Developing a New Model for Drilling Rate of Penetration Prediction Using Convolutional Neural Network von Matinkia, Morteza, Sheykhinasab, Amirhossein, Shojaei, Soroush, Vojdani Tazeh Kand, Ali, Elmi, Arad, Bajolvand, Mahdi, Mehrad, Mohammad

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Modeling the effect of the striker geometry on the wave propagation pattern in the Split-Hopkinson pressure bar test using the discrete element method von Majid Nikkhah, Mahdi Bajolvand

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Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning von Davoodi, Shadfar, Mehrad, Mohammad, Wood, David A., Rukavishnikov, Valeriy S., Bajolvand, Mahdi

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Developing a new rigorous drilling rate prediction model using a machine learning technique von Mehrad, Mohammad, Bajolvand, Mahdi, Ramezanzadeh, Ahmad, Neycharan, Jalil Ghavidel

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Optimization of controllable drilling parameters using a novel geomechanics-based workflow von Bajolvand, Mahdi, Ramezanzadeh, Ahmad, Mehrad, Mohammad, Roohi, Abbas

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Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms von Mehrad, Mohammad, Ramezanzadeh, Ahmad, Bajolvand, Mahdi, Reza Hajsaeedi, Mohammad

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A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network von Matinkia, Morteza, Amraeiniya, Ali, Behboud, Mohammad Mohammadi, Mehrad, Mohammad, Bajolvand, Mahdi, Gandomgoun, Mohammad Hossein, Gandomgoun, Mehdi

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