Smart models for predicting under-saturated crude oil viscosity: a comparative study

Razghandi, Milad; Hemmati-Sarapardeh, Abdolhossein; Rashidi, Fariborz; Dabir, Bahram; Shamshirband, Shahaboddin

doi:10.1080/15567036.2018.1555634

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Cited by 8 publications

(1 citation statement)

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“…Menad et al 4 estimated the solubility of CO2 in brine via advaned ML techniques. Razghandi et al 14 predicted under-saturated crude oil viscosity by ML algorithms. Bolandi et al 15 evaluated source rock characteristics by ML methods.…”

Section: Utilizedmentioning

confidence: 99%

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

Mosavi

Safaei-Farouji²

2021

SSRN Journal

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Section: Utilizedmentioning

confidence: 99%

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

Mosavi

Safaei-Farouji²

2021

SSRN Journal

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A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model

Lashkenari

Bagheri

Tatar

et al. 2023

Neural Comput & Applic

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Experimental Measurement and Accurate Prediction of Crude Oil Viscosity Utilizing Advanced Intelligent Approaches

Sadi¹,

Shahrabadi²

2023

Nat Resour Res

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Smart models for predicting under-saturated crude oil viscosity: a comparative study

Cited by 8 publications

References 17 publications

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model

Experimental Measurement and Accurate Prediction of Crude Oil Viscosity Utilizing Advanced Intelligent Approaches

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