The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform

Hossei Jalalifa - One of the best experts on this subject based on the ideXlab platform.

  • a new empirical correlation for estimating bubble point oil Formation Volume Factor
    Journal of Natural Gas Science and Engineering, 2014
    Co-Authors: Masoud Karimnezhad, Mohammad Heidaria, Mosayyeb Kamari, Hossei Jalalifa
    Abstract:

    Abstract To determine the bubble point oil Formation Volume Factor ( B ob ), which is one of the most important PVT properties, several correlations have been proposed for different regions. None of the correlations could be applied as a universal correlation due to regional changes in crude oil compositions and properties. In this paper, a new correlation is proposed to predict the B ob for Middle East crudes. Genetic Algorithm (GA) was used as the dominant tool for development of the new correlation. A total of 429 data sets of different crude oils from Middle East reservoirs were used. These Data include B ob and conventional PVT properties. Among those, 286 data sets as training data and 143 data sets as test data were randomly selected for constructing the correlation and for correlation validation, respectively. The measured mean squared errors (MSEs) of predicted B ob from the correlation in the test data were 0.0029 and the correlation coefficient ( R 2 ) between predicted values from the model and experimental values in the test data was 0.9646. These results show a very good agreement with experimental data and are more accurate for Middle East crudes than those of all existing empirical correlations.

Aref Hashemi Fath - One of the best experts on this subject based on the ideXlab platform.

  • application of radial basis function neural networks in bubble point oil Formation Volume Factor prediction for petroleum systems
    Fluid Phase Equilibria, 2017
    Co-Authors: Aref Hashemi Fath
    Abstract:

    Abstract This paper presents a powerful and comprehensive predictive model based on radial basis function (RBF) neural networks to predict the bubble point oil Formation Volume Factor (FVF), which is one of the most important pressure–Volume–temperature properties of crude oils. For this purpose, a large reliable data bank covering a wide range of various crude oil samples was used, with the data collected from the open literature. The performance of the proposed model for the prediction of the bubble point oil FVF was evaluated, using statistical and graphical error analyses, against a number of well-known predictive empirical correlations. The results indicated that, the developed RBF model is able to provide a strong agreement between the predicted values and corresponding experimental data, with an average absolute percent relative error and a coefficient of determination of 1.4562% and 0.9887, respectively, making it more accurate and reliable than the published empirical correlations. In addition, the leverage approach showed that the developed model was statistically acceptable and valid, and only six data points may be considered as probable outliers.

Masoud Karimnezhad - One of the best experts on this subject based on the ideXlab platform.

  • a new empirical correlation for estimating bubble point oil Formation Volume Factor
    Journal of Natural Gas Science and Engineering, 2014
    Co-Authors: Masoud Karimnezhad, Mohammad Heidaria, Mosayyeb Kamari, Hossei Jalalifa
    Abstract:

    Abstract To determine the bubble point oil Formation Volume Factor ( B ob ), which is one of the most important PVT properties, several correlations have been proposed for different regions. None of the correlations could be applied as a universal correlation due to regional changes in crude oil compositions and properties. In this paper, a new correlation is proposed to predict the B ob for Middle East crudes. Genetic Algorithm (GA) was used as the dominant tool for development of the new correlation. A total of 429 data sets of different crude oils from Middle East reservoirs were used. These Data include B ob and conventional PVT properties. Among those, 286 data sets as training data and 143 data sets as test data were randomly selected for constructing the correlation and for correlation validation, respectively. The measured mean squared errors (MSEs) of predicted B ob from the correlation in the test data were 0.0029 and the correlation coefficient ( R 2 ) between predicted values from the model and experimental values in the test data was 0.9646. These results show a very good agreement with experimental data and are more accurate for Middle East crudes than those of all existing empirical correlations.

B. Moradi - One of the best experts on this subject based on the ideXlab platform.

  • new oil Formation Volume Factor empirical correlation for middle east crude oils
    2013
    Co-Authors: B. Moradi, Esmaiel Malekzadeh, Mohammad Amin Shoushtari, Parisa Moradi, Islamic Azad, Universityomidieh Branch
    Abstract:

    A b s t r a c t Oil Formation Volume Factor is one of important data in the design of various stages of oilfield operations by both reservoir and production engineers. In this study, the new correlation has been developed to estimate oil Formation Volume Factor based on 581 set data points of Middle East crude oils; ranging between 19.4 to 52 oAPI. The new correlation has been compared with existing ones and results show that the new correlation predicts oil Formation Volume Factor much better with the average absolute percent relative error of 1.13% for Middle East crude oils.

  • Development of New Correlations for Predicting Bubble Point Pressure and Bubble Point Oil Formation Volume Factor of Malaysian Crude Oils
    2011
    Co-Authors: B. Moradi, Seyed Javad Hosseini, Birol Demiral, Madjid Amani
    Abstract:

    One of the most crucial parts of the input data in petroleum engineering calculations is fluid properties data. From the exploration stage, these properties should be determined either by laboratory experiments or using some empirical correlations. Although, no one can underestimate the accuracy of the experimental results but these results are highly tied to the quality of the sample taken from the reservoir fluid and also, the condition of the reservoir can affect the quality of the sample. In addition, sometimes laboratory data is not available or maybe for double checking and comparison purposes, we need another source of dataset rather than experimental data. In this situation, empirical correlations can be a relatively reliable alternative. These correlations can predict physical properties of reservoir fluid under a wide range of pressure and temperature1. Among the properties of the reservoir fluids, Bubble point pressure (Pb) and oil Formation Volume Factor (Bo) at Pb ,are essential in reservoir engineering calculations, since in improved oil recovery(IOR), if the reservoir pressure reaches to the Pb , the gas will start to evolve in the reservoir and due to the gas bubbles, the oil relative permeability will drastically decrease. Also, estimating Bo at Pb is quite challenging because this point is a inflection point in the curve of Bo vs. pressure and Bo is in its maximum value at Pb .So, it is very important to correctly predict it at Pb 2 . In this study, the new correlations has been developed to estimate bubble point pressure and oil Formation Volume Factor of Malaysian crude oils. This correlation is applicable for crude oils of ranging between 26 to 54 oAPI. The comparison of this new correlation with other published ones shows that it is much more accurate than the other ones.

  • oil Formation Volume Factor correlation for middle east crude oils
    1st International Petroleum Conference and Exhibition Shiraz 2009, 2009
    Co-Authors: B. Moradi, E Malekzadrh, Riyaz Kharrat
    Abstract:

    Oil Formation Volume Factor (OFVF) is an important reservoir fluid property. Ideally, OFVF is determined experimentally in the laboratory; However, this value is not always available and correlations are consequently used to determine it. At this work, first the published OFVF are reviewed and then a new correlation is developed to estimate OFVF of crude oils in Middle East. This correlation is applicable for crude oils of API ranging between 19 to 49. The comparison of this new correlation with other published ones shows that it is much more accurate then the other ones. The absolute average deviation error (percent) of the new correlation is about 1.13%.

Amir Ebrahimian - One of the best experts on this subject based on the ideXlab platform.

  • estimating the bubble point pressure and Formation Volume Factor of oil using artificial neural networks
    Chemical Engineering & Technology, 2008
    Co-Authors: Hanieh Rasouli, Fariborz Rashidi, Amir Ebrahimian
    Abstract:

    The phase performance of hydrocarbons is a very complicated behavior that hydrocarbons show at the time of phase change or when they remain in a particular phase. Process design is almost impossible without a good understanding of this behavior. Artificial Neural Networks have been widely utilized for engineering applications during the last two decades. Two models are presented for the prediction of the bubble point pressure and the oil Formation Volume Factor for hydrocarbon mixtures using the Artificial Neural Networks (ANNs) approach. For this purpose, five-layer neural networks were designed and trained using 106 experimental data points. After the training step, 9 experimental data points were also used for the model evaluation step and as a reliability check. The output of the models for both the training and predicted data are compared with the empirical equations of Standing, Glaso and Marhoun. It is concluded that the ANNs approach has an excellent capability for these purposes compared to the conventional methods.