... advances in deep learning based on deep neural networks, convolutional neural networks, and autoencoders to create machine-learning-based proxy models that predict production and injection profiles as well as the bottomhole pressure of all wells. Thus, the proposed workflows replace the time-consuming...

..., which makes the method suitable for images with large size. The paper provides a way to develop an alternative approach of PNM simulation method for permeability prediction from CT images. upstream oil & gas neural network fluid dynamics deep learning permeability asia government...

... is that the former uses a regularization parameter, which is used to lower the error by fitting a function properly and avoid overfitting. XGBoost works at its most efficient performance when there are multiple input parameters in the dataset. Neural Networks The neural network model is one of the most used...

... government machine learning linear regression empirical correlation journal coefficient composition bubble point pressure proceedings neural network petroleum technology crude oil deviation reservoir interactive multivariate linear regression INTRODUCTION R s = C 1 × G...

... as possible. reservoir optimization problem history matching neural network upstream oil & gas reservoir simulation artificial intelligence machine learning boundary evolutionary algorithm north kuwait criteria asia government kuwait government history workflow permeability complex...

... engineer neural network machine learning concentration fno kaolinite co 2 permeability tariq exhibition simulator subsurface storage climate change society mole fraction abdulraheem mineral prediction anorthite Introduction Mineral trapping is recognized as the most secure carbon...

... of a reservoir simulator. However, tree-based methods such as random forests and gradient boosting are widespread. Castelletti et al. 2010 ( Castelletti et al., 2010 ), most studies in this area have employed artificial neural networks (ANN) as the learning algorithm ( Ahmadi et al. (2013) ( Ahmadi et al., 2013...

...Deep Neural Networks (DNNs) Processor: Intel(R) Xeon(R) Gold 6132 CPU @ 2.600GHz 2.59 GHz RAM: 64.0 GB CPU: 24 cores for parallel run The equilibration result perfectly matched the black oil model and the compositional model. The black oil fine model takes 186,335 seconds...

.... asphaltene inhibition upstream oil & gas neural network oilfield chemistry artificial intelligence uae government composition scale inhibition equation of state wax inhibition scale remediation machine learning remediation of hydrates asia government hydrate inhibition envelope enhanced...

... of compositional reservoir simulation. asia government calculation neural network machine learning upstream oil & gas china government pvt measurement artificial intelligence prediction molar composition reduction diagram accuracy simulation application fraction mole fraction procedure...

... as a workflow with any existing reservoir simulator for optimal WAG parameters to maximize reservoir life cycle cumulative recoveries. The reservoir simulator is treated as a sample generator to form an ensemble of recovery scenarios with the WAG parameters as inputs to a dense neural network (DNN) and outputs...

..., SOBOL analysis, and MORRIS analysis. For generating the related proxy models, polynomial regression, and radial basis function (RBF) neural networks were investigated. Subsequently, the DECE-based and PSO-based optimization methods were employed to examine the effect of chemical design parameters...

... & gas petroleum engineer fluid dynamics structural geology neural network social responsibility modeling & simulation drillstem/well testing united states government reservoir simulation subsurface storage norway government deep learning drillstem testing europe government air...

.... A robust forward surrogate model f l is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid...

... of the solution is represented by powerful machine learning methods such as Generalized Additive Models (GAM), Gradient Boosting, and Convolutional and Recurrent Neural Networks. Neural Networks and Gradient Boosting methods are very popular machine learning techniques. However, in this work, it is demonstrated...

... applications. upstream oil & gas artificial intelligence gradient neural network npv united states government optimization iteration prediction denote convergence criteria stosag simulation constraint algorithm reynolds ls-svr proxy deep learning machine learning approximate...

... consuming to update history match with all the extra inputs and forecast; and optimizing the development with all the input parameters within a short timeframe is always a challenge. The process employed in this approach was based on deep learning artificial neural networks (ANN) coupled with numerical...

... and can be used as a quick assessment tool to evaluate the long term feasibility of CO 2 movement in fractured carbonate medium. asia government upstream oil & gas petroleum engineer neural network artificial intelligence complex reservoir denmark government deep learning hydraulic...

... is to compare the carbonate channel systems with modern analogue in Bahama tidal flat and outcrop scales in Wadi Mi'Aidin (Northern Oman). Thereafter, the supervised probabilistic neural network (PNN) and linear regression method were undertaken to detect an additional channel distribution. The relationship...

... from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30...