TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractSeveral authors have shown the applicability of modified black oil (MBO) approach for modeling gas condensate and volatile oil reservoirs. It was shown before that MBO could adequately replace compositional simulation in many applications. In this work, a new set of MBO PVT correlations was developed. The four PVT functions (oil-gas ratio, R v , solution gas-oil ratio, R s , oil formation volume factor, B o , and gas formation volume factor, B g ) were investigated. According to our knowledge, no other correlation for calculating oil-gas ratio exists in the petroleum literature. Alternatively, oil-gas ratio (needed for material balance and reservoir simulation calculations of gas condensate and volatile oil reservoirs) had to be generated from a combination of laboratory experiments and elaborate calculation procedures using EOS models. In previous work, we found that Whitson and Torp method for generating Modified Black Oil (MBO) PVT properties yielded best results when compared with compositional simulation. This method (and the others available in the literature such as Coats' and Walsh's) requires the use of data from PVT laboratory experiments and proper construction of EOS models. We used Whitson and Torp's method to generate our database of the MBO PVT curves used in developing our correlations after matching the PVT experimental results with an EOS model. For each one of the four PVT parameters, we used 1850 values obtained from PVT analysis of eight gas condensate fluid samples and 1180 values obtained from PVT analysis of five volatile oil fluid samples. The samples were selected to cover a wide range of fluid composition, condensate yield, reservoir temperature, and pressure. The data points were generated by extracting the PVT properties of each sample at six different separator conditions. We then used multi-variable regression techniques to calculate our correlation constants.The new correlations were validated using the generalized material balance equation calculations with data generated from a compositional reservoir simulator. These new correlations depend only on readily available parameters in the field and can have wide applications when representative fluid samples are not available.

Artificial neural networks theory creates, with other theories and algorithms, a new science. This science deals with the human body as an excellent source, through which it can simulate some biological basics and systems, to be used in solving many scientific, and engineering problems. Neural networks are tested successfully in so many fields as pattern recognition or intelligent classifier, prediction, and correlation development. Recently, Neural network has gained popularity in petroleum applications. In this paper we applied this technique in PVT parameters determinations. The application interests in the estimation of the bubble point pressure through a designed neural network. As this value well estimated, it then used with other variables in a second network to determine oil FVF at this value of bubble point pressure. A comparison study between the performance of neural network and other published correlations has shown an excellent response with smallest absolute relative average error, and highest correlation coefficient for the designed networks among all correlations. Introduction PVT properties always determined experimentally from calculation based on samples collected from either well bore or at the surface. Such samples may be very expensive to obtain. Hence, in case of the absence of the experimental measured PVT properties, it is necessary to use the empirically derived correlation to predict the PVT data.1 Many correlation already exist in the oil and gas industry such as: Standing correlation, Glaso correlation, Beggs and Vasquez correlation,....etc. Many investigators recognize that the neural network can serve the petroleum industry to create more accurate predication PVT correlations. So, there is a number of papers in this area. R. B. Gharbi, and A. M. El- Sharkawy2,3, in 1997, published two papers in this field. The first paper use the neural system to estimate the PVT data for middle east crude oil reservoirs28, while the next one was interest in developing a universal neural network for predicting PVT properties for any oil reservoir3.

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractSeveral authors have shown the applicability of modified black oil (MBO) approach for modeling gas condensate and volatile oil reservoirs. It was shown before that MBO could adequately replace compositional simulation in many applications. In this work, a new set of MBO PVT correlations was developed. The four PVT functions (oil-gas ratio, R v , solution gas-oil ratio, R s , oil formation volume factor, B o , and gas formation volume factor, B g ) were investigated. According to our knowledge, no other correlation for calculating oil-gas ratio exists in the petroleum literature. Alternatively, oil-gas ratio (needed for material balance and reservoir simulation calculations of gas condensate and volatile oil reservoirs) had to be generated from a combination of laboratory experiments and elaborate calculation procedures using EOS models. In previous work, we found that Whitson and Torp method for generating Modified Black Oil (MBO) PVT properties yielded best results when compared with compositional simulation. This method (and the others available in the literature such as Coats' and Walsh's) requires the use of data from PVT laboratory experiments and proper construction of EOS models. We used Whitson and Torp's method to generate our database of the MBO PVT curves used in developing our correlations after matching the PVT experimental results with an EOS model. For each one of the four PVT parameters, we used 1850 values obtained from PVT analysis of eight gas condensate fluid samples and 1180 values obtained from PVT analysis of five volatile oil fluid samples. The samples were selected to cover a wide range of fluid composition, condensate yield, reservoir temperature, and pressure. The data points were generated by extracting the PVT properties of each sample at six different separator conditions. We then used multi-variable regression techniques to calculate our correlation constants.The new correlations were validated using the generalized material balance equation calculations with data generated from a compositional reservoir simulator. These new correlations depend only on readily available parameters in the field and can have wide applications when representative fluid samples are not available.

Nowadays, the devastating effects of the pollutants produced by gasoline are known well. As a result, scientists are looking for a better formula to replace the gasoline currently in use. Using different additives has been one of the strategies developed throughout the years. However, because certain compounds damage the environment and human life, researchers must now choose which additives to use. The primary goal of this work is to test a gasoline combination with nano-additives Ag2O and MnO2 in a 4-stroke vehicle engine (Fiat 128) and to investigate the influence of novel mixes on the efficiency of combustion rates and the amount of target pollutant gas released (CO, NOx, and the exhaust temperature). The tests were carried out at three different engine speeds: 2000, 2500, and 2900 rpm. At the end of the test, the 0.05% concentration of Ag2O nano-additive was chosen as the best sample, which increases engine performance in gasoline combustion rates and minimizes harmful gas emissions. Furthermore, CO and NOx emissions were lowered by 52% and 35%, respectively, according to EURO 6, indicating a considerable reduction in mortality rates and costs. Finally, a new mechanism was observed using Ag2O nanoparticles, leading to a reduction in CO and CO2 at the same time.