Background Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.Purpose The current research attempts to indicate the ability of adaptive network-bas...Background Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.Purpose The current research attempts to indicate the ability of adaptive network-based fuzzy inference system(ANFIS)to forecast the volume fractions in a water-oil-gas three-phase flow system.Method The current investigation devotes to measure the volume fractions in the stratified three-phase flow,on the basis of a dual-energy metering system consisting of the 152Eu and 137Cs and one NaI detector using ANFIS.The summation of volume fractions is equal to 100%and is also a constant,and this is enough for the ANFIS just to forecast two volume fractions.In the paper,three ANFIS models are employed.The first network is applied to forecast the oil and water volume fractions.The next to forecast the water and gas volume fractions,and the last to forecast the gas and oil volume fractions.For the next step,ANFIS networks are trained based on numerical simulation data from MCNP-X code.Results The accuracy of the nets is evaluated through the calculation of average testing error.The average errors are then compared.The model in which predictions has the most consistency with the numerical simulation results is selected as the most accurate predictor model.Based on the results,the best ANFIS net forecasts the water and gas volume fractions with the mean error of less than 0.8%.Conclusion The proposed methodology indicates that ANFIS can precisely forecast the volume fractions in a water-oil-gas three-phase flow system.展开更多
Purpose Gamma spectrum of a cement sample is not straightforward to analyze as a result of peak overlapping produced by Compton effect of gamma rays radiated from activated elements in the neutron activation process a...Purpose Gamma spectrum of a cement sample is not straightforward to analyze as a result of peak overlapping produced by Compton effect of gamma rays radiated from activated elements in the neutron activation process and also because of the change into the neutron energy spectrum in the target sample during activation.Methods Artificial neural network(ANN)is an excellent solution for complex and nonlinear systems.Consequently,the use of ANN for quantitative analysis of major cement elements including Ca,Si,Al,and Fe would be very advantageous.Iranian inertial electrostatic confinement fusion device is a fast,monoenergetic and steady neutron generator.It was simulated as a high-energy neutron source for performing neutron activation analysis.In the present study,a library of 29 members of delayed gamma-ray spectra of knowing cement samples were generated via MCNPX version 2.7.Specific photo-peaks related to Ca,Si,Al and Fe obtained from these spectra were used as inputs for ANN(21 of them for training and 8 for testing).Then,using MLP architecture,an ANN model has been presented to model the system to predict the percentages of the elements in cement.The ANN model is optimized to have only a hidden layer with five neurons.Results The comparison between modeling data and results of proposed MLP network shows that there is a good consistency between them.The MAE of training set for Ca,Si and Fe outputs were 0.0351,0.0656 and 0.0660,respectively,and the MAE of testing set for mentioned outputs were 0.0997,0.3046 and 0.8699,respectively.Conclusion Overall,it can be concluded that the defined dispensable errors show that this modeling is a good one as a prediction tool for the mentioned purpose.展开更多
文摘Background Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.Purpose The current research attempts to indicate the ability of adaptive network-based fuzzy inference system(ANFIS)to forecast the volume fractions in a water-oil-gas three-phase flow system.Method The current investigation devotes to measure the volume fractions in the stratified three-phase flow,on the basis of a dual-energy metering system consisting of the 152Eu and 137Cs and one NaI detector using ANFIS.The summation of volume fractions is equal to 100%and is also a constant,and this is enough for the ANFIS just to forecast two volume fractions.In the paper,three ANFIS models are employed.The first network is applied to forecast the oil and water volume fractions.The next to forecast the water and gas volume fractions,and the last to forecast the gas and oil volume fractions.For the next step,ANFIS networks are trained based on numerical simulation data from MCNP-X code.Results The accuracy of the nets is evaluated through the calculation of average testing error.The average errors are then compared.The model in which predictions has the most consistency with the numerical simulation results is selected as the most accurate predictor model.Based on the results,the best ANFIS net forecasts the water and gas volume fractions with the mean error of less than 0.8%.Conclusion The proposed methodology indicates that ANFIS can precisely forecast the volume fractions in a water-oil-gas three-phase flow system.
文摘Purpose Gamma spectrum of a cement sample is not straightforward to analyze as a result of peak overlapping produced by Compton effect of gamma rays radiated from activated elements in the neutron activation process and also because of the change into the neutron energy spectrum in the target sample during activation.Methods Artificial neural network(ANN)is an excellent solution for complex and nonlinear systems.Consequently,the use of ANN for quantitative analysis of major cement elements including Ca,Si,Al,and Fe would be very advantageous.Iranian inertial electrostatic confinement fusion device is a fast,monoenergetic and steady neutron generator.It was simulated as a high-energy neutron source for performing neutron activation analysis.In the present study,a library of 29 members of delayed gamma-ray spectra of knowing cement samples were generated via MCNPX version 2.7.Specific photo-peaks related to Ca,Si,Al and Fe obtained from these spectra were used as inputs for ANN(21 of them for training and 8 for testing).Then,using MLP architecture,an ANN model has been presented to model the system to predict the percentages of the elements in cement.The ANN model is optimized to have only a hidden layer with five neurons.Results The comparison between modeling data and results of proposed MLP network shows that there is a good consistency between them.The MAE of training set for Ca,Si and Fe outputs were 0.0351,0.0656 and 0.0660,respectively,and the MAE of testing set for mentioned outputs were 0.0997,0.3046 and 0.8699,respectively.Conclusion Overall,it can be concluded that the defined dispensable errors show that this modeling is a good one as a prediction tool for the mentioned purpose.
