38-10 Physics-Informed Neural Network Framework for Modeling Flow in Dual-Pore Porous Media

Session: Geoscience and Hydrogeology in the AI Era: From Predictive Models to Real-Time Applications

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Abstract:

Porous media span a remarkably diverse range of systems—including fractured carbonate reservoirs, ultra-tight shale formations, architected metallic foams, tissue-engineering scaffolds, and even plant xylem conduits. In many of these environments, flow and transport processes are governed by two hierarchically distinct but hydraulically coupled pore domains: a larger, highly permeable network that serves as the main conduit for flow, and a finer, more tortuous micro-pore network that controls storage, diffusion, and exchange dynamics. To accurately capture the resulting multiscale mass and solute transfer, the double-porosity/permeability (DPP) framework offers a powerful modeling approach. However, classical discretization methods—such as finite volume, finite difference, or finite element techniques—are often inadequate for workflows requiring rapid scenario screening, real-time data assimilation, or high-dimensional inverse analysis under sparse and noisy observations. In this talk, we introduce a physics-informed neural network (PINN) framework tailored to address these limitations. The method employs a shared neural backbone through which residual-balancing dynamically reconciles competing physical constraints. An error-guided collocation strategy further enhances accuracy by adaptively concentrating sampling points near steep pressure gradients and evolving flow fronts. Representative numerical results demonstrate that this mesh-free formulation offers multiple performance advantages: it resolves discontinuities without artificial diffusion, maintains numerical stability under extreme permeability contrasts, and avoids the spurious oscillations commonly observed in traditional mixed-element schemes.A particularly attractive feature of the proposed framework is its adaptability: the network can be incrementally retrained as new laboratory measurements, wireline logs, or in-situ sensor data become available. This enables continuously updated forecasting capabilities for critical applications such as critical-mineral extraction, tight-shale production, CO₂ and hydrogen storage integrity, and geothermal heat-exchange optimization.

Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-9522

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