{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:58:32Z","timestamp":1778896712051,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013000","name":"Politecnico di Torino","doi-asserted-by":"publisher","award":["1490"],"award-info":[{"award-number":["1490"]}],"id":[{"id":"10.13039\/100013000","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EDISU","award":["1490"],"award-info":[{"award-number":["1490"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Machine learning-based Surrogate Reservoir Models (SRMs) can replace\/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs\u2019 potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3\u20135 hidden layers with 128\u2013512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations\u2019 safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2.<\/jats:p>","DOI":"10.3390\/a17100452","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:10:16Z","timestamp":1728634216000},"page":"452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2794-5569","authenticated-orcid":false,"given":"Behzad","family":"Amiri","sequence":"first","affiliation":[{"name":"Department of Energy Resources, University of Stavanger, 4021 Stavanger, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0270-0313","authenticated-orcid":false,"given":"Ashkan","family":"Jahanbani Ghahfarokhi","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Norwegian University of Science and Technology, 7031 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-9388","authenticated-orcid":false,"given":"Vera","family":"Rocca","sequence":"additional","affiliation":[{"name":"Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuthbert Shang Wui","family":"Ng","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Norwegian University of Science and Technology, 7031 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","unstructured":"UNFCCC (2015, January 12). 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