Physics-Guided Machine Learning for Predicting Critical Salt Concentration and pH in Sandstone Reservoirs.

Fines migration is a critical formation-damage mechanism in sandstone reservoirs, causing permeability reduction and injectivity loss during water flooding and injection operations. The onset of fines mobilization is governed by various key physicochemical thresholds, including critical salt concentration (CSC) and critical pH. These parameters control the balance between attractive and repulsive surface forces at the fine-brine-rock interface through electrical double-layer expansion. Although Derjaguin-Landau-Verwey-Overbeek (DLVO)-based modeling provides mechanistic insight, high-throughput application requires repeated calculations and extensive system-specific electrokinetic characterization. This study presents a physics-informed machine-learning framework that provides rapid and accurate predictions of both CSC and critical pH directly from reservoir, brine, and mineralogical descriptors. The main contribution is the development and systematic evaluation of machine-learning surrogate models capable of reproducing DLVO-derived fines-mobilization thresholds without repeated electrokinetic calculations. Nine ensemble algorithms were evaluated and optimized via Bayesian hyperparameter tuning using Optuna. Model performance was assessed using