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

Yousefi, Seyed Hamidreza; Azamifard, Arash; Hosseini, S H Sadat; Shamsoddini, Mohammad-Javad; Alizadeh, Nasser

doi:10.1016/j.jngse.2014.06.003

Cited by 9 publications

(1 citation statement)

References 37 publications

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“…They predicted the viscosity of natural gas in a wide range of temperatures (up to 444 K), pressures (up to 138 MPa), and compositions, which helps during the design process of surface facilities and the management of natural gas [103]. Major effects of the molecular weight of gas on viscosity compared to pressure and temperature were also observed by Yousefi et al [104], who developed a model that predicted the viscosity of natural gas. Another critical parameter that affects natural-gas transportation in the pipeline is the dew point, which causes condensation and pressure drop, thereby affecting transportation efficiency.…”

Section: Volume Fraction (%)mentioning

confidence: 82%

Natural Gas Engine Technologies: Challenges and Energy Sustainability Issue

2018

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Energy sustainability and environmental issues pose greater challenges on different primary energy sectors as the global energy demand increases and it is projected to further increase with an upsurge in population. On the other hand, energy sources from conventional fossil-based fuels are depleting, forcing explorations in challenging and difficult locations. As a result, the use of alternative fuels received dramatic consideration to substitute these conventional fuels, of which natural gas took the significant share. However, the share of natural-gas vehicles in the current vehicle market is quite small, and it is estimated to be below 5%. This paper reviews the current resource scenarios including proven and potential reserves, current production, and consumption, along with the fueling infrastructure, distribution, and storage. It also provides summary of the development of fuel-injection technologies aimed to enhance the performance of gas engines. More attention was also given to natural-gas engines and their limitations. Parameters affecting the performance and combustion of compressed natural gas (CNG) in spark-ignition (SI) engines are thoroughly assessed, among which compression ratio and injection timing play major roles in the optimization of CNG-fueled engines. Furthermore, different technologies that help close the performance gap between conventional liquid-fuel and natural-gas engines and future directions of the research are presented.

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Section: Volume Fraction (%)mentioning

confidence: 82%

Natural Gas Engine Technologies: Challenges and Energy Sustainability Issue

2018

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Hybrid approach for gas viscosity in Yemeni oil fields

Al-Gathe,

Baarimah,

Al-Khudafi

et al. 2023

Earth Sci Inform

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The estimation of a gas viscosity experimentally is often difficult. So, accurate determination of gas viscosity has been the main challenge in a gas reservoir development. There are many correlations to estimate this property. Often time, the results of these correlations do not agree with experimental data, thereby causing a considerable amount of error. The difficulty of these correlations can be propagated simply by tuning against some experimental data using artificial intelligent model. Currently, the achievements of artificial neural networks (ANN) techniques alone to predict gas viscosity open the door to use the hybrid system. In this model, the Particle Swarm Optimization (PSO) algorithm is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 868 data points obtained from the laboratory measurements of gas viscosity were used. The data include measured gas viscosity, specific gas gravity, temperature, pressure, molecular weight, pseudo-critical temperature and pressure and non-hydrocarbon components (H2S, CO2, and N2). The performance of the PSONN model is compared with performance of ANN and other empirical model to show the most general and accurate model for predicting gas viscosity. From the results of this study, we found that the PSONN model is more reliable and accurate with the absolute present relative (APRE) error and mean square error (RMS) of 2.76 and 5.49 respectively.

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