S
Shadfar Davoodi
Researcher at Tomsk Polytechnic University
Publications - 38
Citations - 641
Shadfar Davoodi is an academic researcher from Tomsk Polytechnic University. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 7, co-authored 15 publications receiving 142 citations. Previous affiliations of Shadfar Davoodi include Sharif University of Technology.
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A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning
Nima Mohamadian,Hamzeh Ghorbani,David A. Wood,Mohammad Mehrad,Shadfar Davoodi,Sina Rashidi,Alireza Soleimanian,Amirafzal Kiani Shahvand +7 more
TL;DR: In this paper, the authors investigated casing collapse in wellbores from an established petroleum geomechanics perspective to develop and compare two hybrid neural-network models, multilayer perceptron's tuned, respectively, with a genetic algorithm (MLP-GA) and a particle swarm algorithm (MPA), which are configured to predict Poisson's ratio ( ϑ ) and maximum horizontal stress ( σ H ) from available well log input data.
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Application of a novel acrylamide copolymer containing highly hydrophobic comonomer as filtration control and rheology modifier additive in water-based drilling mud
TL;DR: In this article, the functionality of a synthetic based acrylamide-styrene copolymer (SBASC) as a supersede additive for water-based drilling mud was investigated.
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A novel field applicable mud formula with enhanced fluid loss properties in High Pressure-High Temperature well condition containing pistachio shell powder
TL;DR: In this paper, the use of pistachio shell powder in the form of two distinct fine particle sizes has been proposed for reducing fluid loss and increasing the thickness of mud cakes.
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Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms
Sina Rashidi,Mohammad Mehrad,Hamzeh Ghorbani,David A. Wood,Nima Mohamadian,Jamshid Moghadasi,Shadfar Davoodi +6 more
TL;DR: Four-hybrid machine-learning-optimization algorithms evaluated all outperform the empirical relationships used for many decades in the oil industry to predict bubble point pressure (BPP) and oil formation volume factor (OFVF).
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Application of sustainable saffron purple petals as an eco-friendly green additive for drilling fluids: A rheological, filtration, morphological, and corrosion inhibition study
TL;DR: In this article, the effects of dried saffron purple petals (SPP) powder were examined on the rheological, fluid loss, and corrosion inhibition properties of bentonite-based drilling fluids.