arXiv:2603.11250v2 Announce Type: replace-cross Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries. However, such modeling remains challenging due to strong nonlinear behavior and uncertainty in model parameters. In particular, gas slippage effects described by the Klinkenberg model introduce pressure-dependent permeability, which complicates numerical simulation and obscures deviations from classical Darcy flow behavior. To addre
The increasing sophistication of machine learning techniques is enabling their application to complex physics problems, such as nonlinear gas flow, which were previously difficult to model accurately.
Improved modeling of gas flow in porous media has significant implications for resource extraction, carbon sequestration, and energy storage technologies, directly impacting the energy sector and climate initiatives.
Machine learning offers a more efficient and accurate method for simulating complex gas flow behaviors, potentially accelerating development and optimization in critical industrial applications.
- · Oil & Gas Industry
- · Carbon Capture & Storage Sector
- · Fuel Cell and Battery Manufacturers
- · Computational Fluid Dynamics (CFD) Software Developers
- · Traditional Numerical Simulation Methods
Enhanced efficiency and reliability in engineering solutions for energy and environmental applications.
Accelerated development of new technologies dependent on precise porous media flow understanding, leading to new market opportunities.
Potentially lower costs and higher yields in energy production and storage, influencing global energy security and supply.
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