جستجوی مقالات مرتبط با کلیدواژه
تکرار جستجوی کلیدواژه algorithm در نشریات گروه فنی و مهندسی
algorithm
در نشریات گروه مهندسی شیمی، نفت و پلیمر
تکرار جستجوی کلیدواژه algorithm در مقالات مجلات علمی
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The static Young’s modulus (Esta) and the uniaxial compressive strength (UCS) are key parameters in the geomechanical study of hydrocarbon reservoirs. These parameters are typically estimated using empirical models that relate the strength and elastic parameters of the rock to their petrophysical properties. In the present research, the existing empirical models in the literature (specifically for carbonate rocks) were compiled and investigated, this was followed by performing experimental tests on 27 core samples to measure the porosity (n), the density (ρ), the compressive and shear wave velocities (Vp and Vs, respectively), Esta and UCS for a carbonate gas reservoir in the south of Iran. Next, particle swarm optimization (PSO) and regression analysis (RA) were conducted to develop estimator models for the Esta and UCS. Results of this study showed that the best models produced by the PSO algorithm were more accurate than not only the best models produced by the RA, but also the models proposed by previous researchers by 7% and 48%, respectively, for the Esta and by 10% and 7%, respectively, for the UCS. On this basis, it was strongly recommended to apply the empirical correlations developed through the PSO for more accurate estimation of the studied parameters across the investigated field and similar fields.Keywords: Young’S, Modulus, UCS, Carbonate, Reservoir, PSO, Algorithm
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A total of 1099 data points consisting of alcohol-alcohol, alcohol-alkane, alkane-alkane, alcohol-amine and acid-acid binary solutions were collected from scientific literature to develop an appropriate artificial neural network (ANN) model. Temperature, molecular weight of the pure components, mole fraction of one component and the structural groups of the components were used as input parameters of the network while the refractive index was selected as its output. The ANN was optimized once by genetic algorithm (GA) and once again by particle swarm optimization algorithm (PSO) in order to predict the refractive index of binary solutions. The optimal topology of the ANN-GA consisted of 13 neurons in the hidden layer and the optimal topology of the ANN-PSO consisted of 16 neurons in the hidden layer. The results revealed that the ANN optimized by PSO had a better accuracy (MSE=0.003441 for test data) compared to the ANN optimized with GA (MSE=0.005117 for test data).Keywords: Algorithm, Artificial Neural Network, Binary Liquid Mixture, Genetic Multi-Layer Perceptron, Particle Swarm Optimization, Refractive Index
نکته
- نتایج بر اساس تاریخ انتشار مرتب شدهاند.
- کلیدواژه مورد نظر شما تنها در فیلد کلیدواژگان مقالات جستجو شدهاست. به منظور حذف نتایج غیر مرتبط، جستجو تنها در مقالات مجلاتی انجام شده که با مجله ماخذ هم موضوع هستند.
- در صورتی که میخواهید جستجو را در همه موضوعات و با شرایط دیگر تکرار کنید به صفحه جستجوی پیشرفته مجلات مراجعه کنید.