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Mohammad Ali Ahmadi   Dr.  Research or Laboratory Scientist 
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Mohammad Ali Ahmadi published an article in November 2018.
Top co-authors
M.A. Rosen

279 shared publications

Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, Canada

Michel Feidt

46 shared publications

LEMTA, URA CNRS 7563, University of Lorraine, 54518 Vandoeuvre-lès-Nancy, France

Ahmad Zeini Vand

18 shared publications

Department of Renewable Energy and Environment, Faculty of New Sciences and Technologies; University of Tehran; Tehran Iran

42
Publications
23
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0
Downloads
168
Citations
Publication Record
Distribution of Articles published per year 
(2011 - 2018)
Total number of journals
published in
 
22
 
Publications See all
Article 0 Reads 0 Citations Renewable energy harvesting with the application of nanotechnology: A review Mohammad H. Ahmadi, Mahyar Ghazvini, Mohammad Alhuyi Nazari,... Published: 14 November 2018
International Journal of Energy Research, doi: 10.1002/er.4282
DOI See at publisher website
Article 1 Read 0 Citations Hybrid connectionist model determines CO2–oil swelling factor Mohammad Ali Ahmadi, Sohrab Zendehboudi, Lesley A. James Published: 26 April 2018
Petroleum Science, doi: 10.1007/s12182-018-0230-5
DOI See at publisher website ABS Show/hide abstract
In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters (γ and σ2) of the LS-SVM model. This model showed a high coefficient of determination (R2 = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.
Article 1 Read 1 Citation Developing a robust proxy model of CO2 injection: Coupling Box–Behnken design and a connectionist method Mohammad Ali Ahmadi, Sohrab Zendehboudi, Lesley A. James Published: 01 March 2018
Fuel, doi: 10.1016/j.fuel.2017.11.030
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Article 0 Reads 0 Citations Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs Ali Shafiei, Mohammad Ali Ahmadi, Maurice B. Dusseault, Ali ... Published: 26 January 2018
Energies, doi: 10.3390/en11020292
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Thermal oil recovery techniques, including steam processes, account for more than 80% of the current global heavy oil, extra heavy oil, and bitumen production. Evaluation of Naturally Fractured Carbonate Reservoirs (NFCRs) for thermal heavy oil recovery using field pilot tests and exhaustive numerical and analytical modeling is expensive, complex, and personnel-intensive. Robust statistical models have not yet been proposed to predict cumulative steam to oil ratio (CSOR) and recovery factor (RF) during steamflooding in NFCRs as strong process performance indicators. In this paper, new statistical based techniques were developed using multivariable regression analysis for quick estimation of CSOR and RF in NFCRs subjected to steamflooding. The proposed data based models include vital parameters such as in situ fluid and reservoir properties. The data used are taken from experimental studies and rare field trials of vertical well steamflooding pilots in heavy oil NFCRs reported in the literature. The models show an average error of <6% for the worst cases and contain fewer empirical constants compared with existing correlations developed originally for oil sands. The interactions between the parameters were considered indicating that the initial oil saturation and oil viscosity are the most important predictive factors. The proposed models were successfully predicted CSOR and RF for two heavy oil NFCRs. Results of this study can be used for feasibility assessment of steamflooding in NFCRs
Article 0 Reads 0 Citations Equilibrium ratio of hydrocarbons and non-hydrocarbons at reservoir conditions: Experimental and modeling study Mohammad Ali Ahmadi, Sohrab Zendehboudi, Lesley A. James Published: 01 December 2017
Fuel, doi: 10.1016/j.fuel.2017.07.111
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Article 0 Reads 1 Citation A reliable strategy to calculate minimum miscibility pressure of CO 2 -oil system in miscible gas flooding processes Mohammad Ali Ahmadi, Sohrab Zendehboudi, Lesley A. James Published: 01 November 2017
Fuel, doi: 10.1016/j.fuel.2017.06.135
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