Designing a committee of machines for modeling viscosity of water-based nanofluids

Hemmati-Sarapardeh, Abdolhossein; Hatami, Sobhan; Taghvaei, Hamed; Naseri, Ali; Band, Shahab S.; Chau, Kwok Wing

doi:10.1080/19942060.2021.1979099

Cited by 7 publications

(2 citation statements)

References 119 publications

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“…After giving data to the network, optimization of primary weights is started until a secondary distribution is obtained using BR since the data used may be associated with many errors, effective methods will be necessary to improve the generalization performance. Hence, the BR includes network complexity regulation and modifying performance function 60 , 61 .…”

Section: Model Developmentmentioning

confidence: 99%

Compositional modeling of gas-condensate viscosity using ensemble approach

Rezaei

Akbari

Rafiei

et al. 2023

Sci Rep

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In gas-condensate reservoirs, liquid dropout occurs by reducing the pressure below the dew point pressure in the area near the wellbore. Estimation of production rate in these reservoirs is important. This goal is possible if the amount of viscosity of the liquids released below the dew point is available. In this study, the most comprehensive database related to the viscosity of gas condensate, including 1370 laboratory data was used. Several intelligent techniques, including Ensemble methods, support vector regression (SVR), K-nearest neighbors (KNN), Radial basis function (RBF), and Multilayer Perceptron (MLP) optimized by Bayesian Regularization and Levenberg–Marquardt were applied for modeling. In models presented in the literature, one of the input parameters for the development of the models is solution gas oil ratio (Rs). Measuring Rs in wellhead requires special equipment and is somewhat difficult. Also, measuring this parameter in the laboratory requires spending time and money. According to the mentioned cases, in this research, unlike the research done in the literature, Rs parameter was not used to develop the models. The input parameters for the development of the models presented in this research were temperature, pressure and condensate composition. The data used includes a wide range of temperature and pressure, and the models presented in this research are the most accurate models to date for predicting the condensate viscosity. Using the mentioned intelligent approaches, precise compositional models were presented to predict the viscosity of gas/condensate at different temperatures and pressures for different gas components. Ensemble method with an average absolute percent relative error (AAPRE) of 4.83% was obtained as the most accurate model. Moreover, the AAPRE values for SVR, KNN, MLP-BR, MLP-LM, and RBF models developed in this study are 4.95%, 5.45%, 6.56%, 7.89%, and 10.9%, respectively. Then, the effect of input parameters on the viscosity of the condensate was determined by the relevancy factor using the results of the Ensemble methods. The most negative and positive effects of parameters on the gas condensate viscosity were related to the reservoir temperature and the mole fraction of C11, respectively. Finally, suspicious laboratory data were determined and reported using the leverage technique.

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Section: Model Developmentmentioning

confidence: 99%

Compositional modeling of gas-condensate viscosity using ensemble approach

Rezaei

Akbari

Rafiei

et al. 2023

Sci Rep

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“…The properties and efficiency of nanofluids in EOR depend on various parameters such as the size and type of the dispersed nanoparticles. − The behavior of pressure drop in nanofluids is different from that in common fluids. Nanoparticles dispersed in nanosuspensions are normally in the size range of 1–100 nm. − The small sizes of the nanomaterials increase their effective surface areas that will change some of the properties of the fluids containing these particles, such as heat transfer, viscosity, and activity of the particles on the surface. − So far, the most important mechanisms studied in EOR by nanofluids include pore channel plugging, pressure disjoining, increased viscosity of the injected phase, reduced oil–water IFT, wettability alteration and deformability, and prevention of asphaltene precipitation. − Esfandyari et al reviewed some published articles from 2010 to 2014. The most commonly used nanoparticles include zinc, aluminum oxide, silica, titanium dioxide, and zirconium oxide .…”

Section: Introductionmentioning

confidence: 99%

Effects of MgO, γ-Al₂O₃, and TiO₂ Nanoparticles at Low Concentrations on Interfacial Tension (IFT), Rock Wettability, and Oil Recovery by Spontaneous Imbibition in the Process of Smart Nanofluid Injection into Carbonate Reservoirs

2022

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Recently, some nanoparticles have been used to upgrade injected water into oil reservoirs to enhance oil recovery. These nanoadditives can be used in a variety of injectable waters, including smart/engineered water with special salinities. In this study, the performance of smart water containing different concentrations of magnesium sulfate (MgSO 4 ) and calcium chloride (CaCl 2 ) and 500 ppm of titanium dioxide (TiO 2 ), γ-alumina (γ-Al 2 O 3 ), and magnesium oxide (MgO) nanoparticles in interfacial tension (IFT) and contact angle reduction and oil production under imbibition of the chemical fluids was investigated. Based on the results, the IFT decreased more when ions and nanoparticles were present in the system. An optimum IFT of 4.684 mN/m was recorded for the nanofluid containing 2000 ppm of MgSO 4 + 500 ppm of MgO. The results of contact angle tests demonstrated improved saline water capabilities in the presence of nanoparticles and showed that a very effective reduction was accessible and highly hydrophilic wettability was obtained when using smart water with stable nanoparticles as a minimum contact angle of 18.33° was obtained by the optimal fluid containing nano-γ-Al 2 O 3 . Finally, an ultimate oil production of 64.1–68.7% was obtained in six experiments with smart water and stable nanoparticles.

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State estimation based on enhanced Bayesian approach: Application in water distribution systems

Shao

Wu³

et al. 2023

Control Engineering Practice

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Designing a committee of machines for modeling viscosity of water-based nanofluids

Cited by 7 publications

References 119 publications

Compositional modeling of gas-condensate viscosity using ensemble approach

Compositional modeling of gas-condensate viscosity using ensemble approach

Effects of MgO, γ-Al₂O₃, and TiO₂ Nanoparticles at Low Concentrations on Interfacial Tension (IFT), Rock Wettability, and Oil Recovery by Spontaneous Imbibition in the Process of Smart Nanofluid Injection into Carbonate Reservoirs

State estimation based on enhanced Bayesian approach: Application in water distribution systems

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Designing a committee of machines for modeling viscosity of water-based nanofluids

Cited by 7 publications

References 119 publications

Compositional modeling of gas-condensate viscosity using ensemble approach

Compositional modeling of gas-condensate viscosity using ensemble approach

Effects of MgO, γ-Al2O3, and TiO2 Nanoparticles at Low Concentrations on Interfacial Tension (IFT), Rock Wettability, and Oil Recovery by Spontaneous Imbibition in the Process of Smart Nanofluid Injection into Carbonate Reservoirs

State estimation based on enhanced Bayesian approach: Application in water distribution systems

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Effects of MgO, γ-Al₂O₃, and TiO₂ Nanoparticles at Low Concentrations on Interfacial Tension (IFT), Rock Wettability, and Oil Recovery by Spontaneous Imbibition in the Process of Smart Nanofluid Injection into Carbonate Reservoirs