On the prediction of interfacial tension (IFT) for water-hydrocarbon gas system

Najafi‐Marghmaleki, Adel; Tatar, Afshin; Barati-Harooni, Ali; Mohebbi, Armin; Kalantari-Meybodi, Mahdi; Mohammadi, Amir H.

doi:10.1016/j.molliq.2016.10.083

Cited by 16 publications

(8 citation statements)

References 89 publications

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“…A RBF neural network has the ability to straightforwardly convert the data into a multi‐dimensional space and treat arbitrary scattered data . This methodology has a wide application in different fields and sciences such as chemical engineering, petroleum engineering, mathematical science, nanotechnology, etc . In contrast to MLP which may have two or more hidden layers, all RBF neural network models have only one hidden layer.…”

Section: Model Developmentmentioning

confidence: 99%

“…[70] This methodology has a wide application in different fields and sciences such as chemical engineering, petroleum engineering, mathematical science, nanotechnology, etc. [69,[71][72][73] In contrast to MLP which may have two or more hidden layers, all RBF neural network models have only one hidden layer. In the RBF model, inputs are transferred to the output layer by the neurons at hidden layer.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

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Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Section: Model Developmentmentioning

confidence: 99%

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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“…In the following section, some of the recent models in this area are briefly reviewed. It should be noted, as the dominant fluids that exist in the reservoir are oil and water, that the majority of ML models were developed to predict the IFT in oil−brine [22,23], water−hydrocarbon [24,25], brine−hydrocarbon [26][27][28], and CO 2 −brine [29][30][31][32] systems. Ahmadi and Mahmoudi [33] predicted the gas-oil IFT with least squares support vector machines (LSSVM) as a well-known ML method.…”

Section: Introductionmentioning

confidence: 99%

Modeling Interfacial Tension of N2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection

et al. 2022

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The combustion of fossil fuels from the input of oil refineries, power plants, and the venting or flaring of produced gases in oil fields leads to greenhouse gas emissions. Economic usage of greenhouse and flue gases in conventional and unconventional reservoirs would not only enhance the oil and gas recovery but also offers CO2 sequestration. In this regard, the accurate estimation of the interfacial tension (IFT) between the injected gases and the crude oils is crucial for the successful execution of injection scenarios in enhanced oil recovery (EOR) operations. In this paper, the IFT between a CO2/N2 mixture and n-alkanes at different pressures and temperatures is investigated by utilizing machine learning (ML) methods. To this end, a data set containing 268 IFT data was gathered from the literature. Pressure, temperature, the carbon number of n-alkanes, and the mole fraction of N2 were selected as the input parameters. Then, six well-known ML methods (radial basis function (RBF), the adaptive neuro-fuzzy inference system (ANFIS), the least square support vector machine (LSSVM), random forest (RF), multilayer perceptron (MLP), and extremely randomized tree (extra-tree)) were used along with four optimization methods (colliding bodies optimization (CBO), particle swarm optimization (PSO), the Levenberg–Marquardt (LM) algorithm, and coupled simulated annealing (CSA)) to model the IFT of the CO2/N2 mixture and n-alkanes. The RBF model predicted all the IFT values with exceptional precision with an average absolute relative error of 0.77%, and also outperformed all other models in this paper and available in the literature. Furthermore, it was found that the pressure and the carbon number of n-alkanes would show the highest influence on the IFT of the CO2/N2 and n-alkanes, based on sensitivity analysis. Finally, the utilized IFT database and the area of the RBF model applicability were investigated via the leverage method.

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“…In the four typical entrained droplet models above, the gas‐liquid surface tension and drag coefficient are all constant. The gas‐liquid surface tension, however, varies with temperature and pressure, 9‐16 and the drag coefficient is a function of droplet deformation and Reynolds number 17 …”

Section: Introductionmentioning

confidence: 99%

“…In the four typical entrained droplet models above, the gas-liquid surface tension and drag coefficient are all constant. The gas-liquid surface tension, however, varies with temperature and pressure, [9][10][11][12][13][14][15][16] and the drag coefficient is a function of droplet deformation and Reynolds number. 17 Wang et al 18 incorporated the influence of droplet deformation caused by the pressure differential on the drag coefficient but did not consider surface tension variation.…”

mentioning

confidence: 99%

A novel model for calculating critical droplet entrainment rate of gas well considering droplet deformation and multiple parameters

Xiao

Tan

et al. 2020

Energy Science & Engineering

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In most gas fields, depletion development has been widely employed, which caused formation pressure to decline, and as a result, the gas well production also decreases because the pressure is insufficient to continuously remove liquids from the gas well. The liquids, accumulating at the bottomhole and imposing an additional back pressure on the formation, further reduce the effective energy to remove the liquids and restricting well productivity; in severe cases, this may even

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On the prediction of interfacial tension (IFT) for water-hydrocarbon gas system

Cited by 16 publications

References 89 publications

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Modeling Interfacial Tension of N2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection

A novel model for calculating critical droplet entrainment rate of gas well considering droplet deformation and multiple parameters

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