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Pezhman Soltani Tehrani
Researcher at University of Tehran
Publications - 4
Citations - 39
Pezhman Soltani Tehrani is an academic researcher from University of Tehran. The author has contributed to research in topics: Geology & Deep learning. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.
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Journal ArticleDOI
Predicting oil flow rate through orifice plate with robust machine learning algorithms
Abouzar Rajabi Behesht Abad,Pezhman Soltani Tehrani,Mohammad Naveshki,Hamzeh Ghorbani,Nima Mohamadian,Shadfar Davoodi,Saeed Khezerloo-ye Aghdam,Jamshid Moghadasi,Hossein Saberi +8 more
TL;DR: In this article, a CNN model was developed to predict the flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (Tf), upstream pressure (Pu), root differential pressure (√ΔP), percentage of base sediment and water (BS&W), oil specific gravity (SG), kinematic viscosity (ν), and beta ratio (β, the ratio of pipe diameter to orifice diameter).
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Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms
Meysam Rajabi,Shadfar Davoodi,David A. Wood,Pezhman Soltani Tehrani,Mohammad Mehrad,Nima Mohamadian,V. Rukavishnikov,Ahmed E. Radwan +7 more
TL;DR: In this article , the authors used hybrid machine learning (HML) and deep learning (DL) algorithms for predicting shear wave velocity (V S ) from sedimentary rock sequences.
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Prediction of Bubble Point Pressure Using New Hybrid Computationail Intelligence Models
Mohammad Naveshki,Ali Naghiei,Pezhman Soltani Tehrani,Mehdi Ahmadi Alvar,Hamzeh Ghorbani,Nima Mohamadian,Jamshid Moghadasi +6 more
TL;DR: The outcomes of the study show the models developed are capable of predicting BPP with promising performance, where the best result was achieved for DWKNN-ICA and the performance comparison of the developed hybrid models with some previously developed models revealed that the DWKnn-ICA outperforms the former empirical models with respect to perdition accuracy.
Journal ArticleDOI
Laboratory study of polymer injection into heavy oil unconventional reservoirs to enhance oil recovery and determination of optimal injection concentration
TL;DR: In this paper , a new type of polymer called FLOPPAM 3630 has been used to investigate the overload of very heavy oil reservoirs, and stability tests on shear rate, time, and temperature were performed.