The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process

Chen, Guang‐Ying; Fu, Kaiyun; Liang, Zhiwu; Sema, Teerawat; Li, Chen; Tontiwachwuthikul, Paitoon; Idem, Raphael

doi:10.1016/j.fuel.2014.02.034

Cited by 214 publications

(81 citation statements)

References 23 publications

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“…This approach was first used by Chen et al [85] for sensitivity analysis of models, which shows the degree and the sign of the effect of that parameter on the output values. In this approach, Equation (17) is used to calculate the relevancy factor of each of input parameters on the output estimation:…”

Section: Sensitivity Analysismentioning

confidence: 99%

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

Rostami

Kalantari-Meybodi

Karimi

et al. 2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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Abstract. Polymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R 2 ) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs.

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Section: Sensitivity Analysismentioning

confidence: 99%

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

Rostami

Kalantari-Meybodi

Karimi

et al. 2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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show abstract

“…Compared with the conventional grey model, the improved grey model has a higher forecast accuracy, as it is able to optimize the initial condition and predict both direct and iterative manners [21][22][23][24][25][26]. The nonhomogeneous discrete grey model can better capture nonhomogeneous effects on the data [27].…”

Section: Literature Reviewmentioning

confidence: 99%

“…BP network is a feed forward neural network realized by a back propagation algorithm [21]. It a non-linear prediction model, achieving a stable prediction effect through determining the combining weights [40,41].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

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Better prediction of energy demand is of vital importance for developing countries to develop effective energy strategies to improve energy security, partly because those countries' energy demands are increasing rapidly. In this work, metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) are adopted to forecast energy demand in India, the third largest energy consumer in the world after China and the USA. The average relative errors between the actual and simulated value are 1.31% (MGM), 1.07%, 0.92% (MGM-ARIMA), and 0.39% (BP). The high prediction accuracy indicates that the prediction result is effective. The result shows that India's energy consumption will increase by 4.75% a year in the next 14 years. Compared with the 5.1% per year on average in 1995-2016, India's energy consumption will still continue its steady growth at about 5% growth from 2017 to 2030.

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“…(2), a sensitivity analysis was performed. Hence, the relevancy factor (r) [52] is utilized in this study for measuring the degree of effect of each reservoir fluid property applied in Eq. (2) for the determination of solution GOR.…”

Section: Variables Relevancy Analysismentioning

confidence: 99%

Rigorous Modeling of Solution Gas–Oil Ratios for a Wide Ranges of Reservoir Fluid Properties

Kamari¹,

Zendehboudi²,

Sheng³

et al. 2016

J Pet Environ Biotechnol

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The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process

Cited by 214 publications

References 23 publications

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Rigorous Modeling of Solution Gas–Oil Ratios for a Wide Ranges of Reservoir Fluid Properties

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