H. Ghorbani
Iran University of Science and Technology
6 Papers
H. Ghorbani is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Computer science & Viscosity. The author has an hindex of 1, co-authored 1 publications.
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Papers
Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
TL;DR: In this paper , a multilayer extreme learning machine (MELM) hybridized with the cuckoo optimization algorithm (COA) was applied to estimate rheology and filtration properties with FD, S%, and March funnel viscosity (MFV) as input variables.
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Determination and investigation of shear wave velocity based on one deep/machine learning technique
Omid Hazbeh,Meysam Rajabi,Somayeh Tabasi,Sahar Lajmorak,H. Ghorbani,Ahmed E. Radwan,Mehdi Ahmadi Alvar,Omid Molaei +7 more
TL;DR: This study uses Deep Belief Network (DBN) and Random Forest (RF) algorithms to predict Shear Wave Velocity (Vs) in subsurface reservoirs, with DBN surpassing RF and empirical models in accuracy, achieving a low RMSE of 0.0398 km/s and R2 of 0.9775.
4
Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters
Seyed Vahid Alavi Nezhad Khalil Abad,Omid Hazbeh,Meysam Rajabi,Somayeh Tabasi,Sahar Lajmorak,H. Ghorbani,Ahmed E. Radwan,Mohammad Mudabbir +7 more
TL;DR: The LSSVM-PSO algorithm is more accurate than the LSSVM-GA algorithm for forecasting the rate of penetration (ROP) in drilling operations, with improved accuracy and reduced data noise.
3
Mass production of multi-wall carbon nanotubes by metal dusting process with high yield
TL;DR: In this paper, carbon nanotubes were synthesized over Fe-Ni nanoparticles generated during disintegration of the surface of alloy 304L under a metal dusting environment, and the results revealed that multi-wall carbon-nanotubes could be formed over nanocatalyst generated on the alloy surface by exploiting metal sanding process.
Application of GMDH model to predict pore pressure
Guozhong Gao,Meysam Rajabi,Somayeh Tabasi,H. Ghorbani,Reza Seyedkamali,Milad Shayanmanesh,Ahmed E. Radwan,Amirhosein Mosavi +7 more
TL;DR: In this article , the authors used the Group method of data handling (GMDH) to predict the pore pressure (PP) in subsurface-formations of three Middle East oil fields.