Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis

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

Kalantari-Meybodi

Karimi

et al. 2018

Self Cite

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.

Section: +mentioning

confidence: 99%

Section: +mentioning

confidence: 99%

Section: +mentioning

confidence: 99%

“…x) is the kernel function that is the product of g (x) and g(x k ) in feasible margin as it is provided as follows [30,45]:…”

Section: +mentioning

confidence: 99%

See 2 more Smart Citations

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

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

Kalantari-Meybodi

Karimi

et al. 2018

Self Cite

“…Additionally, numerous researchers described performance of the choke in both subcritical and critical conditions by using analytical models . Such theoretical models usually use several assumptions for the aim of simplification; therefore, application of these methods may result in considerable errors for broader operational conditions …”

Section: Introductionmentioning

confidence: 99%

Toward gene expression programming for accurate prognostication of the critical oil flow rate through the choke: correlation development

Asia-Pacific J Chem Eng

Ebadi

2017

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In this study, gene expression programming (GEP) was employed to develop a new equation for calculating choke flow rate as a function of fluid properties and wellhead choke characteristics. For this reason, a comprehensive databank was adopted from the petroleum industry, and then, the databank was split into three groups of training set for model construction (70% of database), test set for model evaluation (15% of database), and validation set for avoiding over‐fitting problem (15% of database). For assessing of GEP model, numerous statistical and graphical tools were applied, and then, it was compared with widely applied literature correlations. Consequently, the proposed tool in this study gives the best fit with the actual flow rate data with the average absolute relative deviation of 11.0872% and determination coefficient (R2) of 0.9695. Additionally, cumulative frequency plot demonstrates that the GEP model has the highest frequency over the database. The outcomes of the GEP model were also compared with the outputs of the artificial neural network (ANN) revealing the higher precision of the ANN than the proposed GEP model in this study. At the end, it is worthwhile that the suggested model can be used as reliable tool for choke performance modeling. © 2017 Curtin University and John Wiley & Sons, Ltd.

On modeling of bitumen/n‐tetradecane mixture viscosity: Application in solvent‐assisted recovery method

Asia-Pacific J Chem Eng

Arabloo

Esmaeilzadeh

et al. 2017

Self Cite

Extremely high viscosity of bitumen is of great challenge in bitumen production, transportation, and processing. Therefore, different techniques such as dilution of bitumen by liquid solvents have been proposed for bitumen viscosity reduction. Accurate prediction of diluted bitumen mixture viscosity is a key parameter in solvent injection during hydrocarbon recovery. In this study, a simple correlation and a model in accordance with adaptive neuro-fuzzy inference systems joined with particle swarm optimization (ANFIS-PSO) algorithm have been developed for accurate estimation of bitumen/n-tetradecane mixture viscosity in comparison to other available mixing rules at different pressures, temperatures, and n-tetradecane mass fractions. For evaluation of the models efficiency, cumulative frequency test was employed to examine the data frequency over the whole databank. The ANFIS-PSO model exhibits the best performance with average absolute relative deviation of 2.44% and squared correlation coefficient (R 2 ) of 0.9985. Moreover, it is proved that the suggested correlation has the best practical performance than the Lederer and other studied correlations in this work. In other words, the proposed correlation is easy-to-use and also, it is a more straightforward function of pressure, temperature, and solvent mass fraction than other literature correlations. Based on the sensitivity analysis, it is demonstrated that solvent concentration is the most influencing parameter in reducing bitumen viscosity. In accordance to the results of this study, the proposed techniques can serve as robust and reliable tools for efficient estimation of bitumen/n-tetradecane mixture viscosity.