{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:26:10Z","timestamp":1782933970553,"version":"3.54.5"},"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100023674","name":"Deanship of Scientific Research, King Khalid University","doi-asserted-by":"publisher","award":["RGP.2\/209\/46"],"award-info":[{"award-number":["RGP.2\/209\/46"]}],"id":[{"id":"10.13039\/501100023674","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016152","name":"Yayasan UTP","doi-asserted-by":"publisher","award":["YUTP-PRG 015PBC-028"],"award-info":[{"award-number":["YUTP-PRG 015PBC-028"]}],"id":[{"id":"10.13039\/501100016152","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Reliable prediction of CO\n                    <jats:sub>2<\/jats:sub>\n                    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\u2013Landau\u2013Verwey\u2013Overbeek (DLVO) colloidal monotonicity condition and the Civan\u2013Kozeny\u2013Carman permeability impairment model. Four GP variants are developed and benchmarked on a curated laboratory dataset (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200944) under a three-tier validation protocol combining Leave-One-Out cross-validation, repeated\n                    <jats:italic>k<\/jats:italic>\n                    -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\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.9401, 95% CI: [0.882, 0.978]) with well-calibrated uncertainty (ECE\u2009=\u20090.026) 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 &gt;30,000 ppm and jamming ratio &gt;0.04. These results provide an uncertainty-aware baseline for future PIML research in subsurface carbon storage.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0352178","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:43:22Z","timestamp":1782927802000},"page":"e0352178","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-informed gaussian process regression for reproducible and uncertainty-aware CO2 injectivity prediction"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4015-1723","authenticated-orcid":true,"given":"Shamsuddeen","family":"Adamu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitham","family":"Alhussian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said Jadid","family":"Abdulkadir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majdy Mohamed","family":"Eltayeb Eltahir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sallam O. F.","family":"Khairy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gasim","family":"Hayder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi Ali","family":"Lathbl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Salisu Mohammed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulrazak Oladeji","family":"Adekunle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gbolagade","family":"Kamaldeen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samaila Musa","family":"Abdullahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahaya","family":"Saidu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"pone.0352178.ref001","article-title":"Climate Change 2022: Mitigation of Climate Change","volume-title":"Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change","author":"IPCC","year":"2022"},{"key":"pone.0352178.ref002","volume-title":"IPCC Special Report on Carbon Dioxide Capture and Storage","author":"B Metz","year":"2005"},{"issue":"2","key":"pone.0352178.ref003","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.pecs.2007.10.001","article-title":"CO2 storage in geological media: role, means, status and barriers to deployment","volume":"34","author":"S Bachu","year":"2008","journal-title":"Progress Energy Combustion Sci"},{"issue":"1","key":"pone.0352178.ref004","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11242-010-9557-0","article-title":"Non-isothermal permeability impairment by fines migration and deposition in porous media including dispersive transport","volume":"85","author":"F Civan","year":"2010","journal-title":"Transp Porous Med"},{"key":"pone.0352178.ref005","doi-asserted-by":"crossref","unstructured":"Mardhatillah MK, Yusof MA, Andhika A, Universiti Teknologi Petronas. Predictive Modelling of Carbon Dioxide Injectivity Using SVR-Hybrid. In: Proceedings of the Offshore Technology Conference Asia; 2022. pp. 1\u201315. https:\/\/doi.org\/10.4043\/31472-MS","DOI":"10.4043\/31472-MS"},{"issue":"546","key":"pone.0352178.ref006","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1080\/01621459.2023.2197686","article-title":"Cross-validation: what does it estimate and how well does it do it?","volume":"119","author":"S Bates","year":"2024","journal-title":"J Am Stat Assoc"},{"key":"pone.0352178.ref007","unstructured":"Adamu S, Alhussian H, Jadid S, Khairy SOF. 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