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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212204-MS
... 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 optimization simulator decomposition manifold...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212202-MS
... intelligence machine learning journal differential equation deep learning approximation reservoir characterization physics-informed neural network simulation application wang accuracy porous media timestep permeability temporospatial prediction information architecture pinn engineering...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212212-MS
.... gradient united states government upstream oil & gas flow in porous media artificial intelligence stosag gradient algorithm approximation iteration optimization problem machine learning realization fluid dynamics waterflooding enhanced recovery permeability journal accuracy...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212167-MS
..., followed by application of the method to a travel-time tomography inverse problem to investigate its model updating performance. neural network resolution architecture united states government artificial intelligence autoencoder application spatial adaptivity deep learning machine learning...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212228-MS
... united states government co 2 evolutionary algorithm constraint procedure multifidelity optimization artificial intelligence reservoir characterization optimization configuration constraint violation durlofsky modeling & simulation enhanced recovery machine learning scaling method...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212217-MS
... of a fully implicit PDE solver directly in the neural network's loss function. The Lebesgue integral is used as a regularization function and allows the neural network to discover the operator space for which the difference in shock estimation is minimal. Our Physics-Informed Machine Learning (PIML...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212177-MS
... machine learning models learn more robust and generalizable representations of the data. Additionally, it can help to improve the interpretability of the model by identifying the specific factors that are driving the model's predictions. Such interpretability is also preferred from an expert's perspective...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212196-MS
... upstream oil & gas neural network brazil government representation assimilation machine learning deep learning reservoir characterization reduction localization permeability journal realization matrix ensemble application reparameterization united states government...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212190-MS
... modes. For the unconventional test problem, the samples are compared with the ones obtained using a Gauss Newton or iterative Ensemble Smoother methods. reservoir simulation history matching artificial intelligence machine learning united states government particle upstream oil & gas...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212207-MS
... by using a dynamic graph adaption algorithm to find a good granularity that improves predictability both inside and slightly outside the range of the training data. europe government optimization upstream oil & gas artificial intelligence node equation of state machine learning tolerance...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212184-MS
... & simulation localization reservoir simulation machine learning truth workflow geological realization gridblock correlation non-adaptive localization journal upstream oil & gas ensemble history ensemble-based history society ensemble kalman filter reynolds Introduction The non...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212188-MS
... of high-fidelity simulation cases. Such approaches are physics-based machine learning models and data-space inversion technique. To start with, a group of prior models with a vast sampling of input parameters are generated and run. Machine learning methods (e.g., support vector machine, CNN, etc...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212180-MS
... government optimization problem upstream oil & gas subroutine reservoir simulation united states government europe government machine learning matrix artificial intelligence flop gntr solver svd orthonormal matrix method algorithm converge performance indicator benchmarking iteration...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212211-MS
... simulation machine learning regression lump clustering technique compositional characterization preprocessor experiment simulation agglomerative petroleum engineer society automated lumping Where M ¯ n and M ¯ m are the average molecular weights of each lumped...
Proceedings Papers

Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212187-MS
... hydrogen energy machine learning hydrogen storage taranaki basin microorganism microbial reaction prediction Introduction Hydrogen gas has become an important energy carrier to support the energy transition. For hydrogen combutions, there are no harmful substances that are released and based...

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