... upstream oil & gas drilling operation bit design artificial intelligence machine learning neural network calgary bit selection strength reservoir geomechanics efficiency predictorname trainregressionmodel hardness compressive strength variablename correlation predictfcn...

... intelligence reservoir characterization gamma ray geophysics lithology neural network deep learning log analysis prediction sequence upstream oil & gas well log prediction deep sequence learning well logging neutron porosity machine learning application information dts journal...

... being the synthetic maximum borehole diameter (Cmax_P) was compared with the measured caliper log. The model was trained with various machine learning algorithms including ensemble algorithms such as XGBoost and recurrent neural network (RNN). In the training process, Leave-One-Out method...

... gradient drilling operation neural network upstream oil & gas decision tree learning wellbore design well logging machine learning abdulraheem estimation arabian journal artificial intelligence reservoir characterization petroleum engineer society johnson wellbore integrity...

...ARMA/DGS/SEG International Geomechanics Symposium 21 0021 In-situ stresses prediction by using a CNN-BiLSTM-Attention hybrid neural network Tianshou Ma*, Guofu Xiang State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan, China...

...., 2016; Gamal et al., 2021a; Gowida et al., 2021), and sonic time prediction (Anemangely et al., 2019a, 2017). By employing machine learning techniques, a recurrent neural network (RNN) with nonlinear autoregressive with exogenous (NARX) inputs was employed to predict Vs and Vp utilizing such inputs...

... (NPHI), resistivity (RDEP), caliper (CALI), spontaneous potential (SP), gamma ray (GR) and density (RHOB) were collected and then models for Vs prediction were developed using multi-variable linear regression (MLR), multi-variable polynomial regression (MPR), empirical models, deep neural network (DNN...

... network architectures, respectively: 1) U-Net for image-to-image problems, 2) Convolutional Neural Network (CNN) for image-to-value problems, 3) Artificial Neural Network (ANN) for valueto-value problems, 4) Long Short-Term Memory (LSTM) for time series problems. ANN model with time as input also could...

...ARMA/DGS/SEG International Geomechanics Symposium 21 40 Investigating the Efficiency of Neural Networks in Predicting Breakdown Pressures for Tight Gas Formations Tameem Almani, Khaqan Khan, and Mohammad Altwaijri Saudi Aramco, Dhahran, Saudi Arabia Copyright 2021 ARMA, American Rock Mechanics...

... this approach, artificial neural network (ANN) machine learning algorithm was used. ANN is very common artificial intelligence method that could be used in regression or classification problems. 2288 data points were used to construct and test the model, while another 1667 data points were hidden from...

... in some wells are missing or incomplete, possibly for cost and time-saving purposes. To overcome these challenges, this study presented two developed models for estimating the shear wave velocity and pore pressure using the neural network (ANNs). A calibration was then performed to check the new models...

... upstream oil & gas elkatatny aape petroleum engineer application mahmoud wellbore integrity log analysis wellbore design drilling parameter pore pressure estimation neural network machine learning ahmed abdelaal pore pressure gradient salaheldin elkatatny abdulazeez abdulraheem ARMA...

... machine learning hydraulic fracturing shale gas prediction performance upstream oil & gas mineralogical percentage artificial neural network brittleness index correlation composition mineralogical composition artificial intelligence neural network new model elkatatny...