The growing demand for rare earth elements (REEs) in high-tech and green energy sectors has prompted renewed exploration of unconventional sources. Drill cuttings, which are commonly discarded during subsurface drilling, are increasingly recognized as a potentially valuable, underutilized secondary REE reservoir. This review adopts a mineral-first lens to assess REE occurrence, extractability, and recovery strategies from drill cuttings across various lithologies. Emphasis is placed on how REEs associate with specific mineral host phases ranging from ion-adsorbed clays and organically bound forms to structurally integrated phosphates, each dictating distinct leaching pathways. The impact of drilling fluids on REE surface chemistry and mineral integrity is critically examined, alongside an evaluation of analytical and extraction methods tailored to different host phases. A scenario-based qualitative techno-economic assessment and a novel decision-tree framework are introduced to align mineralogy with optimal recovery strategies. Limitations in prior studies, particularly in characterization workflows and mineralogical misalignment in leaching protocols, are highlighted. This review redefines drill cuttings from industrial waste to a strategic resource, advocating for mineralogically guided extraction approaches to enhance sustainability in the critical mineral supply chain.

Predicting post-acidizing porosity and permeability in sandstone reservoirs remains a fundamental challenge due to complex, nonlinear acid–rock interactions. This study presents a physics-augmented machine learning and deep learning (ML/DL) framework that integrates 24 laboratory core-flooding measurements from the Lower Indus Basin, Pakistan, with 500 synthetic training samples generated by the Panga–Lemerle–Balakotaiah (PLB) two-scale continuum simulator. Eight ML/DL architectures are benchmarked on the augmented test set (n = 75): Support Vector Regression (SVR) achieves the highest porosity R $$^{2}$$ = 0.927 and Random Forest the highest permeability R $$^{2}$$ = 0.907. A systematic domain-gap analysis reveals that naive median imputation of the unknown HCl concentration causes catastrophic external validation failure (porosity R $$^{2}$$ =−2.34). Five targeted remediation strategies are evaluated. Strategy S4—Optuna-tuned LightGBM with Leave-One-Out cross-validated joint augmented+experimental training—raises external R $$^{2}$$ to 0.918 (porosity) and 0.841 (permeability), representing improvements of +3.25 and +2.18 R $$^{2}$$ units over baseline. SHAP interpretability analysis identifies HCl concentration, initial permeability, and the Damköhler number as collectively governing >75% of prediction variance. The framework provides a physically interpretable, calibration-ready forecasting tool for pre-treatment petrophysical prediction aligned with sustainable low-concentration acidizing design in accordance with UN SDG 7 and SDG 12.