N. Mohamadian

发表

Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

David A. Wood, Hamzeh Ghorbani حمزه قربانی, A. Choubineh, 2019, Experimental and Computational Multiphase Flow.

Shear modulus prediction of embedded pressurized salt layers and pinpointing zones at risk of casing collapse in oil and gas wells

David A. Wood, Mehdi Ahmadi Alvar, K. Shahbazi, 2020 .

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

David A. Wood, Hamzeh Ghorbani حمزه قربانی, A. Choubineh, 2020 .

A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning

David A. Wood, Hamzeh Ghorbani حمزه قربانی, N. Mohamadian, 2021 .

Reinforcement of oil and gas wellbore cements with a methyl methacrylate/carbon-nanotube polymer nanocomposite additive

David A. Wood, R. Ashena, N. Mohamadian, 2020 .

Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

David A. Wood, Hamzeh Ghorbani حمزه قربانی, A. Choubineh, 2018, Journal of Petroleum Exploration and Production Technology.

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

David A. Wood, Pezhman Soltani Tehrani, Hamzeh Ghorbani حمزه قربانی, 2022, Journal of Petroleum Exploration and Production Technology.

Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

Khalil Shahbazi, Hamzeh Ghorbani, David A. Wood, 2021 .

Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well

Jamshid Moghadasi, Hamzeh Ghorbani, Nima Mohamadian, 2021 .

Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling

Shahab S. Band, David A. Wood, N. Mohamadian, 2022, Marine and Petroleum Geology.

A hybrid nanocomposite of poly(styrene-methyl methacrylate- acrylic acid) /clay as a novel rheology-improvement additive for drilling fluids

David A. Wood, N. Mohamadian, Hamzeh Ghorbani, 2019, Journal of Polymer Research.

Rheological and ﬁltration characteristics of drilling ﬂuids enhanced by nanoparticles with selected additives: an experimental study

Hamzeh Ghorbani, David A. Wood, Nima Mohamadian, 2018 .

Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response

David A. Wood, N. Mohamadian, M. Abdali, 2021, Journal of Construction Materials.

Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms

Jamshid Moghadasi, Hamzeh Ghorbani, Mohammad Mehrad, 2021, Journal of Petroleum Science and Engineering.

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

David A. Wood, Pezhman Soltani Tehrani, N. Mohamadian, 2022, Journal of Petroleum Exploration and Production Technology.

Robust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir

Shahab S. Band, David A. Wood, Mehdi Ahmadi Alvar, 2022, Marine and Petroleum Geology.

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Mehdi Ahmadi Alvar, N. Mohamadian, Ahmed E. Radwan, 2021, Journal of Petroleum Exploration and Production Technology.

Predicting oil flow rate through orifice plate with robust machine learning algorithms

Saeed Khezerloo-ye Aghdam, Pezhman Soltani Tehrani, N. Mohamadian, 2021, Flow Measurement and Instrumentation.

Prediction of Bubble Point Pressure Using New Hybrid Computationail Intelligence Models

Jamshid Moghadasi, Hamzeh Ghorbani, Nima Mohamadian, 2021 .

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Jamshid Moghadasi, Hamzeh Ghorbani, Nima Mohamadian, 2021 .

Carbon-nanotube-polymer nanocomposites enable wellbore cements to better inhibit gas migration and enhance sustainability of natural gas reservoirs

N. Mohamadian, David A. Wood, Hamzeh Ghorbani, 2022, Sustainable Natural Gas Reservoir and Production Engineering.

Prediction of oil flow rate through orifice flow meters: Optimized machine-learning techniques

Mohammad Farsi, Hamzeh Ghorbani, David A. Wood, 2021 .

Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes

David A. Wood, N. Mohamadian, J. Moghadasi, 2021, Journal of Petroleum Exploration and Production.

Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

David A. Wood, N. Mohamadian, M. Farsi, 2021, Natural Resources Research.

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Hamid Reza Nasriani, Hamzeh Ghorbani, Mohammad Mehrad, 2022, Fuel.

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

David A. Wood, N. Mohamadian, S. Rashidi, 2020 .

Effects of DC plasma nitriding parameters on properties of DIN 1.2344 low alloy steel

N. Mohamadian, H. Khorsand, H. Aghajani, 2013 .

Optimized machine learning models for natural fractures prediction using conventional well logs

David A. Wood, N. Mohamadian, S. Tabasi, 2022, Fuel.