Physics-informed machine-learning (PIML) techniques enhance permeability prediction in carbonate reservoirs. Accurate permeability estimation is crucial for reservoir characterization, fluid-flow modeling, and oil and gas production optimization. However, traditional empirical models and conventional ML techniques often fail to capture the complex nonlinear relationships governing permeability, particularly in heterogeneous carbonate formations. To address this challenge, the authors of this paper integrate physics-based constraints into ML models, thereby improving predictive accuracy and robustness.
