Physics-Informed Machine Learning for CO2 Solubility in Brines: Robustness to Noise and Out-Of-Distribution Conditions
Abstract
Accurate prediction of CO2 solubility in formation brines is central to carbon storage design because dissolution trapping reduces CO2 mobility and supports long term containment. Yet, solubility data and correlations are often limited in coverage, uncertain at high salinity and pressure, and can be unreliable when extrapolated beyond the calibration range. This work develops a physics-informed benchmarking framework that evaluates when machine learning (ML) models provide reliable CO2 solubility predictions under reservoir relevant conditions, and when established physics-based correlations remain the safer choice. A physics-informed CO2 brine dataset was generated over geologically realistic ranges that represent deep saline reservoirs at approximately 4,900 to 13,000 ft depth, spanning 35 to 347 bar, 285 to 430 K, and 0 to 259 g/L salinity. Ground-truth solubilities were produced using Henry's law with van't Hoff temperature dependence and a Setchenov salting-out correction, then supplemented with fugacity and activity-coefficient adjustments to address non-ideal behavior at higher pressure and salinity. A calibrated non-ideality term was included to preserve physically consistent monotonic trends across the full Pressure-Temperature-Salinity(P–T–S) space. To emulate laboratory uncertainty without changing the sampled inputs, controlled zero-mean Gaussian noise of 1%, 3%, 5%, and 10% relative standard deviation was applied to solubility targets. Three ML models, Linear Regression, Random Forest, and a three-layer MLP (64-32-16, ReLU), were trained using identical feature sets (P, T, S), standardized preprocessing, and consistent train, validation, and test splits. Model performance was evaluated using R2, MAE, RMSE, k-fold cross-validation, parity and residual diagnostics, calibration curves, and bootstrap uncertainty estimates. Out-of-distribution (OOD) robustness was quantified by withholding a high-salinity band (S > 200 g/L) and a low-temperature band (T < 300 K) from training to test generalization in regimes relevant to storage screening. The resulting workflow provides a practical, physics-consistent basis for selecting solubility predictors and defining reliable application envelopes for ML in CCUS studies.