The Experts below are selected from a list of 77493 Experts worldwide ranked by ideXlab platform
Jamshid Moghadasi - One of the best experts on this subject based on the ideXlab platform.
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natural Gas viscosity estimation through corresponding states based models
Fluid Phase Equilibria, 2013Co-Authors: Ehsan Heidaryan, Feridun Esmaeilzadeh, Jamshid MoghadasiAbstract:Abstract 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 (14.69–20053 psia), 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.
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A NOVEL CORRELATION APPROACH FOR PREDICTION OF NATURAL Gas COMPRESSIBILITY FACTOR
Journal of Natural Gas Chemistry, 2010Co-Authors: Ehsan Heidaryan, Amir Salarabadi, Jamshid MoghadasiAbstract:Abstract Gas compressibility factor (z-Factor) is one of the most important parameters in upstream and downstream calculations of petroleum industries. The importance of z-Factor cannot be overemphasized in oil and Gas Engineering calculations. The experimental measurements, Equations of State (EoS) and empirical correlations are the most common sources of z-Factor calculations. There are more than twenty correlations available with two variables for calculating the z-Factor from fitting in an EoS or just through fitting techniques. However, these correlations are too complex, which require initial value and more complicated and longer computations or have magnitude error. The purpose of this study is to develop a new accurate correlation to rapidly estimate z-Factor. Result of this correlation is compared with large scale of database and experimental data also. Proposed correlation has 1.660 of Absolute Percent Relative Error (EABS) versus Standing and Katz chart and has also 3.221 of EABS versus experimental data. The output of this correlation can be directly assumed or be used as an initial value of other implicit correlations. This correlation is valid for Gas coefficient of isothermal compressibility (cg) calculations also.
Ehsan Heidaryan - One of the best experts on this subject based on the ideXlab platform.
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natural Gas viscosity estimation through corresponding states based models
Fluid Phase Equilibria, 2013Co-Authors: Ehsan Heidaryan, Feridun Esmaeilzadeh, Jamshid MoghadasiAbstract:Abstract 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 (14.69–20053 psia), 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.
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A NOVEL CORRELATION APPROACH FOR PREDICTION OF NATURAL Gas COMPRESSIBILITY FACTOR
Journal of Natural Gas Chemistry, 2010Co-Authors: Ehsan Heidaryan, Amir Salarabadi, Jamshid MoghadasiAbstract:Abstract Gas compressibility factor (z-Factor) is one of the most important parameters in upstream and downstream calculations of petroleum industries. The importance of z-Factor cannot be overemphasized in oil and Gas Engineering calculations. The experimental measurements, Equations of State (EoS) and empirical correlations are the most common sources of z-Factor calculations. There are more than twenty correlations available with two variables for calculating the z-Factor from fitting in an EoS or just through fitting techniques. However, these correlations are too complex, which require initial value and more complicated and longer computations or have magnitude error. The purpose of this study is to develop a new accurate correlation to rapidly estimate z-Factor. Result of this correlation is compared with large scale of database and experimental data also. Proposed correlation has 1.660 of Absolute Percent Relative Error (EABS) versus Standing and Katz chart and has also 3.221 of EABS versus experimental data. The output of this correlation can be directly assumed or be used as an initial value of other implicit correlations. This correlation is valid for Gas coefficient of isothermal compressibility (cg) calculations also.
E Elm M Shokir - One of the best experts on this subject based on the ideXlab platform.
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artificial neural networks modeling for hydrocarbon Gas viscosity and density estimation
Journal of King Saud University: Engineering Sciences, 2011Co-Authors: Abdulrahman A. Alquraishi, E Elm M ShokirAbstract:Abstract Natural Gas is a naturally occurring petroleum product and one of the major fossil energy sources. It is composed of a complex mixture of hydrocarbon compounds and a minor amount of inorganic compounds. Hydrocarbon Gas properties of viscosity and density are of great importance for Gas Engineering calculations. These properties are measured experimentally but if unavailable, they can be predicted through different correlations. This work is aimed at developing new models for Gas viscosity and Gas density using generalized regression neural (GRN) networks. A large database of experimental measurements were gathered from the literature and used to develop and test the models. The database consists of Gas composition, measured viscosity and density, temperature, pressure, and compressibility factor of different hydrocarbon Gases and pure and impure Gas mixtures containing up to pentane plus fractions and small concentrations of non-hydrocarbon components. A total of 4445 experimental measurements were used in this study constituting of 1853 pure Gases and 2592 Gas mixtures. Two neural nets were trained and tested separately to predict Gas viscosity and Gas density. Viscosity is predicted as a function of Gas density, pseudo reduced pressure, and pseudo reduced temperature while density is predicted as a function of molecular weight, pseudo reduced pressure, and pseudo reduced temperature. The two neural networks were trained and validated using a set of 800 data points chosen randomly from the collected data set. The developed networks were blind tested using a total of 3645 data points. The networks prediction was validated and their efficiencies were tested against some other correlations. The comparison indicates a better performance for the developed neural networks compared to the conventional tested correlations with an average absolute error of 3.65% and 4.93% for Gas viscosity and Gas density nets, respectively.
Amir H. Mohammadi - One of the best experts on this subject based on the ideXlab platform.
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application of wilcoxon generalized radial basis function network for prediction of natural Gas compressibility factor
Journal of The Taiwan Institute of Chemical Engineers, 2015Co-Authors: Mohammadhadi Shateri, Abdolhossein Hemmatisarapardeh, Shohreh Ghorbani, Amir H. MohammadiAbstract:Gas compressibility factor is necessary in most of chemical and petroleum Engineering calculations. Accurate and fast calculation of this property is of a vital importance in a large number of simulators used in petroleum and Gas Engineering. In this study, a large data bank (978 data points), covering a wide range of natural Gases, was collected from open literature sources. Afterwards, one of the newest and most powerful modeling approach, namely Wilcoxon generalized radial basis function network (WGRBFN) was employed to predict the compressibility factor of natural Gases. The results obtained from the proposed model were compared to those of nine empirical correlations and five equations of state. Statistical and graphical error analyses demonstrated that the developed model can satisfactorily predict the compressibility factor of natural Gases with an average absolute percent relative error of 2.3%. Moreover, it was demonstrated that the proposed model outperforms all of the studied empirical correlations and equations of state. Finally, to identify the probable outliers the Leverage approach was performed. All of the experimental data seem to be reliable except 2%. Therefore, the developed model is reliable for the prediction of natural Gas compressibility factor in its applicability domain.
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new tools predict monoethylene glycol injection rate for natural Gas hydrate inhibition
Journal of Loss Prevention in The Process Industries, 2015Co-Authors: Arash Kamari, Amir H. Mohammadi, Alireza Bahadori, Sohrab ZendehboudiAbstract:Abstract In the oil and Gas production operations, hydrates deposition leads to serious problems including over pressuring, irreparable damages to production equipment, pipeline blockage, and finally resulting in production facilities shut down and even human life and the environment dangers. Hence, it is of great importance to forecast the hydrate formation conditions in order to overcome problems associated with deposition of hydrate. In this article, an effective, mathematical and predictive strategy, known as the least squares support vector machine, is employed to determine the hydrate forming conditions of sweet natural Gases as well as the monoethylene glycol (MEG) flow-rate and desired depression of the Gas hydrate formation temperature (DHFT). The outcome of this study reveals that the developed technique offers high predictive potential in precise estimation of this important characteristic in the Gas industry. Beside the accuracy and reliability, the proposed model includes lower number of coefficients in contrast with conventional correlations/methods, implying an interesting feature to be added to the modeling simulation software packages in Gas Engineering.
Abdulrahman A. Alquraishi - One of the best experts on this subject based on the ideXlab platform.
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artificial neural networks modeling for hydrocarbon Gas viscosity and density estimation
Journal of King Saud University: Engineering Sciences, 2011Co-Authors: Abdulrahman A. Alquraishi, E Elm M ShokirAbstract:Abstract Natural Gas is a naturally occurring petroleum product and one of the major fossil energy sources. It is composed of a complex mixture of hydrocarbon compounds and a minor amount of inorganic compounds. Hydrocarbon Gas properties of viscosity and density are of great importance for Gas Engineering calculations. These properties are measured experimentally but if unavailable, they can be predicted through different correlations. This work is aimed at developing new models for Gas viscosity and Gas density using generalized regression neural (GRN) networks. A large database of experimental measurements were gathered from the literature and used to develop and test the models. The database consists of Gas composition, measured viscosity and density, temperature, pressure, and compressibility factor of different hydrocarbon Gases and pure and impure Gas mixtures containing up to pentane plus fractions and small concentrations of non-hydrocarbon components. A total of 4445 experimental measurements were used in this study constituting of 1853 pure Gases and 2592 Gas mixtures. Two neural nets were trained and tested separately to predict Gas viscosity and Gas density. Viscosity is predicted as a function of Gas density, pseudo reduced pressure, and pseudo reduced temperature while density is predicted as a function of molecular weight, pseudo reduced pressure, and pseudo reduced temperature. The two neural networks were trained and validated using a set of 800 data points chosen randomly from the collected data set. The developed networks were blind tested using a total of 3645 data points. The networks prediction was validated and their efficiencies were tested against some other correlations. The comparison indicates a better performance for the developed neural networks compared to the conventional tested correlations with an average absolute error of 3.65% and 4.93% for Gas viscosity and Gas density nets, respectively.
