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Tiberiu Harko - One of the best experts on this subject based on the ideXlab platform.
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can dark matter be a bose einstein Condensate
arXiv: Astrophysics, 2007Co-Authors: Christian G Boehmer, Tiberiu HarkoAbstract:We consider the possibility that the dark matter, which is required to explain the dynamics of the neutral hydrogen clouds at large distances from the galactic center, could be in the form of a Bose-Einstein Condensate. To study the Condensate we use the non-relativistic Gross-Pitaevskii equation. By introducing the Madelung representation of the wave function, we formulate the dynamics of the system in terms of the continuity equation and of the hydrodynamic Euler equations. Hence dark matter can be described as a non-relativistic, Newtonian Bose-Einstein gravitational Condensate Gas, whose density and pressure are related by a barotropic equation of state. In the case of a Condensate with quartic non-linearity, the equation of state is polytropic with index $n=1$. To test the validity of the model we fit the Newtonian tangential velocity equation of the model with a sample of rotation curves of low surface brightness and dwarf galaxies, respectively. We find a very good agreement between the theoretical rotation curves and the observational data for the low surface brightness galaxies. The deflection of photons passing through the dark matter halos is also analyzed, and the bending angle of light is computed. The bending angle obtained for the Bose-Einstein Condensate is larger than that predicted by standard general relativistic and dark matter models. Therefore the study of the light deflection by galaxies and the gravitational lensing could discriminate between the Bose-Einstein Condensate dark matter model and other dark matter models.
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Can dark matter be a Bose-Einstein Condensate?
Journal of Cosmology and Astroparticle Physics, 2007Co-Authors: Tiberiu HarkoAbstract:We consider the possibility that the dark matter which is required to explain the dynamics of the neutral hydrogen clouds at large distances from the galactic centre could be in the form of a Bose–Einstein Condensate. To study the Condensate we use the non-relativistic Gross–Pitaevskii equation. By introducing the Madelung representation of the wavefunction, we formulate the dynamics of the system in terms of the continuity equation and of the hydrodynamic Euler equations. Hence dark matter can be described as a non-relativistic, Newtonian Bose–Einstein gravitational Condensate Gas, whose density and pressure are related by a barotropic equation of state. In the case of a Condensate with quartic non-linearity, the equation of state is polytropic with index n = 1. In the framework of the Thomas–Fermi approximation the structure of the Newtonian gravitational Condensate is described by the Lane–Emden equation, which can be exactly solved. General relativistic configurations with quartic non-linearity are studied, by numerically integrating the structure equations. The basic parameters (mass and radius) of the Bose–Einstein Condensate dark matter halos sensitively depend on the mass of the condensed particle and of the scattering length. To test the validity of the model we fit the Newtonian tangential velocity equation of the model with a sample of rotation curves of low surface brightness and dwarf galaxies, respectively. We find a very good agreement between the theoretical rotation curves and the observational data for the low surface brightness galaxies. The deflection of photons passing through the dark matter halos is also analysed, and the bending angle of light is computed. The bending angle obtained for the Bose–Einstein Condensate is larger than that predicted by standard general relativistic and dark matter models. The angular radii of the Einstein rings are obtained in the small angle approximation. Therefore the study of the light deflection by galaxies and the gravitational lensing could discriminate between the Bose–Einstein Condensate dark matter model and other dark matter models.
Mohammad Ali Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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robust intelligent tool for estimating dew point pressure in retrograded Condensate Gas reservoirs application of particle swarm optimization
Journal of Petroleum Science and Engineering, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad Ebadi, Arash YazdanpanahAbstract:Abstract Liquid production from Gas Condensate reservoirs, which is an important economic and technical issue, depends on the thermodynamic conditions underlying the porous media. Accurately estimating the relevant parameters is an incentive for researchers to develop and propose a diversity of correlations; however, certain correlations are not sufficiently precise compared with correlations that are routinely applied to determine the dew point pressure ( P d ). Due to numerous misunderstandings in P d estimations, which are typically observed in upstream industries, great effort was expended herein to produce a high-performance method to monitor the P d . The solution was produced by creating a hybrid of two effective and robust methods, the swarm intelligence and artificial neural network (ANN) models. The proposed model was extended using precise dew point pressure data reported in previous studies; moreover, based on these data, the evolved intelligent approach and conventional schemes were compared. The statistical results show a notable performance by the smart model in determining the dew point pressure of Condensate Gas reservoirs. Based on the reliable results, which are highly accurate and effective, it can logically be inferred that implementing the proposed approach, PSO-ANN, can aid in better understanding reservoir fluid behavior through reservoir simulation scenarios.
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Evolving predictive model to determine Condensate-to-Gas ratio in retrograded Condensate Gas reservoirs
Fuel, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad Ebadi, Payam Soleimani Marghmaleki, Mohammad Mahboubi FouladiAbstract:Abstract Added values to project economy from Condensate sales and Gas deliverability loss due to Condensate blockage are the distinctive differences between Gas Condensate and dry Gas reservoirs. To estimate the added value, one needs to obtain Condensate to Gas ratio (CGR); however, this needs special pressure–volume–temperature (PVT) experimental study and field tests. In the absence of experimental studies during early period of field exploration, techniques which correlate such a parameter would be of interest for engineers. In this work, the developed model inspired from a new intelligent scheme known as “least square support vector machine (LSSVM)” to monitor Condensate Gas ratio (CGR) in retrograde Condensate Gas reservoirs. The proposed approach is conducted to the laboratorial data from Iranian oil fields and reported in literature has been implemented to mature and test this approach. The generated results from the LSSVM model were compared to the addressed real data and generated results of conventional correlation and fuzzy logic models. Making judgements between the generated outcomes of our model and the another course of action proves that the least square support vector machine model estimate Condensate Gas ratio more accurately in comparison with the conventional applied approaches. It worth mentioning that, least square support vector machine do not have any conceptual errors like as over-fitting issue while artificial neural networks suffer from many local minima solutions. Outcomes of this research could couple with the commercial production softwares for Condensate Gas reservoirs for different goals such as production optimization and facilitate design.
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evolving smart approach for determination dew point pressure through Condensate Gas reservoirs
Fuel, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad EbadiAbstract:To design Gas Condensate production planes with low uncertainty along with robust reservoir simulation, precise estimation or monitoring of dew point pressure play a crucial role. To handle successfully the addressed issue of Condensate Gas reservoirs, massive attentions have been performed previously but unfortunately fail to develop accurate approach for estimation dew point pressure. Dedicated to this fact, in current study enormous attempts have been put forth to proposed revolutionary method for determining dew point pressure in Gas Condensate reservoirs. To gain this end the new type of support vector machine method which evolved by Suykens and Vandewalle was utilized to generate robust approach to figure dew point pressure in Condensate Gas reservoir out. Also, lucrative and high precise dew point pressures reported in previous attentions were carried out to test and validate support vector machine approach. To serve better understanding of the proposed support vector machine approach, the conventional feed-forward artificial neural network and couple of genetic algorithm (GA) and fuzzy logic applied to the referred data banks and the gained solutions were contrasted with each other. According to the root mean square error (RMSE), correlation coefficient and average absolute relative deviation, the suggested support vector machine approach has acceptable reliability, integrity and robustness draw an analogy with the artificial neural network model and conventional methods. Thus, the proposed intelligent based way can be considered as an alternative model to monitor the dew point pressure of Condensate Gas reservoirs when the required real data are not accessible.
Jun Tai Shi - One of the best experts on this subject based on the ideXlab platform.
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Production forecasting of Gas Condensate well considering fluid phase behavior in the reservoir and wellbore
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Jun Tai Shi, Liang Huang, Kamy SepehrnooriAbstract:Abstract Retrograde condensation occurs when the reservoir pressure falls below the dew point pressure in Gas Condensate reservoirs. Complex fluid phase behavior in the reservoir and the wellbore makes it challenging to predict the productivity of Gas Condensate wells. To date, the Gas rate in the deliverability equation of Gas well is assumed the Gas rate at surface condition converted from that at the reservoir condition by using the volume factor. However, because of the complex fluid phase behavior in Gas Condensate wells, the Gas rate at the reservoir condition cannot be directly changed to that at surface condition by using volume factor. Hence, the development of a new analytical model to accurately calculate the productivity of Gas Condensate wells is still required and necessary. In this work, we propose a new deliverability equation of Gas Condensate wells with a consideration of fluid phase behavior in both the reservoir and the wellbore. Also, several pseudo-pressure functions for different Condensate distribution and flow models are examined systematically; these include the model before condensation, the model after condensation, but without Condensate flow, the model after condensation and with Condensate flow, and the model after re-vaporization. Two synthetic numerical simulation cases and two field case studies are performed to validate these deliverability equations for Gas Condensate wells. Results show that the phase behavior of Gas Condensate fluid in the wellbore plays a significant role in the deliverability evaluation and in the forecasting of Gas Condensate wells. If neglecting its effect on the deliverability, Gas and Condensate production rates could not be accurately predicted. The data from the proposed model have good agreement with the simulation and field production data of wells in Yakela Gas Condensate Reservoir and Yaha Gas Condensate Reservoir in China. If the conventional deliverability equation neglecting the effect of phase behavior in the wellbore was used, the predicted Gas production will be higher than the actual value; even 50% higher than the actual value at high flow rates. Through these case studies, it can be concluded that the effect of Condensate-Gas phase behavior in the wellbore cannot be ignored in the deliverability equation for Gas Condensate wells. This work can provide a more accurate method of forecasting the Gas and Condensate production for Condensate Gas reservoirs and also guide optimization of single well production rate and Gas recovery rate for Gas Condensate reservoirs.
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Research on a Computational Method for Reservoir Pressure of a Water-Drive Condensate Gas Reservoir
Petroleum Science and Technology, 2013Co-Authors: Bicheng Yan, Jun Tai ShiAbstract:Reservoir pressure of Condensate Gas reservoir is an indispensable parameter for calculation of Gas reserves, Gas well productivity evaluation, dynamic analysis, and evaluation of antiCondensate. The water-drive Condensate Gas reservoir is ranked among the most complex types during development. In the development process, reservoir pressure declines, Condensate oil dropouts, and content of water vapor in Condensate Gas increases, while edge water invades continuously, thus accurate calculation and prediction of reservoir pressure is particularly difficult and important. Calculation of retrograde Gas Condensate influenced by porous media adsorption and capillary pressure, and water vapor content in Condensate Gas tested under different pressure, according to material balance principle, material balance equation of water-drive Condensate Gas reservoir is established by consideration of absorption in porous media, capillary pressure and water vapor as well, and finally reservoir pressure at any time during p...
Mohammad Ebadi - One of the best experts on this subject based on the ideXlab platform.
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robust intelligent tool for estimating dew point pressure in retrograded Condensate Gas reservoirs application of particle swarm optimization
Journal of Petroleum Science and Engineering, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad Ebadi, Arash YazdanpanahAbstract:Abstract Liquid production from Gas Condensate reservoirs, which is an important economic and technical issue, depends on the thermodynamic conditions underlying the porous media. Accurately estimating the relevant parameters is an incentive for researchers to develop and propose a diversity of correlations; however, certain correlations are not sufficiently precise compared with correlations that are routinely applied to determine the dew point pressure ( P d ). Due to numerous misunderstandings in P d estimations, which are typically observed in upstream industries, great effort was expended herein to produce a high-performance method to monitor the P d . The solution was produced by creating a hybrid of two effective and robust methods, the swarm intelligence and artificial neural network (ANN) models. The proposed model was extended using precise dew point pressure data reported in previous studies; moreover, based on these data, the evolved intelligent approach and conventional schemes were compared. The statistical results show a notable performance by the smart model in determining the dew point pressure of Condensate Gas reservoirs. Based on the reliable results, which are highly accurate and effective, it can logically be inferred that implementing the proposed approach, PSO-ANN, can aid in better understanding reservoir fluid behavior through reservoir simulation scenarios.
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Evolving predictive model to determine Condensate-to-Gas ratio in retrograded Condensate Gas reservoirs
Fuel, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad Ebadi, Payam Soleimani Marghmaleki, Mohammad Mahboubi FouladiAbstract:Abstract Added values to project economy from Condensate sales and Gas deliverability loss due to Condensate blockage are the distinctive differences between Gas Condensate and dry Gas reservoirs. To estimate the added value, one needs to obtain Condensate to Gas ratio (CGR); however, this needs special pressure–volume–temperature (PVT) experimental study and field tests. In the absence of experimental studies during early period of field exploration, techniques which correlate such a parameter would be of interest for engineers. In this work, the developed model inspired from a new intelligent scheme known as “least square support vector machine (LSSVM)” to monitor Condensate Gas ratio (CGR) in retrograde Condensate Gas reservoirs. The proposed approach is conducted to the laboratorial data from Iranian oil fields and reported in literature has been implemented to mature and test this approach. The generated results from the LSSVM model were compared to the addressed real data and generated results of conventional correlation and fuzzy logic models. Making judgements between the generated outcomes of our model and the another course of action proves that the least square support vector machine model estimate Condensate Gas ratio more accurately in comparison with the conventional applied approaches. It worth mentioning that, least square support vector machine do not have any conceptual errors like as over-fitting issue while artificial neural networks suffer from many local minima solutions. Outcomes of this research could couple with the commercial production softwares for Condensate Gas reservoirs for different goals such as production optimization and facilitate design.
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evolving smart approach for determination dew point pressure through Condensate Gas reservoirs
Fuel, 2014Co-Authors: Mohammad Ali Ahmadi, Mohammad EbadiAbstract:To design Gas Condensate production planes with low uncertainty along with robust reservoir simulation, precise estimation or monitoring of dew point pressure play a crucial role. To handle successfully the addressed issue of Condensate Gas reservoirs, massive attentions have been performed previously but unfortunately fail to develop accurate approach for estimation dew point pressure. Dedicated to this fact, in current study enormous attempts have been put forth to proposed revolutionary method for determining dew point pressure in Gas Condensate reservoirs. To gain this end the new type of support vector machine method which evolved by Suykens and Vandewalle was utilized to generate robust approach to figure dew point pressure in Condensate Gas reservoir out. Also, lucrative and high precise dew point pressures reported in previous attentions were carried out to test and validate support vector machine approach. To serve better understanding of the proposed support vector machine approach, the conventional feed-forward artificial neural network and couple of genetic algorithm (GA) and fuzzy logic applied to the referred data banks and the gained solutions were contrasted with each other. According to the root mean square error (RMSE), correlation coefficient and average absolute relative deviation, the suggested support vector machine approach has acceptable reliability, integrity and robustness draw an analogy with the artificial neural network model and conventional methods. Thus, the proposed intelligent based way can be considered as an alternative model to monitor the dew point pressure of Condensate Gas reservoirs when the required real data are not accessible.
Kamy Sepehrnoori - One of the best experts on this subject based on the ideXlab platform.
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Production forecasting of Gas Condensate well considering fluid phase behavior in the reservoir and wellbore
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Jun Tai Shi, Liang Huang, Kamy SepehrnooriAbstract:Abstract Retrograde condensation occurs when the reservoir pressure falls below the dew point pressure in Gas Condensate reservoirs. Complex fluid phase behavior in the reservoir and the wellbore makes it challenging to predict the productivity of Gas Condensate wells. To date, the Gas rate in the deliverability equation of Gas well is assumed the Gas rate at surface condition converted from that at the reservoir condition by using the volume factor. However, because of the complex fluid phase behavior in Gas Condensate wells, the Gas rate at the reservoir condition cannot be directly changed to that at surface condition by using volume factor. Hence, the development of a new analytical model to accurately calculate the productivity of Gas Condensate wells is still required and necessary. In this work, we propose a new deliverability equation of Gas Condensate wells with a consideration of fluid phase behavior in both the reservoir and the wellbore. Also, several pseudo-pressure functions for different Condensate distribution and flow models are examined systematically; these include the model before condensation, the model after condensation, but without Condensate flow, the model after condensation and with Condensate flow, and the model after re-vaporization. Two synthetic numerical simulation cases and two field case studies are performed to validate these deliverability equations for Gas Condensate wells. Results show that the phase behavior of Gas Condensate fluid in the wellbore plays a significant role in the deliverability evaluation and in the forecasting of Gas Condensate wells. If neglecting its effect on the deliverability, Gas and Condensate production rates could not be accurately predicted. The data from the proposed model have good agreement with the simulation and field production data of wells in Yakela Gas Condensate Reservoir and Yaha Gas Condensate Reservoir in China. If the conventional deliverability equation neglecting the effect of phase behavior in the wellbore was used, the predicted Gas production will be higher than the actual value; even 50% higher than the actual value at high flow rates. Through these case studies, it can be concluded that the effect of Condensate-Gas phase behavior in the wellbore cannot be ignored in the deliverability equation for Gas Condensate wells. This work can provide a more accurate method of forecasting the Gas and Condensate production for Condensate Gas reservoirs and also guide optimization of single well production rate and Gas recovery rate for Gas Condensate reservoirs.
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a compositional wellbore reservoir simulator to model multiphase flow and temperature distribution
Journal of Petroleum Science and Engineering, 2009Co-Authors: Peyman Pourafshary, Kamy Sepehrnoori, Abdoljalil Varavei, A L PodioAbstract:Abstract Production of hydrocarbon often involves Gas and liquid (oil/water) concurrent flow in the wellbore. As a multi-phase/multi-component Gas–oil mixture flows from the reservoir to the surface, pressure, temperature, composition and liquid holdup distributions are interrelated. However, nearly all two-phase wellbore simulations are currently performed using “black oil” simulators. In this paper, a compositional-wellbore model coupled with a reservoir simulator to compute pressure and temperature distribution is presented. In this work, compositions of liquid and Gaseous phases in the wellbore can be determined by two-phase equilibrium flash calculations and by considering the slip between phases. Our simulator has the capability of predicting the temperature profile in the wellbore, which helps to predict multiphase flow physics such as liquid holdup and pressure drop more accurately. As the wellbore model is coupled with a reservoir simulator, it can be used as a tool to calculate fluid-flow compositions between reservoir and wellbore. The simulated results of our compositional model were compared to the equivalent blackoil model for pressure and temperature distribution. Although the input requirements and computing expenses are higher for compositional calculations than for blackoil, our simulations show that in some cases, such as those involving highly-volatile oil and retrograde Condensate Gas, ignoring compositional effects may lead to errors in pressure profile prediction for the wellbore.