Imagen de Portada

Dynamic Mode Decomposition Accelerated Forecast and Optimization of Geological CO2 Storage in Deep Saline Aquifers

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Voulanas, Dimitrios, Gildin, Eduardo
Formato:	Preprint
Publicado:	2024
Materias:	Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Updating DMD Operators for Changes in Domain Properties
por: Voulanas, Dimitrios, et al.
Publicado: (2025)

Introducing Coherent-Control Koopman Modeling to Reservoir Scale Porous Media Flow Studies
por: Voulanas, Dimitrios, et al.
Publicado: (2025)

Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learning
por: Chen, Jungang, et al.
Publicado: (2024)

Geological Screening for CO2 Storage in Deep Saline Aquifers in the Lower Mainland British Columbia (LMBC), Canada
por: Maziyar Nazemi, et al.
Publicado: (2025)

Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
por: Jordanou, Jean Panaioti, et al.
Publicado: (2025)

The Potential of Horizontal Wells for Aquifer Storage and Recovery in Saline Aquifers
por: Simon Kreipl, et al.
Publicado: (2026)

Paradigm for Determining the Optimal Ultradeep and Super‐thick Saline Aquifer for High‐TDS Mine Water Geological Storage
por: Xin LI, et al.
Publicado: (2024)

Accelerating Particle-in-Cell Kinetic Plasma Simulations via Reduced-Order Modeling of Space-Charge Dynamics using Dynamic Mode Decomposition
por: Nayak, Indranil, et al.
Publicado: (2023)

Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
por: Velegar, Meghana, et al.
Publicado: (2024)

Identifying Potential Geochemical and Microbial Impacts of Hydrogen Storage in a Deep Saline Aquifer
por: Kara A. Tinker, et al.
Publicado: (2025)

Self-Enhancing Halite Growth Creates Secondary Porous Networks During CO2 Storage in Saline Aquifers
por: Nooraiepour, Mohammad, et al.
Publicado: (2025)

Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology
por: Gahlot, Abhinav Prakash, et al.
Publicado: (2025)

Kinetically Consistent Coarse Graining using Kernel-based Extended Dynamic Mode Decomposition
por: Nateghi, Vahid, et al.
Publicado: (2024)

U-DeepONet: U-Net Enhanced Deep Operator Network for Geologic Carbon Sequestration
por: Diab, Waleed, et al.
Publicado: (2023)

Accelerated decomposition of bistochastic kernel matrices by low rank approximation
por: Vales, Chris, et al.
Publicado: (2025)

A Generalized Expression for Accelerating Beamlet Decomposition Simulations
por: Ashcraft, Jaren N., et al.
Publicado: (2024)

Machine Learning–Based Estimation of Hydrogen Solubility in Brine for Underground Storage in Saline Aquifers
por: Fahd Mohamad Alqahtani, et al.
Publicado: (2025)

Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
por: Chen, Jungang, et al.
Publicado: (2023)

A parallel-in-time method based on the Parareal algorithm and High-Order Dynamic Mode Decomposition with applications to fluid simulations
por: Liu, Weifan
Publicado: (2025)

Surrogate Model for Geological CO2 Storage and Its Use in Hierarchical MCMC History Matching
por: Han, Yifu, et al.
Publicado: (2023)

Deep Learning-Accelerated Dynamic Kinetic Monte Carlo Simulation for Hydrogen Transport in Tungsten
por: Saito, Seiki, et al.
Publicado: (2026)

Multi-Step Embed to Control: A Novel Deep Learning-based Approach for Surrogate Modelling in Reservoir Simulation
por: Chen, Jungang, et al.
Publicado: (2024)

Featurizing Koopman Mode Decomposition For Robust Forecasting
por: Aristoff, David, et al.
Publicado: (2023)

Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields
por: Badawi, Daniel, et al.
Publicado: (2024)

Extending SST Anomaly Forecasts Through Simultaneous Decomposition of Seasonal and PDO Modes
por: Kallummal, Rameshan
Publicado: (2026)

Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO$_2$ to Methanol Conversion
por: Pisal, Prajwal, et al.
Publicado: (2024)

Towards Quantum Accelerated Large-scale Topology Optimization
por: Ye, Zisheng, et al.
Publicado: (2025)

Data-Driven Reconstruction and Characterization of Stochastic Dynamics via Dynamical Mode Decomposition
por: Baratz, Adva, et al.
Publicado: (2025)

Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling
por: Gahlot, Abhinav Prakash, et al.
Publicado: (2025)

Impact of Wettability on CO 2 Mineral Trapping in Carbonate Saline Aquifers: A Reactive Transport Simulation Study
por: Reza Khoramian, et al.
Publicado: (2025)

Probabilistic Upscaling of Hydrodynamics in Geological Fractures Under Uncertainty
por: Perez, Sarah, et al.
Publicado: (2026)

Optimizing Quantum Transformation Matrices: A Block Decomposition Approach for Efficient Gate Reduction
por: Man, Lai Kin, et al.
Publicado: (2024)

RETRACTION : Artificial Intelligence‐based Wind Forecasting Using Variational Mode Decomposition
Publicado: (2026)

N-Mode Quantized Anharmonic Vibronic Hamiltonians for Matrix Product State Dynamics
por: Barandun, Valentin, et al.
Publicado: (2025)

Accelerating Porous Media Flow Simulations With Fourier Neural Operators: An Application to Geologic Storage of CO 2
por: Anirban Chandra, et al.
Publicado: (2025)

Accelerating Molecular Dynamics through Informed Resetting
por: Church, Jonathan R., et al.
Publicado: (2024)

Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics
por: Meinecke, Stefan, et al.
Publicado: (2024)

Exponential Spectral Scaling: Robust and Efficient Stellarator Boundary Optimization via Mode-Dependent Scaling
por: Jang, Byoungchan, et al.
Publicado: (2025)

Phase Dynamics of Self-Accelerating Bose-Einstein Condensates
por: Pellner, Maximilian L. D. D., et al.
Publicado: (2026)

Synthetic Geology: Structural Geology Meets Deep Learning
por: Ghyselincks, Simon, et al.
Publicado: (2025)