A physics-informed ML method embeds a differentiable flow simulator into neural network training to infer permeability from sparse pressure data, halving inference error versus data-driven baselines across scenarios and maintaining accuracy on extreme events.
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Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator
A physics-informed ML method embeds a differentiable flow simulator into neural network training to infer permeability from sparse pressure data, halving inference error versus data-driven baselines across scenarios and maintaining accuracy on extreme events.