... intelligence hoteit deep learning norway government prediction bayesian mcmc bayesian inversion markov chain monte carlo kwak spe annual technical conference history matching neural network information accuracy geothermal reservoir bilstm Introduction A fast and large-scale transition...

... gas field in New Zealand. The framework evaluates reservoir parameters, such as temperature, pressure, salinity and hydrogen injection volumes as well as duration, and then classifies which reactions may take place as well as indicates the likelihood of the reaction taking place. For the deep learning...

... calculation deep learning equation of state calculation oil shale complex reservoir artificial intelligence algorithm dataset mlp shale reservoir multi-layer perceptron pr-c eos pr eos application pvt measurement reservoir simulation shale gas equilibrium machine learning pinn node peng...

... upstream oil & gas artificial intelligence autoencoder latent representation dimension neural network machine learning reduction permeability deep learning reservoir characterization ensemble reconstruction implementation realization petroleum science application reparameterization...

... model with the efficacy of deep learning, model calibration can be done very efficiently without constructing a geologic model. The proposed hybrid models with physics-based regularization and preprocessing provide novel approaches to augment data-driven models with underlying physics to build...

... the finite difference method. The performance exhibits the potential of the proposed deep learning model for hydraulic-geomechanical processes simulation. The predicted pressure field displays a high degree of accuracy up to 95%, while the error in stress prediction is slightly higher due to the limitation...

.... united states government fluid dynamics neural network deep learning artificial intelligence matrix machine learning koopman operator simulation proxy algorithm upstream oil & gas flow in porous media equation prediction architecture trajectory saturation dynamic mode decomposition...

... Abstract This paper presents a new deep learning-based parameterization approach for model calibration with two important properties: spatial adaptivity and multiresolution representation. The method aims to establish a spatially adaptive multiresolution latent space representation...

... to no-flow boundary. However, in complex fractures scenario its accuracy is limited to constant pressure boundary conditions. We also found that mixed and adaptive activation functions improve the performance of PIML for modeling complex fractures and fluxes. flow in porous media deep learning...

... deep-learning reduced-order model as a surrogate model for subsurface flow production forecast. The implemented deep learning model is a physics-guided encoder-decoder, constructed based on the Embed-to-Control (E2C) framework. In our implementation, the E2C works in a way analogous to Proper...

... Abstract We present our progress on the application of physics informed deep learning to reservoir simulation problems. The model is a neural network that is jointly trained to respect governing physical laws and match boundary conditions. The methodology is hereby used to simulate a 2-phase...

... applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative...

... Abstract The use of deep-learning-based procedures for geological parameterization and fast surrogate flow modeling may enable the application of rigorous history matching algorithms that were previously considered impractical. In this study we incorporate such methods – specifically...

... upstream oil & gas prediction shape factor model closure fracture geometry loss function dataset permeability deep learning training dataset lägerdorf dataset upscaled parameter fine-scale simulation neural network accuracy random linear fracture configuration fracture architecture...

... Abstract We present a deep learning architecture for efficient reduced-order implementation of ensemble data assimilation. Specifically, deep learning is used to improve two important aspects of data assimilation workflows: (i) low-rank representation of complex reservoir property distributions...

... history matching for subsurface flow modeling. flow in porous media neural network history matching upstream oil & gas artificial intelligence deep learning dimensionless time posterior distribution high-fidelity model workflow latin hypercube long-short term memory accuracy...

... a method to calculate matrices A r t , B r t , C r t r and D r t based on deep learning, using state snapshots as training data. Jin, Liu and Durlofsky (2020 ) extended this idea by adding a physical loss function related to reservoir simulation. Here...

... it in a compositional simulator to replace the traditional flash calculation module, speeding the simulation by 30%. deep learning Phase Behavior machine learning PVT measurement proxy neural network dimension flash calculation phase classification step preconditioner input parameter calculation module...

... configuration time-sery data steam-assisted gravity drainage shale barrier true model matrix temperature profile deep learning observation well input feature realization production profile machine learning neural network steam chamber Reservoir Heterogeneity configuration cnn model temperature...

... Abstract The objective of this work is to design novel multi-layer neural network architectures for simulations of multi-phase flow taking into account the observed data (e.g., production data) and physical modeling concepts. Our approaches use deep learning concepts combined with model...