupt. Gas residence time has an important influence on the H2S oxidation behavior under the experimental conditions studied. A recent kinetic model by the authors has been updated with two new reactions, obtaining a good prediction of H2S oxidation over a wide variety of experimental conditions, both from this work and the literature. The formation of H2O2 species, favored at high pressures, is responsible for the importance of the two new reactions here proposed, improving the model predictions at high pressures. At atmospheric pressure, the reaction pathways obtained using the current mechanism, remain essentially unaltered in comparison with previous studies.

Molecular dynamics simulation of natural gas sweetening by monoethanolamine

The aim of the study is to investigate sweeting process of sour gas by dynamic simulation of monoethanolamine (MEA) molecule. In the present paper using molecular dynamic simulation, the interaction of sour gas mixture included methane, ethane and H2S with MEA as absorption was also investigated the quantum method DFT B3LYP 6-311 (+) G** was used for electric charge calculation. The simulation results confirmed that the tendency of the H2S molecule is to be absorbed to amine nitrogen and oxygen hydroxyl group in MEA. No tendency for strong interaction between sulfur atoms of H2S molecule and hydrogen of amine or hydroxyl groups was observed. The investigation of changing distance between the hydrogen of H2S and nitrogen/oxygen of MEA confirmed a stable between hydrogen atoms of H2S and nitrogen/oxygen atoms in MEA. Also the investigation of distance changing show movement of hydrogen atoms of H2S molecule which interacted with MEA molecule in the time frame of the simulation. This study was observed that after absorption of H2S molecule by MEA molecules sour of them made the bridge for connection of MEA molecules with each other. Actually H2S molecules after interact with MEA molecules used addition their free hydrogen forinteraction and Making Bridge. Finally a structure of some MEA molecules are joined together, which are stable up to end of the simulation.

Tunable sour gas separations: Simultaneous H2S and CO2 removal from natural gas via crosslinked telechelic poly(ethylene glycol) membranes

Gas separation studies typically investigate polymeric materials in the context of pure gas, and occasionally binary mixed gas properties, yet real world separations involve a myriad of components that can affect separation performance. The purification of sour gas demands efficient separations of both H2S and CO2 from natural gas, making realistic testing critical. In this work, we introduce a novel crosslinking scheme of the commercially ubiquitous polymer, poly(ethylene glycol) (PEG), via a facile route amenable to thin film composite formation. We crosslinked a systematic series of telechelic PEG oligomers with average molecular weights of 200–2050 g mol−1 and investigated the gas transport properties of the corresponding membranes in pure, binary CO2/CH4, and ternary H2S/CO2/CH4 sour gas mixtures. Crosslinking effectively eliminated PEG crystallinity for molecular weights up to 1000 g mol−1 and favorably increased CO2/CH4 selectivity. Though previously unexplored for H2S separations, crosslinked PEG membranes demonstrated exceptionally high H2S/CH4 selectivities ranging from 65-116 in ternary sour gas feeds. We achieved tunable transport performance of CO2/CH4 and H2S/CH4 separations by balancing PEG content with crosslink density to disrupt crystallinity. Crosslinked PEG derived from a 2050 g mol−1 oligomer showed a semi-crystalline microstructure, defining the upper limit of PEG chain length of this system for gas separation applications. Thermal and diffraction studies on the PEG membranes further revealed only amorphous morphologies up to 1000 g mol−1 and that the resulting membranes spanned both glassy and rubbery regimes, which resulted in profound differences on CO2/CH4 and H2S/CH4 transport performance.

Investigation on sulfur solubility in sour gas at elevated temperatures and pressures with an artificial neural network algorithm

Accurate prediction of the solubility of elemental sulfur in sour gas mixtures has been recognized as a key issue in the development of sour gas fields. Many experimental measurements and empirical models have shown the complicated relationships between sulfur solubility and sour gas properties. However, the accurate model that can be used to predict sulfur solubility in sour gas over a wide range of temperature and pressure is rare. Therefore, the objective of this work is to build an efficient model, namely T-S fuzzy neural network (T-S FNN), to investigate the sulfur solubility in sour gas. The model considers the reservoir pressure, temperature, the mole fraction of methane, hydrogen sulfide and carbon dioxide as input parameters and the sulfur solubility as target parameters. Subsequently, multiple experimental sulfur solubility data sets accessible to the literature are employed to train and test the model respectively. Finally, a series of studies are conducted to appraise the accuracy and generalization capability of the model. The result shows that the predicted solubility data had great agreement with experimental sulfur solubility with overall average absolute relative deviation of 5.35%, which proves the model in this work is feasible and effective. Additionally, it provides a new idea to the prediction of sulfur solubility in sour gas and helps operators to develop the sour gas reservoir better.

Fluorinated copolyimide membranes for sour mixed‐gas upgrading

ABSTRACTThree random and two block 4,4′‐(hexafluoroisopropylidene)diphthalic anhydride (6FDA)‐based copolyimides with different 6FpDA:Durene molar ratio varying from 25 to 80% were prepared and characterized. The pure‐gas permeation data of their membranes were investigated at 100 psi and 22 °C. The CO2/CH4 ideal selectivity coefficient increased to around 47 with the increase of the 6FpDA content to 80% in the copolymer backbone while the CO2 permeability coefficient found to be the highest (378 barrer) with highest Durene content copolymer. Based on its attractive pure‐gas permeation properties(CO2/CH4 = 47), 6FDA‐6FpDA/6FDA‐Durene (4:1) block copolyimide was selected for further analyses, where the effect of pressure and temperature on its gas transport properties was evaluated. Furthermore, the mixed‐gas permeation properties were investigated using multicomponent sweet and sour gas mixtures prepared from N2 (30% or 10%), CH4 (59%), C2H6 (1%), CO2 (10%), and H2S (0% or 20%)accordingly. The sweet mixed‐gas CO2/CH4 selectivity and CO2 permeability coefficients of 6FDA‐6FpDA/6FDA‐Durene (4:1) are around 39 and 45 barrer, respectively, at elevated pressure (800 psi). The polymer, however, showed nonideal behavior when subjected to high H2S‐content gas mixture (20 vol. % H2S), where the CO2/CH4 selectivity value dropped to around 21 and the H2S/CH4 selectivity coefficient is 13. The CO2 and H2S permeability coefficients are 42 and 26 barrer, respectively, at an upstream pressure up to 500 psi. When plotted on the combined acid gas permeability‐selectivity curve, the polymer separation efficiency was nearby the high‐performing polymers reported in the literature, and way superior to the industrial standard glassy polymer, cellulose acetate, used currently in gas separation. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48336.

Pure - and sour mixed-gas transport properties of 4,4′-methylenebis(2,6-diethylaniline)-based copolyimide membranes

Three aromatic 6FDA-Durene/MDEA random copolyimides with different Durene:MDEA molar ratios (3:1; 1:1 and 1:3) were synthesized, and pure gas permeation properties through their dense films were investigated. Compared to their corresponding homopolymers (6FDA-MDEA and 6FDA-Durene), the copolymers exhibit significant improvement in gas separation performance, as the membranes display improved gas transport properties. For example, for pure gas, the CO2 permeability for 6FDA-Durene/MDEA (3:1) is around 234 Barrer and CO2/CH4 selectivity equal to 17.8 obtained at 22 °C and feed pressure of 100 psi. In addition, for sweet gas mixture consisting of 10, 59, 30 and 1 vol% CO2, CH4, N2 and C2H6, respectively, the 6FDA-Durene/MDEA (1:3) copolyimide membrane shows CO2 permeability of 73 Barrer and CO2/CH4 selectivity of about 24 at an elevated pressure of 800 psi. For a sour gas mixture with high H2S content (20 vol % H2S in the feed gas), the membrane exhibits H2S/CH4 selectivity of 21; and H2S permeability of 112 Barrer. The transport properties of the herein reported membrane are similar to or even better than the performance exhibited by some polymeric membranes reported in the literature.

Equations of State with Group Contribution Binary Interaction Parameters for Calculation of Two-Phase Envelopes for Synthetic and Real Natural Gas Mixtures with Heavy Fractions

Three equations of state with a group contribution model for binary interaction parameters were employed to calculate the vapor-liquid equilibria of synthetic and real natural gas mixtures with heavy fractions. In order to estimate the binary interaction parameters, critical temperatures, critical pressures and acentric factors of binary constituents of the mixture are required. The binary interaction parameter model also accounts for temperature. To perform phase equilibrium calculations, the heavy fractions were first discretized into 12 Single Carbon Numbers (SCN) using generalized molecular weights. Then, using the generalized molecular weights and specific gravities, the SCN were characterized. Afterwards, phase equilibrium calculations were performed employing a set of (nc + 1) equations where nc stands for the number of known components plus 12 SCN. The equations were solved iteratively using Newton's method. Predictions indicate that the use of binary interaction parameters for highly sour natural gas mixtures is quite important and must not be avoided. For sweet natural gas mixtures, the use of binary interaction parameters is less remarkable, however.

Cardo-type random co-polyimide membranes for high pressure pure and mixed sour gas feed separations

A series of aromatic random co-polyimide membranes based on 6FDA-Durene/CARDO backbone with varying content of CARDO moiety (3:1; 1:1 and 1:3) were synthesized for enhanced acid gas separation performance. Gas transport properties of pure and mixed gas streams consisting of H2S, CO2, He, CH4, N2 and C2H6 through the dense films of the co-polyimide were studied. Mixed sweet gas tests were done with feeds containing no H2S up to 55bar, while mixed sour gas measurements were conducted with feeds containing H2S up to 34bar for 10% H2S and 20% H2S. The membranes exhibit very attractive pure gas transport properties, as CO2 permeability and CO2/CH4 selectivity are up to 323 barrer and 35 respectively. Furthermore, the transport properties of sour gas mixture consisting of five gases were also found attractive, as the CO2/CH4 and H2S/CH4 ideal selectivities are in the range of 18–23 and 19–21 respectively; while CO2 and H2S permeabilities are in the range of 38–51 and 40–47 barrers respectively for 20% H2S in the gas mixture. These values and separation performance exhibited by the co-polyimide are comparable and very competitive even, as compared to the values obtained in some of the high performance polymeric membranes that have been reported in the literature. The stability of the random co-polyimide 6FDA-Durene/CARDO membrane under these aggressive environments is quite remarkable.

Prediction of water content of natural gases using the PC-SAFT equation of state

Water contents of synthetic sweet and sour natural gas–like mixtures and their constituents for wide ranges of temperature and pressure are studied in this work. The perturbed-chain version of statistical associated fluid theory (PC-SAFT) is used to describe the vapor and aqueous phase. In addition to repulsion and dispersion interactions, the 4C association scheme is used to account for self-association between water molecules. Using the PC-SAFT approach, the water contents of methane, ethane, hydrogen sulfide, carbon dioxide, and certain synthetic sweet and sour natural gas mixtures are predicted satisfactorily.

The Impact of Vapor/Liquid-Equilibria Calculations on Scale-Prediction Modeling

Summary Vapor/liquid-equilibria (VLE) calculations, particularly involving the phase behavior of carbon dioxide (CO2) and hydrogen sulfide (H2S), are used in scale-prediction modeling. In this work, the impact of VLE calculations for CO2- and H2S-rich gas phases and for acid- and sour-gas mixtures on scale-prediction calculations is evaluated. Three equations of state (EOSs)—Soave-Redlich-Kwong (SRK) (Soave 1972), Peng-Robinson (PR) (Peng and Robinson 1976), and Valderrama-Patel-Teja (VPT) (Valderrama 1990)—are implemented in the Heriot-Watt model and used in VLE calculations. The solubility of single-component CO2 and H2S in water and the solubility of a gas mixture in water were compared with experimental data in terms of the absolute relative deviation (ARD). The solubility data were then used in PHREEQC (USGS 2016) to calculate the impact of using different EOSs on carbonate and sulfide scales, particularly on calcium carbonate (CaCO3) and iron sulfide (FeS). Average ARDs of 6.04, 4.10, and 3.77% between experimental and calculated values for CO2 solubility in water were obtained for the SRK, PR, and VPT EOSs, respectively. Similarly, for H2S solubility in water, average ARDs of 6.49, 6.66, and 6.48% were obtained for each EOS, respectively. For the solubility of sour- and acid-gas mixtures in water, average ARDs of 13.92, 13.25, and 10.78% were obtained, respectively. It has thus been concluded that the VPT EOS performs better than the SRK and PR EOSs in VLE calculations for the analyzed data. The errors introduced in VLE calculations have been found to impact the calculation of the amount of CaCO3 precipitated, with consequences for scale-inhibitor selection. Higher deviations were found in the amount of CaCO3 precipitation for gas mixtures when compared with a single-component, CO2-rich phase. Furthermore, the large errors occurring in VLE calculations for H2S solubility have not been found to impact the calculation of the amount of FeS precipitated when H2S is in excess with respect to Fe2+. In addition to this, a case study that was performed by use of formation-water data from the Brazilian presalt revealed that the choice of EOS can cause errors of 6 kg of precipitate during each day of production. Scale-prediction calculations carried out with PHREEQC demonstrate that VLE calculations can have a high impact on mineral precipitation. Thus, it is recommended that the best VLE model available should always be used for scale-prediction modeling, particularly when mixtures of gases are present.

In-depth study on the solubility of elemental sulfur in sour gas mixtures based on the Chrastil's association model

Abstract Accurately predicting the solubility of elemental sulfur in sour gas mixtures is a primary task. As a current and widely-used model on the solubility of elemental sulfur in sour gas mixtures, Chrastil's association model has a big error in the process of predicting experimental data based on different fitting methods. This paper combined with experimental data reported by relevant scholars about elemental sulfur solubility in sour gases and selected density, temperature and pressure as three important influential factors. According to different fitting methods, we can calculate the correlation parameters in Chrastil's model. Then different solubility formulas can be used to predict the solubility of elemental sulfur in sour gas mixtures. Through in-depth research and analysis of Chrastil's solubility model from numerical aspects, it's easy to find the irrationality about Chrastil's solubility model and fitting methods. Especially in fitting methods, further improvement of the fitting method is proposed and used to predict the solubility of elemental sulfur in sour gas mixtures. The calculation results show that some improvements of the predicting precision have been achieved by using the improved fitting method in Chrastil's association model.

A new prediction model of elemental sulfur solubility in sour gas mixtures

The mining measure of sour gas reservoirs is different from that in the conventional natural gas reservoirs due to the strong corrosivity and highly toxic of H2S. Accurately predicting the solubility of elemental sulfur in sour gas mixtures is the primary task. As a current and widely-used model on the solubility of elemental sulfur in sour gas mixtures, Chrastil's solubility prediction model cannot fit experimental data well. By analyzing the Chrastil's model, we transfer the constant correlation coefficient k in Chrastil's model to be a variate as a function of temperature. We also introduce the M by weakening the effects of the gas density and then propose a new predication model. The coefficients in the new model are fitted by the experimental data. By comparing the new model with other similar type models such as Robert's formula and Hu's formula, we find the predicated results from the new model can fit the experimental data better.

A New Approach for Accurate Prediction of the Phase Behavior of the Complex Mixtures of Acid Gases, Hydrocarbons, Water, and Methanol in the Petroleum Industry

In this work a new approach for accurate prediction of the vapor-liquid equilibrium of the complex mixtures of water, methanol, acid gases (H2S, CO2), and hydrocarbons considering the hydrogen bonding association, hydrolytic dissociation, and acid gas solvation effects is presented. The overall average absolute deviation between the predicted and measured compositions regarding 330 sour gas mixtures is about 3.22%. The proposed CPA/electrolyte model is quite reliable over wide ranges of temperatures and sour gas concentrations and can be employed for accurate design of sour natural treatment and flow assurance systems in oil and gas industries.