Afshin Tatar

Afshin Tatar

Higher Degree by Research Candidate

School of Chemical Engineering

Faculty of Sciences, Engineering and Technology

Afshin's research focuses on Machine Learning-Assisted Carbon Capture and Storage (CCS): Modelling and Optimisation. In this project, he addresses both the storage and capture components.

In the storage phase, he investigates both geological (rock) and fluid (brine) properties. Deep learning methods are applied to identify and classify rock types suitable for CO₂ storage, while fluid modeling focuses on the behavior of brine when CO₂ is dissolved, assessing its characteristics through data-driven models.

For the capture phase, he utilizes machine learning techniques to analyze and optimize the absorption capacities of various chemicals used in CO₂ capture. This includes studying both amine-based and ionic liquid absorbents, as well as investigating their efficiency in real-world applications.

Afshin's research focuses on Machine Learning-Assisted Carbon Capture and Storage (CCS): Modelling and Optimisation. In this project, he addresses both the storage and capture components.

In the storage phase, he investigates both geological (rock) and fluid (brine) properties. Deep learning methods are applied to identify and classify rock types suitable for CO₂ storage, while fluid modeling focuses on the behavior of brine when CO₂ is dissolved, assessing its characteristics through data-driven models.

For the capture phase, he utilizes machine learning techniques to analyze and optimize the absorption capacities of various chemicals used in CO₂ capture. This includes studying both amine-based and ionic liquid absorbents, as well as investigating their efficiency in real-world applications.

His research interests include:

  • Carbon Capture and Storage (CCS)
  • Artificial Intelligence (Machine Learning, Data Mining, Neural Networks, Deep Learning, Image Analysis, and Optimisation)
  • Data-Driven and Physics-Informed Models
  • Cheminformatics
  • Ionic Liquids

 

  • Appointments
    Date Position Institution name
    2023 - 2023 Lecturer, Python Programming Islamic Azad University of Bojnord, Faculty of Skill and Entrepreneurship · Contract
    2022 - 2023 Research Assistant Amol University of Special Modern Technologies, full time
    2021 - 2021 Research Assistant Nazarbayev University, full time
    2019 - 2021 Chemical Engineer SINOPEC, full time
    2016 - 2019 Assistant Chemical Engineer SINOPEC, full time
    2014 - 2016 Production Operator SINOPEC, full time

  • Awards and Achievements
    Date Type Title Institution Name Country Amount
    2024 Award 2024 SPE-SA Scholarship Society of Petroleum Engineering (SPE) Australia -
    2023 Scholarship University of Adelaide research scholarship for Ph.D. in Engineering The University of Adelaide Australia -
    2017 Recognition Highly Cited Research in Journal of Natural Gas Science and Engineering Journal of Natural Gas Science and Engineering, Elsevier Netherlands -
    2017 Award Most Cited Paper Award Certificate journal of Greenhouse Gases: Science & Technology, Wiley. United Kingdom -
    2016 Award Most Outstanding Researcher Award Young Researchers and Elite Club Iran, Islamic Republic of -
    2015 Award Most Cited Paper Award Certificate journal of Petroleum (Online ISSN: 2405-5816), KeAi Publishing China -
    2007 Scholarship Full scholarship from National Iranian Oil Company (NIOC) for B.Sc. in Petroleum University of Technology Petroleum University of Technology Iran, Islamic Republic of -

  • Language Competencies
    Language Competency
    English Can read, write, speak, understand spoken and peer review
    Persian Can read, write, speak, understand spoken and peer review

  • Education
    Date Institution name Country Title
    2023 University of Adelaide Australia PhD
    2011 - 2014 Sahand University of Technology Iran M.Sc.
    2007 - 2011 Petroleum University of Technology Iran B.Sc.

  • Certifications
    Date Title Institution name Country
    2024 Convolutional Neural Networks DeepLearning.AI, offered through Coursera -
    2024 Machine Learning Exploitability Kaggle -
    2024 16th IEAGHG International CCS Summer School International Energy Agency Greenhouse Gas R&D Programme (IEAGHG) Australia
    2023 PYTHON203: Data Manipulation and Visualisation in Python Intersect Research Faster -
    2023 Data Manipulation and Visualisation in Python DeepLearning.AI, offered through Coursera -
    2023 Structuring Machine Learning Projects DeepLearning.AI, offered through Coursera -
    2023 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization DeepLearning.AI, offered through Coursera -
    2023 Introduction to Deep Learning & Neural Networks with Keras IBM, offered through Coursera -
    2023 Neural Networks and Deep Learning DeepLearning.AI, offered through Coursera -

  • Research Interests
    Carbon capture and storage Artificial Intelligence Computer Vision Machine learning Optimisation Petroleum and Reservoir Engineering

  • Journals
    Year Citation
    2025 Tatar, A., Haghighi, M., & Zeinijahromi, A. (2025). Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks. Journal of Rock Mechanics and Geotechnical Engineering, 17(1), 106-125.
    DOI Scopus3
    2025 Tatar, A., Zeinijahromi, A., & Haghighi, M. (2025). Explainable Artificial Intelligence in modelling hydrogen gas solubility in n-Alkanes. Separation and Purification Technology, 362, 131741.
    DOI
    2025 Shokrollahi, A., Tatar, A., Atrbarmohammadi, S., & Zeinijahromi, A. (2025). Explainable Advanced Modelling of CO2-Dissolved Brine Density: Applications for Geological CO2 Storage in Aquifers. Inventions, 10(1), 15.
    DOI
    2024 Foroughizadeh, P., Shokrollahi, A., Tatar, A., & Zeinijahromi, A. (2024). Hydrogen solubility in different chemicals: A modelling approach and review of literature data. Engineering Applications of Artificial Intelligence, 136(Part B), 108978-1-108978-24.
    DOI Scopus5
    2024 Tatar, A., Shokrollahi, A., Zeinijahromi, A., & Haghighi, M. (2024). Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems. Sustainability, 16(17), 7512.
    DOI
    2024 Shokrollahi, A., Tatar, A., & Zeinijahromi, A. (2024). Advancing CO2 Solubility Prediction in Brine Solutions with Explainable Artificial Intelligence for Sustainable Subsurface Storage. Sustainability, 16(17), 7273.
    DOI Scopus1
    2023 Lashkenari, M. S., Bagheri, M., Tatar, A., Rezazadeh, H., & Inc, M. (2023). A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model. Neural Computing and Applications, 35(14), 10663-10676.
    DOI Scopus8 WoS4
    2023 Ghasemi, M., Tatar, A., Shafiei, A., & Ivakhnenko, O. P. (2023). Prediction of asphaltene adsorption capacity of clay minerals using machine learning. Canadian Journal of Chemical Engineering, 101(5), 2579-2597.
    DOI Scopus5 WoS1
    2023 Behvandi, R., Tatar, A., Shokrollahi, A., & Zeinijahromi, A. (2023). Evaluation of phase equilibrium conditions of clathrate hydrates in natural gas binary mixtures: Machine learning approach. Geoenergy Science and Engineering, 224, 211634.
    DOI Scopus2
    2023 Rayhani, M., Tatar, A., Shokrollahi, A., & Zeinijahromi, A. (2023). Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons. Geoenergy Science and Engineering, 226, 211778.
    DOI Scopus11
    2023 Tatar, A., & Shafiei, A. (2023). Connectionist Models for Asphaltene Precipitation Prediction by <i>n</i>-Alkane Titration─Pressure and Crude Oil Properties Considered. Industrial and Engineering Chemistry Research, 62(34), 13281-13302.
    DOI Scopus1
    2023 Esmaeili-Jaghdan, Z., Tatar, A., Shokrollahi, A., Bon, J., & Zeinijahromi, A. (2023). Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models. Neural Computing and Applications, 36(4), 1973-1995.
    DOI Scopus2
    2022 Shafiei, A., Tatar, A., Rayhani, M., Kairat, M., & Askarova, I. (2022). Artificial neural network, support vector machine, decision tree, random forest, and committee machine intelligent system help to improve performance prediction of low salinity water injection in carbonate oil reservoirs. Journal of Petroleum Science and Engineering, 219, 19 pages.
    DOI Scopus27 WoS5
    2022 Mirzaie, M., Esfandyari, H., & Tatar, A. (2022). Dew point pressure of gas condensates, modeling and a comprehensive review on literature data. Journal of Petroleum Science and Engineering, 211, 17 pages.
    DOI Scopus12 WoS5
    2022 Tatar, A., Esmaeili-Jaghdan, Z., Shokrollahi, A., & Zeinijahromi, A. (2022). Hydrogen solubility in n-alkanes: Data mining and modelling with machine learning approach. International Journal of Hydrogen Energy, 47(85), 35999-36021.
    DOI Scopus20 WoS1
    2022 Yerkenov, T., Tazikeh, S., Tatar, A., & Shafiei, A. (2022). Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models. ACS Omega, 7(37), 33123-33137.
    DOI Scopus4 WoS2 Europe PMC2
    2021 Tatar, A., Askarova, I., Shafiei, A., & Rayhani, M. (2021). Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs. ACS Omega, 6(47), 32304-32326.
    DOI Scopus14 WoS10 Europe PMC2
    2021 Sayahi, T., Tatar, A., Rostami, A., Anbaz, M. A., & Shahbazi, K. (2021). Determining solubility of CO<inf>2</inf> in aqueous brine systems via hybrid smart strategies. International Journal of Computer Applications in Technology, 65(1), 1-13.
    DOI Scopus13 WoS8
    2021 Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., & Mohamadian, N. (2021). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared (vol 6, pg 404, 2019). PETROLEUM, 7(2), 2 pages.
    2020 Mirzaie, M., & Tatar, A. (2020). Modeling of interfacial tension in binary mixtures of CH<inf>4</inf>, CO<inf>2</inf>, and N<inf>2</inf> - alkanes using gene expression programming and equation of state. Journal of Molecular Liquids, 320, 15 pages.
    DOI Scopus31 WoS20
    2020 Ghorbani, H., Wood, D. A., Choubineh, A., Mohamadian, N., Tatar, A., Farhangian, H., & Nikooey, A. (2020). Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared. Experimental and Computational Multiphase Flow, 2(4), 225-246.
    DOI Scopus46 WoS31
    2020 Tatar, A., Moghtadaei, G. M., Manafi, A., Cachadiña, I., & Mulero, Á. (2020). Determination of pure alcohols surface tension using Artificial Intelligence methods. Chemometrics and Intelligent Laboratory Systems, 201, 14 pages.
    DOI Scopus11 WoS7
    2020 Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., & Mohamadian, N. (2020). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared. Petroleum, 6(4), 404-414.
    DOI Scopus60
    2019 Barati-Harooni, A., Najafi-Marghmaleki, A., Hoseinpour, S. A., Tatar, A., Karkevandi-Talkhooncheh, A., Hemmati-Sarapardeh, A., & Mohammadi, A. H. (2019). Estimation of minimum miscibility pressure (MMP) in enhanced oil recovery (EOR) process by N<inf>2</inf> flooding using different computational schemes. Fuel, 235, 1455-1474.
    DOI Scopus39 WoS25
    2019 Rashid, S., Ghamartale, A., Abbasi, J., Darvish, H., & Tatar, A. (2019). Prediction of Critical Multiphase Flow Through Chokes by Using A Rigorous Artificial Neural Network Method. Flow Measurement and Instrumentation, 69, 9 pages.
    DOI Scopus27 WoS19
    2019 Ameli, F., Hemmati-Sarapardeh, A., Tatar, A., Zanganeh, A., & Ayatollahi, S. (2019). Modeling interfacial tension of normal alkane-supercritical CO<inf>2</inf> systems: Application to gas injection processes. Fuel, 253, 1436-1445.
    DOI Scopus16 WoS10
    2019 Tatar, A., Barati, A., Najafi, A., & Mohammadi, A. H. (2019). Radial basis function (RBF) network for modeling gasoline properties. Petroleum Science and Technology, 37(11), 1306-1313.
    DOI Scopus9 WoS3
    2019 Moghadasi, R., Rostami, A., Tatar, A., & Hemmati-Sarapardeh, A. (2019). An experimental study of Nanosilica application in reducing calcium sulfate scale at high temperatures during high and low salinity water injection. Journal of Petroleum Science and Engineering, 179, 7-18.
    DOI Scopus23 WoS18
    2019 Rahmati, A. S., & Tatar, A. (2019). Application of Radial Basis Function (RBF) neural networks to estimate oil field drilling fluid density at elevated pressures and temperatures. Oil and Gas Science and Technology, 74, 6 pages.
    DOI Scopus18 WoS12
    2018 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., Arabloo, M., Rafiee-Taghanaki, S., & Mohammadi, A. H. (2018). Reliable modeling of constant volume depletion (CVD) behaviors in gas condensate reservoirs. Fuel, 231, 146-156.
    DOI Scopus33 WoS20
    2018 Hemmat Esfe, M., Tatar, A., Ahangar, M. R. H., & Rostamian, H. (2018). A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant. Physica E: Low-Dimensional Systems and Nanostructures, 96, 85-93.
    DOI Scopus83 WoS65
    2018 Hoseinpour, S. A., Barati-Harooni, A., Nadali, P., Mohebbi, A., Najafi-Marghmaleki, A., Tatar, A., & Bahadori, A. (2018). Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra-n-butylammonium bromide solutions. Journal of Chemometrics, 32(2), 20 pages.
    DOI Scopus7 WoS6
    2018 Ahmadi, M. H., Tatar, A., Seifaddini, P., Ghazvini, M., Ghasempour, R., & Sheremet, M. A. (2018). Thermal conductivity and dynamic viscosity modeling of Fe<inf>2</inf>O<inf>3</inf>/water nanofluid by applying various connectionist approaches. Numerical Heat Transfer; Part A: Applications, 74(6), 1301-1322.
    DOI Scopus45 WoS40
    2018 Kahani, M., Ahmadi, M. H., Tatar, A., & Sadeghzadeh, M. (2018). Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO<inf>2</inf>/water nanofluid flows through non-straight pathways. Numerical Heat Transfer; Part A: Applications, 74(4), 1190-1206.
    DOI Scopus62 WoS56
    2018 Rostami, A., Kalantari-Meybodi, M., Karimi, M., Tatar, A., & Mohammadi, A. H. (2018). Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding. Oil and Gas Science and Technology, 73, 17 pages.
    DOI Scopus50 WoS38
    2018 Ahmadi, M. H., Tatar, A., Alhuyi Nazari, M., Ghasempour, R., Chamkha, A. J., & Yan, W. M. (2018). Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks. International Journal of Heat and Mass Transfer, 126, 1079-1086.
    DOI Scopus44 WoS38
    2017 Zendehboudi, A., & Tatar, A. (2017). Oil flooded scroll compressors: Predicting the energy performance and evaluating the experimental data. Measurement: Journal of the International Measurement Confederation, 112, 38-46.
    DOI Scopus12 WoS11
    2017 Zendehboudi, A., Tatar, A., & Li, X. (2017). A comparative study and prediction of the liquid desiccant dehumidifiers using intelligent models. Renewable Energy, 114, 1023-1035.
    DOI Scopus29 WoS25
    2017 Najafi-Marghmaleki, A., Barati-Harooni, A., Tatar, A., Mohebbi, A., & Mohammadi, A. H. (2017). On the prediction of Watson characterization factor of hydrocarbons. Journal of Molecular Liquids, 231, 419-429.
    DOI Scopus25 WoS19
    2017 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., & Bahadori, A. (2017). Accurate prediction of CO<inf>2</inf> solubility in eutectic mixture of levulinic acid (or furfuryl alcohol) and choline chloride. International Journal of Greenhouse Gas Control, 58, 212-222.
    DOI Scopus23 WoS16
    2017 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., & Mohammadi, A. H. (2017). A GEP based model for prediction of densities of ionic liquids. Journal of Molecular Liquids, 227, 373-385.
    DOI Scopus14 WoS11
    2017 Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., Mohebbi, A., Khaksar-Manshad, A., & Mohammadi, A. H. (2017). An accurate model for prediction of wax deposition in oil systems. Petroleum and Coal, 59(6), 797-807.
    2017 Zendehboudi, A., & Tatar, A. (2017). Utilization of the RBF network to model the nucleate pool boiling heat transfer properties of refrigerant-oil mixtures with nanoparticles. Journal of Molecular Liquids, 247, 304-312.
    DOI Scopus45 WoS36
    2017 Barati-Harooni, A., Nasery, S., Tatar, A., Najafi-Marghmaleki, A., Isafiade, A. J., & Bahadori, A. (2017). Prediction of H<inf>2</inf>S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks. Chemical Engineering and Technology, 40(2), 367-375.
    DOI Scopus10 WoS9
    2017 Dadkhah, M. R., Tatar, A., Mohebbi, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Ghiasi, M. M., . . . Pourfayaz, F. (2017). Prediction of solubility of solid compounds in supercritical CO<inf>2</inf> using a connectionist smart technique. Journal of Supercritical Fluids, 120, 181-190.
    DOI Scopus29 WoS20
    2016 Nasery, S., Barati-Harooni, A., Tatar, A., Najafi-Marghmaleki, A., & Mohammadi, A. H. (2016). Accurate prediction of solubility of hydrogen in heavy oil fractions. Journal of Molecular Liquids, 222, 933-943.
    DOI Scopus40 WoS31
    2016 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Mohebbi, A., Ghiasi, M. M., Mohammadi, A. H., & Hajinezhad, A. (2016). Comparison of two soft computing approaches for predicting CO<inf>2</inf> solubility in aqueous solution of piperazine. International Journal of Greenhouse Gas Control, 53, 85-97.
    DOI Scopus41 WoS31
    2016 Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., & Mohammadi, A. H. (2016). Experimental and modeling studies on adsorption of a nonionic surfactant on sandstone minerals in enhanced oil recovery process with surfactant flooding. Journal of Molecular Liquids, 220, 1022-1032.
    DOI Scopus94 WoS70
    2016 Barati-Harooni, A., Soleymanzadeh, A., Tatar, A., Najafi-Marghmaleki, A., Samadi, S. J., Yari, A., . . . Mohammadi, A. H. (2016). Experimental and modeling studies on the effects of temperature, pressure and brine salinity on interfacial tension in live oil-brine systems. Journal of Molecular Liquids, 219, 985-993.
    DOI Scopus58 WoS46
    2016 Tatar, A., Barati-Harooni, A., Partovi, M., Najafi-Marghmaleki, A., & Mohammadi, A. H. (2016). An accurate model for predictions of vaporization enthalpies of hydrocarbons and petroleum fractions. Journal of Molecular Liquids, 220, 192-199.
    DOI Scopus18 WoS16
    2016 Sayahi, T., Tatar, A., & Bahrami, M. (2016). A RBF model for predicting the pool boiling behavior of nanofluids over a horizontal rod heater. International Journal of Thermal Sciences, 99, 180-194.
    DOI Scopus59 WoS52
    2016 Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., Arabloo, M., Phung, L. T. K., Lee, M., & Bahadori, A. (2016). Prediction of frictional pressure loss for multiphase flow in inclined annuli during Underbalanced Drilling operations. Natural Gas Industry B, 3(4), 275-282.
    DOI Scopus11
    2016 Tatar, A., Barati-Harooni, A., Moradi, S., Nasery, S., Najafi-Marghmaleki, A., Lee, M., . . . Bahadori, A. (2016). Prediction of heavy oil viscosity using a radial basis function neural network. Petroleum Science and Technology, 34(21), 1742-1748.
    DOI Scopus10 WoS5
    2016 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., Mohebbi, A., Kalantari-Meybodi, M., & Mohammadi, A. H. (2016). On the prediction of interfacial tension (IFT) for water-hydrocarbon gas system. Journal of Molecular Liquids, 224, 976-990.
    DOI Scopus16 WoS13
    2016 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Ahmadi-Pour, M., Nadali, P., & Mohammadi, A. H. (2016). Prediction of moisture content of natural gases using a GA-RBF model. Journal of Molecular Liquids, 223, 994-999.
    DOI Scopus5 WoS4
    2016 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., Choobineh, M. J., & Mohammadi, A. H. (2016). GA-RBF model for prediction of dew point pressure in gas condensate reservoirs. Journal of Molecular Liquids, 223, 979-986.
    DOI Scopus26 WoS14
    2016 Tatar, A., Nasery, S., Bahadori, A., Bahadori, M., Najafi-Marghmaleki, A., & Barati-Harooni, A. (2016). Implementing radial basis function neural network for prediction of surfactant retention in petroleum production and processing industries. Petroleum Science and Technology, 34(11-12), 992-999.
    DOI Scopus11 WoS10
    2016 Tatar, A., Nasery, S., Bahadori, M., Bahadori, A., Bahadori, M., Barati-Harooni, A., & Najafi-Marghmaleki, A. (2016). Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network. Petroleum Science and Technology, 34(10), 951-960.
    DOI Scopus15 WoS12
    2016 Tatar, A., Najafi-Marghmaleki, A., Barati-Harooni, A., Gholami, A., Ansari, H. R., Bahadori, M., . . . Bahadori, A. (2016). Implementing radial basis function neural networks for prediction of saturation pressure of crude oils. Petroleum Science and Technology, 34(5), 454-463.
    DOI Scopus22 WoS19
    2016 Tatar, A., Naseri, S., Bahadori, M., Hezave, A. Z., Kashiwao, T., Bahadori, A., & Darvish, H. (2016). Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks. Journal of the Taiwan Institute of Chemical Engineers, 60, 151-164.
    DOI Scopus70 WoS50
    2016 Tatar, A., Barati, A., Yarahmadi, A., Najafi, A., Lee, M., & Bahadori, A. (2016). Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models. International Journal of Greenhouse Gas Control, 47, 122-136.
    DOI Scopus59 WoS50
    2016 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Norouzi-Farimani, B., & Mohammadi, A. H. (2016). Predictive model based on ANFIS for estimation of thermal conductivity of carbon dioxide. Journal of Molecular Liquids, 224, 1266-1274.
    DOI Scopus33 WoS26
    2016 Rezaei, A., Abdi-Khangah, M., Mohebbi, A., Tatar, A., & Mohammadi, A. H. (2016). Using surface modified clay nanoparticles to improve rheological behavior of Hydrolized Polyacrylamid (HPAM) solution for enhanced oil recovery with polymer flooding. Journal of Molecular Liquids, 222, 1148-1156.
    DOI Scopus107 WoS89
    2016 Naseri, S., Tatar, A., & Shokrollahi, A. (2016). Development of an accurate method to prognosticate choke flow coefficients for natural gas flow through nozzle and orifice type chokes. Flow Measurement and Instrumentation, 48, 1-7.
    DOI Scopus36 WoS21
    2016 Afshin, T., Ali, B. H., Hossein, M., Saeid, N., Meysam, B., Moonyong, L., . . . Adel, N. M. (2016). Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF) neural networks. Natural Gas Industry B, 3(2), 173-180.
    DOI Scopus8
    2015 Tatar, A., Shokrollahi, A., Lee, M., Kashiwao, T., & Bahadori, A. (2015). Prediction of supercritical CO<inf>2</inf>/brine relative permeability in sedimentary basins during carbon dioxide sequestration. Greenhouse Gases: Science and Technology, 5(6), 756-771.
    DOI Scopus13 WoS11
    2015 Darvish, H., Nouri-Taleghani, M., Shokrollahi, A., & Tatar, A. (2015). Geo-mechanical modeling and selection of suitable layer for hydraulic fracturing operation in an oil reservoir (south west of Iran). Journal of African Earth Sciences, 111, 409-420.
    DOI Scopus28 WoS20
    2015 Shokrollahi, A., Tatar, A., & Safari, H. (2015). On accurate determination of PVT properties in crude oil systems: Committee machine intelligent system modeling approach. Journal of the Taiwan Institute of Chemical Engineers, 55, 17-26.
    DOI Scopus61 WoS52
    2015 Tatar, A., Shokrollahi, A., Halali, M. A., Azari, V., & Safari, H. (2015). A Hybrid Intelligent Computational Scheme for Determination of Refractive Index of Crude Oil Using SARA Fraction Analysis. Canadian Journal of Chemical Engineering, 93(9), 1547-1555.
    DOI Scopus28 WoS23
    2015 Nouri-Taleghani, M., Mahmoudifar, M., Shokrollahi, A., Tatar, A., & Karimi-Khaledi, M. (2015). Fracture density determination using a novel hybrid computational scheme: A case study on an Iranian Marun oil field reservoir. Journal of Geophysics and Engineering, 12(2), 188-198.
    DOI Scopus32 WoS17
    2015 Tatar, A., Naseri, S., Sirach, N., Lee, M., & Bahadori, A. (2015). Prediction of reservoir brine properties using radial basis function (RBF) neural network. Petroleum, 1(4), 349-357.
    DOI Scopus32
    2015 Naseri, S., Tatar, A., Bahadori, M., Rozyn, J., Kashiwao, T., & Bahadori, A. (2015). Application of Radial Basis Function Neural Network for Prediction of Titration-Based Asphaltene Precipitation. Petroleum Science and Technology, 33(23-24), 1875-1882.
    DOI Scopus10 WoS9
    2015 Tatar, A., Naseri, S., Bahadori, M., Rozyn, J., Lee, M., Kashiwao, T., & Bahadori, A. (2015). Evaluation of Different Artificial Intelligent Models to Predict Reservoir Formation Water Density. Petroleum Science and Technology, 33(20), 1749-1756.
    DOI Scopus8 WoS5
    2014 Tatar, A., Yassin, M. R., Rezaee, M., Aghajafari, A. H., & Shokrollahi, A. (2014). Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs. Journal of Natural Gas Science and Engineering, 21, 79-94.
    DOI Scopus46 WoS40
    2014 Hemmati-Sarapardeh, A., Shokrollahi, A., Tatar, A., Gharagheizi, F., Mohammadi, A. H., & Naseri, A. (2014). Reservoir oil viscosity determination using a rigorous approach. Fuel, 116, 39-48.
    DOI Scopus122 WoS106
    2013 Kamari, A., Khaksar-Manshad, A., Gharagheizi, F., Mohammadi, A. H., & Ashoori, S. (2013). Robust Model for the Determination of Wax Deposition in Oil Systems. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 52(44), 15664-15672.
    DOI
    2013 Tatar, A., Shokrollahi, A., Mesbah, M., Rashid, S., Arabloo, M., & Bahadori, A. (2013). Implementing Radial Basis Function Networks for modeling CO<inf>2</inf>-reservoir oil minimum miscibility pressure. Journal of Natural Gas Science and Engineering, 15, 82-92.
    DOI Scopus148 WoS119
    - A, T., & MA, H. (n.d.). On the Estimation of the Density of Brine with an Extensive Range of Different Salts Compositions and Concentrations. Journal of Thermodynamics &amp; Catalysis, 7(2).
    DOI

  • Book Chapters
    Year Citation
    2018 Tatar, A. (2018). Microbial Enhanced Oil Recovery: Microbiology and Fundamentals. In Fundamentals of Enhanced Oil and Gas Recovery from Conventional and Unconventional Reservoirs (pp. 291-508). Elsevier.
    DOI Scopus14

  • Teaching Assistant, Well Testing & Pressure Transient Analysis, University of Adelaide, March 2025-Present. 

  • Teaching Assistant Tutor, Unconventional Resources & Recovery, University of Adelaide, March 2025-Present. 

  • Teaching Assistant, Data analytics in oil and gas industry, University of Adelaide, July 2024-November 2024.

  • Teaching Assistant Tutor, Formation Evaluation, Petrophysics & Rock Properties, University of Adelaide, July 2024-November 2024.

  • Teaching Assistant, Unconventional Resources & Recovery, University of Adelaide, March 2024-May 2024. 

  • Teaching Assistant, Formation Evaluation, Petrophysics & Rock Properties, University of Adelaide, July 2023-December 2023. 

  • Lecturer, Python Programming, Faculty of Skill and Entrepreneurship of Islamic Azad University, Bojnord branch, 2023.

  • Committee Memberships
    Date Role Committee Institution Country
    2025 - ongoing Representative SPE South Australia Section University of Adelaide Australia

  • Memberships
    Date Role Membership Country
    2025 - ongoing Member  European Association of Geoscientists and Engineers (EAGE) Australia
    2024 - ongoing Member Petroleum Exploration Society of Australia (PESA) Australia
    2023 - ongoing Member Society of Petroleum Engineering (SPE) Australia

  • Editorial Boards
    Date Role Editorial Board Name Institution Country
    2024 - ongoing Editor International Journal of Energy Research Wiely United States
    2024 - ongoing Editor Frontiers in Energy Research Frontiers Switzerland
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