Development of a robust model for prediction of under-saturated reservoir oil viscosity

Hajirezaie, Sassan; Pajouhandeh, Amin; Hemmati-Sarapardeh, Abdolhossein; Pournik, Maysam; Dabir, Bahram

doi:10.1016/j.molliq.2016.11.088

Cited by 35 publications

(12 citation statements)

References 37 publications

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“…The application of artificial intelligence and soft computing for building intelligent methods in many industries has recently attracted much attention (Anitescu, Atroshchenko, Alajlan, & Rabczuk, 2019;Chuntian & Chau, 2002;Fotovatikhah et al, 2018;Guo, Zhuang, & Rabczuk, 2019;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Taherei Ghazvinei et al, 2018;Yaseen, Sulaiman, Deo, & Chau, 2019). In petroleum and gas industries, intelligent models have been used to determine, oil and gas thermodynamic properties, reservoir formation properties and miscibility conditions required for gas injection processes (Dargahi-Zarandi, Hemmati-Sarapardeh, Hajirezaie, Dabir, & Atashrouz, 2017;Dashtian, Bakhshian, Hajirezaie, Nicot, & Hosseini, 2019;Hajirezaie, Hemmati, & Ayatollahi, 2014;Hajirezaie, Hemmati-Sarapardeh, Mohammadi, Pournik, & Kamari, 2015;Hajirezaie, Pajouhandeh, Hemmati-Sarapardeh, Pournik, & Dabir, 2017;Hajirezaie, Wu, Soltanian, & Sakha, 2019;Hemmati-Sarapardeh, Tashakkori, Hosseinzadeh, Mozafari, & Hajirezaie, 2016;Kamari, Pournik, Rostami, Amirlatifi, & Mohammadi, 2017;Kamari, Safiri, & Mohammadi, 2015;Rostami, Kamari, Panacharoensawad, & Hashemi, 2018). These models take both input and output values to get trained and later can make predictions.…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent models have been used in many reservoir engineering calculations. There are also some intelligent models that were developed specifically for predicting natural gas properties (Dargahi-Zarandi et al, 2017;Hajirezaie et al, 2015Hajirezaie et al, , 2017. We have already developed two intelligent models for predicting natural gas compressibility factor using the same data bank (Kamari et al, 2013;Shateri et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Modeling natural gas compressibility factor using a hybrid group method of data handling

Hemmati-Sarapardeh

Hajirezaie

Soltanian

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model's superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling natural gas compressibility factor using a hybrid group method of data handling

Hemmati-Sarapardeh

Hajirezaie

Soltanian

et al. 2019

Engineering Applications of Computational Fluid Mechanics

Self Cite

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show abstract

“…These models have been used to determine, oil and gas thermodynamic properties, reservoir formation properties and miscibility conditions required for gas injection processes [23][24][25][26][27][28][29][30][31][32]. These models take both input and output values to get trained and later can make predictions.…”

Section: Introductionmentioning

confidence: 99%

Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling

Hemmati‐Sarapardeh¹,

Hajirezaie²,

Soltanian³

et al. 2019

Preprint

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A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

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“…Hence, developing accurate and predictive models for solubility prediction of CO 2 in TBAB and other compounds is of great importance. The excellent performance of intelligent methods has been proved in solving different problems of chemical engineering . This study presents application of intelligent approaches named genetic algorithm–radial basis function (GA‐RBF), hybrid adaptive neuro‐fuzzy inference systems (Hybrid‐ANFIS), and gene expression programming (GEP) models for prediction of solubility of CO 2 in TBAB using experimental data from literature.…”

Section: Introductionmentioning

confidence: 99%

“…The excellent performance of intelligent methods has been proved in solving different problems of chemical engineering. [30][31][32][33][34] This study presents application of intelligent approaches named genetic algorithm-radial basis function (GA-RBF), hybrid adaptive neuro-fuzzy inference systems (Hybrid-ANFIS), and gene expression programming (GEP) models for prediction of solubility of CO 2 in TBAB using experimental data from literature. The effectiveness and reliability of models were checked through various graphical and statistical analyses.…”

Section: Introductionmentioning

confidence: 99%

Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra‐n‐butylammonium bromide solutions

Hoseinpour

Barati-Harooni

Nadali

et al. 2017

Journal of Chemometrics

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This study highlights the application of radial basis function (RBF) neural networks, adaptive neuro‐fuzzy inference systems (ANFIS), and gene expression programming (GEP) in the estimation of solubility of CO2 in aqueous solutions of tetra‐n‐butylammonium bromide (TBAB). The experimental data were gathered from a published work in literature. The proposed RBF network was coupled with genetic algorithm (GA) to access a better prediction performance of model. The structure of ANFIS model was trained by using hybrid method. The input parameters of the model were temperature, pressure, mass fraction of TBAB in feed aqueous solution (wTBAB), and mole fraction of TBAB in aqueous phase (xTBAB). The solubility of CO2 (xCO2) was the output parameter. Statistical and graphical analyses of the results showed that the proposed GA‐RBF, Hybrid‐ANFIS, and GEP models are robust and precise in the estimation of literature solubility data.

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Development of a robust model for prediction of under-saturated reservoir oil viscosity

Cited by 35 publications

References 37 publications

Modeling natural gas compressibility factor using a hybrid group method of data handling

Modeling natural gas compressibility factor using a hybrid group method of data handling

Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling

Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra‐n‐butylammonium bromide solutions

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