Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((Ba-133-Cs-137), (Ba-133-Co-60), ((AmCs)-Am-241-Cs-137), (Am-241-Co-60), (...
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Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((Ba-133-Cs-137), (Ba-133-Co-60), ((AmCs)-Am-241-Cs-137), (Am-241-Co-60), (Ba-133-Am-241) and ((CoCs)-Co-60-Cs-137)) were investigated in order to optimize the three-phase flow meter. Three phases were water, oil and gas and the regime was considered annular. The required data was numerically generated using mcnp-x code which is a Monte-Carlo code. Indeed, the present study devotes to forecast the volume fractions in the annular three-phase flow, based on a multi energy metering system including various radiation sources and also one NaI detector, using a hybrid model of artificial neural network and Jaya Optimization algorithm. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must forecast only two volume fractions. Six hybrid models associated with the number of used radiation sources are designed. The models are employed to forecast the gas and water volume fractions. The next step is to train the hybrid models based on numerically obtained data. The results show that, the best forecast results are obtained for the gas and water volume fractions of the system including the (Am-241-Cs-137) as the radiation source.
Scale can be defined as chemical compounds that are inorganic, initially insoluble, and precipitate accumulating on the internal walls of pipes, surface equipment, and/or parts of components involved in the production...
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Scale can be defined as chemical compounds that are inorganic, initially insoluble, and precipitate accumulating on the internal walls of pipes, surface equipment, and/or parts of components involved in the production and transport of oil. These compounds, when precipitating, cause problems in the oil industry and consequently result in losses in the optimization of the extraction process. Despite the importance and impact of the precipitation of these compounds in the technological and economic scope, there remains difficulty in determining the methods that enable the identification and quantification of the scale at an initial stage. The use of gamma transmission technique may provide support for a better understanding of the deposition of these compounds, making it a suitable tool for the noninvasive determination of their deposition in oil transport pipelines. The geometry used for the scale detection includes a 280-mm diameter steel tube containing barium sulphate (BaSO4) scale ranging from 0.5 to 6 cm, a gamma radiation source with divergent beam, and a NaI(Tl) 2 x 2 '' scintillation detector. The opening size of the collimated beam was also evaluated (2-7 mm) to quantify the associated error in calculating the scale. The study was done with computer simulation, using the mcnp-x code, and the results were validated using analytical equations. Data obtained by the simulation were used to train an artificial neural network (ANN), thereby making the study system more complex and closer to the real one. The input data provided for the training, testing, and validation of the network consisted of pipes with 4 different internal diameters (D1, D2, D3, and D4) and 14 different scale thicknesses (0.5 to 7 cm, with steps of 0.5 cm). The network presented generalization capacity and good convergence, with 70% of cases with less than 10% relative error and a linear correlation coefficient of 0.994, which indicates the possibility of using this study for this purpose.
This work presents a new methodology for density prediction of petroleum and derivatives for products' monitoring application. The approach is based on pulse height distribution pattern recognition by means of an ...
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This work presents a new methodology for density prediction of petroleum and derivatives for products' monitoring application. The approach is based on pulse height distribution pattern recognition by means of an artificial neural network (ANN). The detection system uses appropriate broad beam geometry, comprised of a Cs-137 gamma-ray source and a NaI(Tl) detector diametrically positioned on the other side of the pipe in order measure the transmitted beam. Theoretical models for different materials have been developed using mcnp-x code, which was also used to provide training, test and validation data for the ANN. 88 simulations have been carried out, with density ranging from 0.55 to 1.26 g cm(-3) in order to cover the most practical situations. Validation tests have included different patterns from those used in the ANN training phase. The results show that the proposed approach may be successfully applied for prediction of density for these types of materials. The density can be automatically predicted without a prior knowledge of the actual material composition. (C) 2016 Elsevier Ltd. All rights reserved.
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