Natural Gas Viscosity Research Articles

Study on development methods of different types of gas wells in tight sandstone gas reservoirs

Reasonable production allocation of tight sandstone gas reservoirs is an important basis for efficient development of gas wells. Taking Block XX in Ordos Basin as an example, the modified flowing material balance equation was established considering the variation of gas viscosity and compression coefficient, the advantages and disadvantages of the method were discussed, and a reasonable production allocation process for gas wells was developed. The results show that: ① The commonly used flow material balance method ignores the change of natural gas compression coefficient, viscosity and deviation coefficient in the production process. The slope of the relationship curve between bottom hole pressure and cumulative production and the slope of the relationship curve between average formation pressure and cumulative production are not equal After considering this change. Compared with the results calculated by the material balance method, the results calculated by the flow material balance method are smaller. ② The production of 660 gas wells in the study area during stable production period is verified. Compared with the open flow method, the dynamic reserve allocation method is better, with an error of 0.06%. ③ The new method in this paper is used to allocate production for different types of gas wells. The cumulative production of different types of gas wells shows different degrees of increase. The I, II, III and IV types of gas wells increase by 32.26%, 30.29%, 23.58% and 25.07% respectively. This study provides technical support for dynamic reserve calculation and reasonable production allocation of gas wells in the study area, and has important guiding significance for the formulation of reasonable development plan and economic and efficient development of tight sandstone gas reservoirs.

Prediction of the viscosity of natural gas at high temperature and high pressure using free-volume theory and entropy scaling

Eighteen models based on two equations of state (EoS), three viscosity models, and four mixing rules were constructed to predict the viscosities of natural gases at high temperature and high pressure (HTHP) conditions. For pure substances, the parameters of free volume (FV) and entropy scaling (ES) models were found to scale with molecular weight, which indicates that the ordered behavior of parameters of Peng-Robinson (PR) and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) propagates to the behavior of parameters of viscosity model. Predicting the viscosities of natural gases showed that the FV and ES models respectively combined with MIX4 and MIX2 mixing rules produced the best accuracy. Moreover, the FV models were more accurate for predicting the viscosities of natural gases than ES models at HTHP conditions, while the ES models were superior to PRFT models. The average absolute relative deviations of the best accurate three models, i.e., PC-SAFT-FV-MIX4, tPR-FV-MIX4, and PC-SAFT-ES-MIX2, were 5.66%, 6.27%, and 6.50%, respectively, which was available for industrial production. Compared with the existing industrial models (corresponding states theory and LBC), the proposed three models were more accurate for modeling the viscosity of natural gas, including gas condensate.

Impact of Heavy Hydrocarbon Concentration on Natural Gas Flow through Transportation Pipelines

In this work, binary, ternary, quaternion, and quinary natural gas mixtures were evaluated including methane, ethane, propane, butane, and pentane to highlight their impact on pipeline performance and thermophysical properties of natural gas. The results presented that all the heavy hydrocarbons have a negative impact on natural gas phase envelope. For binary mixtures, methane/propane recorded the widest two-phase envelopes while the quinary mixtures generally formed the widest two-phase envelopes over the other mixtures. Besides, the heavy hydrocarbons content of different mixtures increased the critical pressures and critical temperatures in comparison to pure methane. The highest temperature drop of 6.495 °C was recorded by the binary mixture and the lowest temperature drop of 6.341 °C was by quinary mixture. The highest pressure drop of 4.964 bars was caused by the quinary mixture, while the lowest pressure drop of 4.1 bars was by the binary mixture. In addition, the results showed that natural gas density controlled by methane content caused increasing the methane content resulting in reducing the density of natural gas mixture. The viscosity of natural gas is a sensitive parameter to the content of the heavy hydrocarbon concentrations and all heavy hydrocarbons increased the viscosity of natural gas in comparison to pure methane.

Evaluation of hydrogen concentration effect on the natural gas properties and flow performance

The natural gas flowing through transmission pipeline is impure and has a wide range of non-hydrocarbons components at different concentrations like hydrogen. The presence of hydrogen in the natural gas mixture influences its properties and flow performance. The effect of hydrogen concentration on the natural gas flowing through a transportation pipeline has not been adequately investigated and widely comprehended. In this paper, several mixtures flow through pipeline include typical natural gas and hydrogen at different concentrations up to 10% are evaluated to demonstrate their impact on the flow assurance and the natural gas properties. The string Ruswil – Griespass part from the Transitgas project with 94 km length is simulated applying Aspen Hysys Version 9 and validated using Aspen Plus. The simulation specifications were 1.228 ∗ 106 kg/h mass flowrate, 1200 mm and 1164 mm the outer and inner diameters, and 75 bar and 29.4 °C operating pressure, and temperature. The effect of different hydrogen concentrations has been examined and the differences from the typical mixture are estimated. The results show that the presence of hydrogen in the natural gas mixture reduces its density, 10% hydrogen content records 11.78% reduction in the density of typical natural gas. Interestingly, it has been found that up to 2% of hydrogen concentration turns in elevating the viscosity of the typical natural gas while the viscosity decreases at the point that hydrogen content increases above 2%. In addition, the pressure losses over the transmission pipeline increases due to the presence of hydrogen, 10% hydrogen concentration turns in 5.39% increase in the pressure drop of the natural gas mixture. Also, the temperature drop across the pipeline decreases as the hydrogen concentration increases; 10% hydrogen content can result in a 6.14% reduction in the temperature drop across the pipeline. As well as, the findings prove that the hydrogen strongly impacts the phase envelope by changing from size symmetric to size asymmetric diagram. The effect of pipeline elevations has been investigated by changing the elevation up to 25 m uphill and 25 m downhill. The results state that increase the pipeline elevation turns in increasing the pressure losses over the pipeline length. Along with this, the results illustrate that the presence of hydrogen in the mixture elevates the critical pressure and reduces the critical temperature.

NEW CORRELATIONS FOR THE PSEUDOCRITICAL PARAMETERS AND THE ACENTRIC FACTOR S OF THE HYDROCARBON COMPONENT OF THE DNIEPER-DONETS PETROLEUM BASIN’S NATURAL GAS

NEW CORRELATIONS FOR THE PSEUDOCRITICAL PARAMETERS AND THE ACENTRIC FACTOR S OF THE HYDROCARBON COMPONENT OF THE DNIEPER-DONETS PETROLEUM BASIN’S NATURAL GAS

Rigorous prognostication of natural gas viscosity: Smart modeling and comparative study

The current study plays a major role in modeling natural gas viscosity in terms of several operating parameters including pseudo-reduced properties and molecular weight through radial basis function neural network (RBFNN), least-squares support vector machine (LSSVM), and multilayer perceptron neural network (MLPFNN). As it known, an important feature of any comprehensive modeling is the application of a large database for model development. Therefore, more than 3800 gas viscosity data points were used for modeling. For upgrading the efficiency of the abovementioned predictive tools, four optimization algorithms including levenberg-marquardt (LM), coupled simulating annealing (CSA), Bayesian regularization (BR), and scaled conjugate gradient (SCG), were integrated with them to find the optimal models’ parameters during prediction analysis. Consequently, it was understood that among the all suggested tools in this study, the MLP-LM and then MLP-BR are the most accurate models for estimating gas viscosity with root mean square error (RMSE) of 0.001 and 0.002, respectively. Comparison of the MLP-LM and MLP-BR with previously published models in literature demonstrates their higher prediction capability, with less numbers of input parameters (without needing any density data), than the existing literature models. Based on the sensitivity analysis, it is concluded that the molecular weight is the most affecting variable on the viscosity prediction. Finally, the suggested tools in this study can be of great value for effective estimation of gas viscosity in simulating both upstream and downstream natural gas processes.

Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system

Estimation of natural gas viscosity is essential for accurate analysis of gas reserves, reservoir simulation and optimum gas consumption. The general method for calculation of gas viscosity is to conduct experimental tests, however, these tests are normally time consuming and expensive as well as sometimes even impossible at some specific pressure and temperature conditions. In this study, a group method of data handling (GMDH)-type neural network was utilized to predict the viscosity of pure hydrocarbon and gas mixtures containing heavy components and impurities such as carbon dioxide, helium and nitrogen by using more than 3800 data points. First, the data sets were divided into three parts based on their density. Next, a model was developed for each part as a function of molecular weight, pseudo reduced temperature, pseudo reduced pressure and density. The proposed models were compared to 13 well-known models by performing graphical and statistical error analyses. The results indicate that the average absolute percent relative error of the proposed GMDH models is 3.45% and the standard deviation from experimental data is 0.0566. Error distribution curves indicate that the proposed models are able to accurately estimate natural gas viscosity over a wide range of pressure and temperature conditions as no significant error trend is observed in the predictions. Finally, the results of relevancy factor analysis showed that the mixture density has the largest impact on viscosity compared to the other input parameters.

A new model for the accurate calculation of natural gas viscosity

Viscosity of natural gas is a basic and important parameter, of theoretical and practical significance in the domain of natural gas recovery, transmission and processing. In order to obtain the accurate viscosity data efficiently at a low cost, a new model and its corresponding functional relation are derived on the basis of the relationship among viscosity, temperature and density derived from the kinetic theory of gases. After the model parameters were optimized using a lot of experimental data, the diagram showing the variation of viscosity along with temperature and density is prepared, showing that: ① the gas viscosity increases with the increase of density as well as the increase of temperature in the low density region; ② the gas viscosity increases with the decrease of temperature in high density region. With this new model, the viscosity of 9 natural gas samples was calculated precisely. The average relative deviation between these calculated values and 1539 experimental data measured at 250–450 K and 0.10–140.0 MPa is less than 1.9%. Compared with the 793 experimental data with a measurement error less than 0.5%, the maximum relative deviation is less than 0.98%. It is concluded that this new model is more advantageous than the previous 8 models in terms of simplicity, accuracy, fast calculation, and direct applicability to the CO2 bearing gas samples.

Viscosity investigation of natural gas hydrate slurries with anti-agglomerants additives

The viscosity of natural gas hydrates slurry in high-pressure hydrates slurry rheological measurement system is investigated, which is meaningful for hydrates risk management to solve flow assurance issues in deep-water offshore field. Based on an appropriate stirring speed and time, a relatively uniform and stable hydrates slurry were formed from a water-in-oil emulsion to study the hydrates formation and slurry viscosity under different water cuts, bath temperatures and AAs concentrations. The influence of water cut on hydrates formation and hydrates slurry viscosity is much more significant than that of bath temperature and AAs concentration. Results indicate that the hydrates volume fraction, the continuous liquid phase viscosity and the dispersion degree of hydrates particles in the slurry are the critical factors to affect the viscosity of natural gas hydrates slurry. Considering both of aggregation and breakage of hydrates particles, a natural gas hydrates slurry viscosity semi-empirical model is developed based on the Einstein effective medium theory. The key parameter non-Newtonian coefficient K of this model is determined by several empirical correlations according to the experimental conditions and fluid properties. The consistence of predicted and experimental data demonstrates the feasibility of this model.

New correlations for predicting pure and impure natural gas viscosity

Accurate determination of natural gas viscosity is important for successful design of production, transportation, and gas storage systems. However, most of available models/correlations suffer from complexity, robustness, and inadequate accuracy especially when wide range of pressure and temperature is applied. Present study illustrates development of two novel models for predicting natural gas viscosity for pure natural gas (CH4) as well as natural gas containing impurities. For this purpose, 6484 data points have been gathered and analyzed from the open literature covering wide range of pressure, temperature, and specific gravity levels, temperature ranges from −262.39 to 620.33 °F (109.6 to 600 K), pressure ranges from 1.4508 to 29,000 psi (0.0100–199.94801 MPa), and gas specific gravity ranges from 0.553 to 1.5741. Sensitivity analysis on the collected data points through design of experiments algorithm showed that pseudo reduced pressure and pseudo reduced temperature are the most effective parameters as the inputs of the models. The Leverage Value Statistics is applied and doubtful data points are determined.The average absolute relative error and the coefficient of determination of the proposed models for predicting pure/impure natural gas viscosity on a wide range of conditions are 5.67% and 1.87%, 0.9826 and 0.9953, respectively. Reliable accuracy of proposed models in comparison to eight commonly used correlations makes them attractive for possible implementing in natural gas simulation/modeling applications.

A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids

Precise evaluation of pure hydrocarbon and natural gas viscosity is vital for reliable reservoir characterization, simulation, transportation and optimum consumption. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. The need of new methods becomes important when there is not enough experimental data for specific composition, pressure, and temperature conditions. In this study, a promising approach is utilized for the prediction of viscosities of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrocarbon sulfide using over 3800 data sets. Gene Expression Programming (GEP) is employed to develop a general model for pure and natural gas viscosity. The proposed model is a function of pseudo reduced pressure, pseudo reduced temperature, molecular weight and density. In addition, comparative studies are performed between the results obtained by the GEP model and previously published empirical correlations. To this end, statistical and graphical error analyses are used simultaneously. The results obtained show a value of 4.9% for average absolute percent relative error which is a measure of relative absolute deviation from the experimental data. The results also propose that standard deviation as a sign of data scattering is only 0.0870. These observations illustrate that the GEP model is more robust, reliable and consistent than the existing correlations for prediction of pure and natural gas viscosity. Finally, the relevancy factor shows that molecular weight has the greatest effect on gas viscosity.

A Novel Correlational Approach to Estimating Natural Gas Viscosity

A new correlation to estimating natural gas viscosity in wide temperature, composition, and pressure ranges is presented on the basis of 1748 data for eleven different mixtures taken from the literature. In comparison with other commonly used correlations, the average error of the new correlation is found to be 3.56%.

On the viscosity of natural gas

This study reports new experimental viscosity measurements at high pressure and high temperature for a sour natural gas mixture. The measurements were conducted with a capillary tube viscometer at pressures ranging from 10.3 to 138MPa and temperatures up to 444K. The study develops a comprehensive model to predict natural gas viscosity in a wide range of pressures, temperatures and compositions. The arithmetic average of the model’s absolute error was found to be 2.4% compared with experimental data from the literature (containing the experimental data of the current study), which is acceptable for accurate engineering calculations; the absolute error demonstrates that this model is superior to other methods.

Estimation of dynamic viscosity of natural gas based on genetic programming methodology

Investigating the behavior of natural gas can contribute to a detailed understanding of hydrocarbon reservoirs. Natural gas, alone or in association with oil in reservoirs, has a large impact on reservoir fluid properties. Thus, having knowledge about gas characteristics seems to be necessary for use in estimation and prediction purposes. In this project, dynamic viscosity of natural gas (μg), as an important quantity, was correlated with pseudo-reduced temperature (Tpr), pseudo-reduced pressure (Ppr), apparent molecular weight (Ma) and gas density (ρg) by operation of the genetic programming method on a large dataset including 1938 samples. The squared correlation coefficient (R2), average absolute relative deviation percent (AARD%) and average absolute error (AAE) are 0.999, 2.55% and 0.00084 cp, respectively. The final results show that the obtained simple-to-use model can predict viscosity of natural gases with high accuracy and confidence.

Toward a predictive model for predicting viscosity of natural and hydrocarbon gases

Accurate knowledge of pure hydrocarbon and natural gas viscosity is essential for reliable reservoir characterization and simulation as well as economic design of natural gas processing and transport units. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. When there is no available experimental data for the required composition, pressure, and temperature conditions, the use of predictive methods becomes important. In this communication, a novel approach was proposed to develop for prediction of viscosity of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrogen sulfide, using over 3800 data sets. A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was proposed (CSA-LSSVM model). Moreover, comparative studies between the CSA-LSSVM model and fourteen empirical correlations were done. To this end, statistical and graphical error analyses have been used simultaneously. Obtained results illustrated that the “two-parameter” CSA-LSSVM model is more robust, reliable, and consistent than the existing correlations for the prediction of pure and natural gas viscosity. Moreover, the relevancy factor illustrated that the molecular weight of gas has the greatest impact on gas viscosity.

A simple correlation to estimate natural gas viscosity

A general investigation of the viscosity of natural gas as a function of temperature, pressure and composition was carried out to develop a generalized correlation. The model obtained was based on 3231 data points of 29 multicomponent mixtures in wide ranges of pressures (0.1–137.8 MPa), temperatures (241–473 K) and specific gravities (0.573–1.337). Correction terms for non-hydrocarbons of hydrogen sulfide, carbon dioxide and nitrogen were up to 70, 54.4 and 15.8 of mole percent, respectively. The arithmetic average of the model's absolute error was found to be 5.05%, which is acceptable in engineering calculations and has superiority over other methods in its class.

An Efficient Reliable Method for Calculation of Natural Gas Viscosity

In recent years, there has been an increase of interest in the flow of gas at relatively high pressures and temperatures. The energy losses calculations in the flow of gases in conduits, as well as through the porous media constituting natural petroleum reservoirs, requires knowledge of the viscosity of the fluid at the pressure and temperature involved. This work presents a new modified empirical correlation for calculating viscosity of natural gas mixtures based on LGE model. The empirical model derived from more than 3,800 experimental data of viscosity at different temperatures and pressures. Also the model can predict viscosity of natural gas accurately for variety ranges pseudo reduced pressure from 0.01 to 21 and pseudoreduced temperatures from 1 to 3. The accuracy of the new model is compared to mostly used empirical models The results showed that the new empirical correlation has 1% of average absolute relative deviation percent (AARD %) relative to experimental data. The comparison indicates the superiority of the new method in simplicity and low deviation relative to other methods used in this work for calculating of natural gas viscosity.

State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive models/correlations available in the literature. It has been observed that the proposed model correctly captures the physical trend of changing the natural gas viscosity as a function of the temperature and pressure. Finally, sensitivity analysis was performed to assess the effect of the gas viscosity uncertainty on the cumulative gas production for a synthetic natural gas reservoir, using a numerical reservoir simulation. Results revealed that applications of LSSVM modeling can lead to a more accurate and reliable estimation of the gas viscosity over a wide range of reservoir conditions.

Predicting Natural Gas Viscosity with a Mixture Viscosity Model for the Entire Fluid Region

A new predictive corresponding-states mixture viscosity model for the entire fluid region and large range of temperature and pressure is reported here. Using previously published parameters for pure n-alkanes, viscosity predictions with this model were made for the methane–ethane system and twelve component Qatari synthetic natural gas mixtures. For the synthetic natural gas mixtures studied, the new model presented here has comparable predictability to other literature models and superior predictability at higher (11 mol %) levels of N2 and CO2 content. The integral use of the PC-SAFT equation of state with the fluid mixture viscosity model introduced here provides an attractive unified approach for modeling flow in porous media.

Natural gas viscosity estimation through corresponding states based models

As natural gas viscosity is one of the most important parameters in natural gas engineering calculations, its accurate value determination plays a key role in its management. In this study, a comprehensive model is suggested for prediction of natural gas viscosity in a wide range of pressures (14.69–20053psia), temperatures (434–820°R), and gas specific gravity of 0.573–1.207. The new model is applicable for gases containing heptane plus and non-hydrocarbon components. It is validated by the 3255 viscosity data from 25 different gas mixtures. The average absolute error of the model was found to be 3.03% and 5.89% in the comparison with the natural gas and gas condensate viscosity data respectively, compared to existing similar methods, its results are quite satisfactory.