Physics-informed gaussian process regression for reproducible and uncertainty-aware CO2 injectivity prediction

Shamsuddeen Adamu, Hitham Alhussian, Said Jadid Abdulkadir, Majdy Mohamed Eltayeb Eltahir, Sallam O F Khairy, Gasim Hayder, Mahdi Ali Lathbl, Hassan Salisu Mohammed, Abdulrazak Oladeji Adekunle, Gbolagade Kamaldeen, Samaila Musa Abdullahi and Yahaya Saidu

PLOS ONE, 2026, vol. 21, issue 7, 1-23

Abstract: Reliable prediction of CO2 injectivity decline is essential for safe geological carbon storage, yet existing machine learning models often provide deterministic point predictions that lack uncertainty quantification. This paper presents a physics-informed Gaussian process regression (PC-GPR) framework for Relative Injectivity Change (RIC) prediction, embedding constraints derived from two independently grounded physical laws: the Derjaguin–Landau–Verwey–Overbeek (DLVO) colloidal monotonicity condition and the Civan–Kozeny–Carman permeability impairment model. Four GP variants are developed and benchmarked on a curated laboratory dataset (n = 44) under a three-tier validation protocol combining Leave-One-Out cross-validation, repeated k-fold cross-validation, and non-parametric bootstrap confidence intervals. Two complementary uncertainty quantification mechanisms are employed: GP posterior calibration via the Expected Calibration Error (ECE) and split-conformal prediction intervals. The GP-Base model achieves strong predictive performance (LOO R2 = 0.9401, 95% CI: [0.882, 0.978]) with well-calibrated uncertainty (ECE = 0.026) and reliable coverage (97.7% at the nominal 95% level). The PC-GPR-M variant reduces DLVO monotonicity violations to 1.5% across the input domain, demonstrating effective soft constraint enforcement. Operationally, the proposed framework translates predictive uncertainty into actionable injection scheduling guidance, identifying high-risk regions at salinity >30,000 ppm and jamming ratio >0.04. These results provide an uncertainty-aware baseline for future PIML research in subsurface carbon storage.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0352178

DOI: 10.1371/journal.pone.0352178

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