A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids

Hajirezaie, Sassan; Hemmati-Sarapardeh, Abdolhossein; Mohammadi, Amir H.; Pournik, Maysam; Kamari, Arash

doi:10.1016/j.jngse.2015.07.045

Cited by 50 publications

(13 citation statements)

References 35 publications

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“…partial derivatives of Eq. (4) with respect to the parameters such as w, b, e, and α, respectively, as follows (Ahmadi et al 2015a(Ahmadi et al , b, 2016Ahmadi and Pournik 2016;Ahmadi and Bahadori 2015;Ayatollahi et al 2016;Hajirezaie et al 2015Hajirezaie et al , 2017a:…”

Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Machine learning-based models for predicting permeability impairment due to scale deposition

Ahmadi

Chen

2020

J Petrol Explor Prod Technol

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Water injection is one of the robust techniques to maintain the reservoir pressure and produce trapped oil from oil reservoirs and improve an oil recovery factor. However, incompatibility between injected water and reservoir water causes an unflavored issue named "scale deposition." Owing to the deposited scales, effective permeability of a reservoir reduced, and pore throats might be plugged. To determine formation damage owing to scale deposition during a water injection process, two well-known machine learning methods, least squares support vector machine (LSSVM) and artificial neural network (ANN), are employed in the present paper. To improve the performance of the LSSVM method, a metaheuristic optimization algorithm, genetic algorithm (GA), is used. The constructed LSSVM model is examined using real formation damage data samples experimentally measured, which was reported in the literature. According to the obtained outputs of the above models, LSSVM has a high performance based on the correlation coefficient, and infinitesimal uncertainty based on a relative error between the model predictions and the corresponding actual data samples was less than 15%. Outcomes from this study indicate the useful application of the LSSVM approach in the prediction of permeability reduction due to scale deposition, and it can lead to a better and more reliable understanding of formation damage effects through water flooding without expensive laboratory measurements.

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Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Machine learning-based models for predicting permeability impairment due to scale deposition

Ahmadi

Chen

2020

J Petrol Explor Prod Technol

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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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“…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%

“…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 [23][24][25]. We have already developed two intelligent models for predicting natural gas compressibility factor using the same data bank of this study [1,2].…”

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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A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids

Cited by 50 publications

References 35 publications

Machine learning-based models for predicting permeability impairment due to scale deposition

Machine learning-based models for predicting permeability impairment due to scale deposition

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

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