.... 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...

...-constrained and standard DL model results to quantify the ability of our approach to honor physical constraints. The evaluation of our physics-informed neural network (PINN) model compared to a standard DL model shows that we can incorporate physical constraints without a considerable reduction in model...

.... 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...

..., and versatile machine learning (ML) methods in a predictive study. Apparent viscosity for NP-CO2 foam is determined while considering all input parameters. In this work, we compare the performance of four data-driven non-linear ML algorithms: Multilayer Perceptron Neural Network, Support Vector Regression, K...

... geometry parameters and bottom hole pressures. Feedforward and recurrent neural networks are trained on historical production data to predict future responses. The physics-constrained model is compared with the purely data-driven model in several scenarios that have different data quality and boundary...

...: 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...

... Reservoir Engineering. SPE Text Book Series , 5 Texas . He , Z. , Yang , L. , Yen , J. , and Wu , C. 2001 . Neural-Network Approach To Predict Wel Performance Using Available Field Data . Paper SPE 68801, presented at the SPE Western Regional Meeting , Bakersfield, California...

... production monitoring Reservoir Surveillance production control coalbed methane neural network coal seam gas well production logging complex reservoir regression coal bed methane Drillstem Testing drillstem/well testing machine learning Artificial Intelligence bottom-hole pressure...

... for successful conventional petroleum exploration. However, this is not such a major impediment to economic production for unconventional prospects. shale gas Reservoir Characterization machine learning complex reservoir neural network Artificial Intelligence Upstream Oil & Gas iii shale unit...