Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system

Dargahi-Zarandi, Atefeh; Hemmati-Sarapardeh, Abdolhossein; Hajirezaie, Sassan; Dabir, Bahram; Atashrouz, Saeid

doi:10.1016/j.molliq.2017.03.066

Cited by 59 publications

(37 citation statements)

References 38 publications

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“…Group Method of Data Handling (GMDH) known also as polynomial neural network is one of the most promising families of artificial neural networks (ANNs) (Dargahi-Zarandi, Hemmati-Sarapardeh, Hajirezaie, Dabir, & Atashrouz, 2017). Beside the reliability shown by GMDH in modeling complex systems, it ensures the advantage of providing user-friendly polynomial formula to the system being studied.…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

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Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

Menad

Zerhouni

Hemmati-Sarapardeh

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil-water systems (K rw and K ro ) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on K rw and K ro is still a major challenge for precise studying of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable and simple to use paradigms to predict the temperature dependency of K rw and K ro . To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K rw and K ro , respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of K rw and K ro in TEOR. ARTICLE HISTORY

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

“…To solve this problem, this latter if transformed to a matrix form as (Dargahi-Zarandi et al, 2017;Hemmati-Sarapardeh & Mohagheghian, 2017):…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

Menad

Zerhouni

Hemmati-Sarapardeh

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…Recently, we applied this methodology for solving complex problems in chemical and petroleum engineering. We used GMDH to model critical properties of pure compounds, the normal boiling point of pure compounds, gas viscosity, interfacial tension of nitrogen‐normal alkane systems, and physicochemical properties of ionic liquid mixtures …”

Section: Model Developmentmentioning

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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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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“…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%

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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Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system

Cited by 59 publications

References 38 publications

Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

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

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

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