Implementation of SVM framework to estimate PVT properties of reservoir oil

Rafiee-Taghanaki, Shahin; Arabloo, Milad; Chamkalani, Ali; Amani, Mahmood; Zargari, Mohammad Hadi; Adelzadeh, Mohammad Reza

doi:10.1016/j.fluid.2013.02.012

Cited by 138 publications

(45 citation statements)

References 37 publications

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“…The chief advantages of SVM-based schemes over classical algorithms are fewer adjustable parameters, less probable over-fitting problems, no earlier requirement for determination of the network topology, satisfactory generalization performance, and no needs for selection of the hidden nodes quantity [32]. In last years, several investigators have utilized the SVM-based algorithms in a wide range of petroleum and chemical engineering as a robust tool successfully [30,[33][34][35][36][37]. In addition to the above-mentioned SVM framework modeling, Adaptive Neuro Fuzzy Inference System (ANFIS) is another strong approach for precise estimation of different industrial and engineering goals.…”

Section: +mentioning

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: +mentioning

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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“…More recently, algorithms such as artificial neural network (ANN) and support vector machine (SVM) have been employed in petroleum engineering for modeling the complex and nonlinear phenomena which occur in well-test analysis [45], well-log interpretation [46][47][48], reservoir characterization [49], PVT [50][51][52] and permeability studies for crude oils [53]. In 2003, a threelayer back propagation artificial neural network (BPNN) model was adopted by Huang et al [54] to accurately predict MMP for both pure and impure CO 2 injection cases.…”

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confidence: 99%

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Bian

Han

et al. 2016

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“…The main parameter for determination of 38 the possibilities to EOR by e.g. CO 2 injection, in particular miscible case, into a specific oil reservoir is the 39 measurement of CO 2 -oil minimum miscibility pressure (MMP).…”

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confidence: 99%

“…the employed data set for their development[38][39][40][41][42][43][44][45][46].142 As a consequence, the most affecting parameters to study CO 2 -143 oil MMP during a CO 2 flooding EOR process are reservoir oil com-144 position, reservoir oil temperature and the purity of CO 2 . The data-145 bank used in this study consisting of experimental values for 146 molecular weight of C 5+ fraction in crude oil (MWC 5+ ), reservoir 147 temperature (T R ), the ratio of volatile (11,13,20,47-56].…”

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confidence: 99%

Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO 2 flooding

Kamari

Arabloo

Shokrollahi

et al. 2015

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Implementation of SVM framework to estimate PVT properties of reservoir oil

Cited by 138 publications

References 37 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

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO 2 flooding

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