.... neural network machine learning drilling operation artificial intelligence deep learning in-seam drilling engineering drill bit gamma ray correlation rop complex reservoir upstream oil & gas prediction rate of penetration formation type noncoal coal noncoal classification regression...

... (flash) calculations to improve computational speed. Unlike previous publications that apply classical deep learning (DL) models to flash calculations, this work will demonstrate the first attempt to incorporate thermodynamics constraints into the training of these models to ensure that they honor...

.... In this study, one of the most classic unsupervised machine learning methods namely, principal component analysis (PCA), was combined with radial basis function neural network (RBFN), which is a low computational complexity deep-learning method (RBFN-PCA) in forecasting the AOFP for shale gas wells. This method...

... Abstract The rise of modern machine learning has inspired many applications in various fields including petroleum. Many researchers have recently tried utilizing machine learning in general and deep learning in specific for petroleum production time series forecast. One challenge of this task...

...: first, using 3D models substantially increases the memory requirements of the computational framework; second, neural network design becomes increasingly challenging due to the higher number of parameters in the model and its larger training time. We utilize distributed deep learning techniques in order...