Meisam Adibifard's Personal Page

Name: Meisam Adibifard

Profile: Computational Scientist

Email: me.adibifard@Gmail.com

Phone: (225) 000-0000

Skill

Python

C++

Matlab

C-Sharp

About me

I graduated with Bachelors of Science (BS) in Petroleum Engineering from Islamic Azad University of Firouzabad, Iran. My research study began with the applications of machine learning (neural networks to be specific) in analyzing pressure transient data, which was also the subject of my Master's thesis in Sahand University of Technology, Iran. In 2019, I received my second Master's, this time in Chemical Engineering, from Mississippi State University, and now pursuing my Ph.D. in Engineering Science in Louisiana State University.

I have comprehensive research track in computational fields of studies, from large-scled reservoir simulations, to continuum-level Computational Fluid Dynamics (CFD), and non-continuum level Molecular Dynamics (MD). I have extensive coding experiences in Matlab, Python, C, and C++. I have written codes for Finite-Difference (FD), Finite-Element (FE), Streamline Simulations (SLS), and Molecular Dynamics (MD) simulations (parallelized using MPI library).

If I ever asked to describe my personality in three words, those would be: perseverant, quick-learner, and versatile!

Research Area

My research area spans multidisciplinary fields of study including Molecular Dynamics (MD), Computational Fluid Dynamics (CFD), and applications of Machine Learning (ML) algorithms in characterizing diversified chemophysical problems.

Molecular Dynamics (MD)

Computational Fluid Dynamics (CFD)

Machine Learning (ML)

Selected Publications

Adibifard, M., Nabizadeh, A., & Sharifi, M. (2020). Computational Fluid Dynamics to develop novel correlations for residual saturation of the displaced fluid in a capillary tube. Journal of Molecular Liquids, 299, 112122.
Nabizadeh, A., Adibifard, M., Hassanzadeh, H., Fahimpour, J., & Moraveji, M. K. (2019). Computational fluid dynamics to analyze the effects of initial wetting film and triple contact line on the efficiency of immiscible two-phase flow in a pore doublet model. Journal of Molecular Liquids, 273, 248-258.
Adibifard, M. (2018). A novel analytical solution to estimate residual saturation of the displaced fluid in a capillary tube by matching time-dependent injection pressure curves. Physics of Fluids, 30(8), 082107.
Adibifard, M., Sheidaie, A., & Sharifi, M. (2020). An intelligent heuristic-clustering algorithm to determine the most probable reservoir model from pressure–time series in underground reservoirs. Soft Computing, 24(20), 15773-15794.
Adibifard, M., Talebkeikhah, M., Sharifi, M., & Hemmati-Sarapardeh, A. (2020). Iterative ensemble Kalman filter and genetic algorithm for automatic reconstruction of relative permeability curves in the subsurface multi-phase flow. Journal of Petroleum Science and Engineering, 192, 107264.
Bazargan, H., & Adibifard, M. (2019). A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter. Neural Computing and Applications, 31(8), 3227-3243.
Aboosadi, Z. A., Rooeentan, S., & Adibifard, M. (2018). Estimation of subsurface petrophysical properties using different stochastic algorithms in nonlinear regression analysis of pressure transients. Journal of Applied Geophysics, 154, 93-107.
Adibifard, M., & Sharifi, M. (2018). A new semianalytical pressure transient model to interpret well test data in reservoirs with limited extent barriers. Journal of Porous Media, 21(11).
Adibifard, M., Bashiri, G., Roayaei, E., & Emad, M. A. (2016). Using particle swarm optimization (pso) algorithm in nonlinear regression well test analysis and its comparison with levenberg-marquardt algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 7(3), 1-23.
Adibifard, M., Tabatabaei-Nejad, S. A. R., & Khodapanah, E. (2014). Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test data. Journal of Petroleum Science and Engineering, 122, 585-594.