[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123119-en":3,"doc-seo-123119-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123119,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models","Machine learning-based surrogate reservoir models (SRMs) are used to replicate reservoir simulation outputs with far lower computational cost while keeping high accuracy relative to 3D numerical simulators. The study targets CO2 storage in the Smeaheia saline aquifer and builds two deep neural network SRMs to predict CO2 saturation and pressure for 50-year periods across all grid blocks. Training relies on 18 million samples with 31 static and dynamic input features, using 3–5 hidden layers. Results show about 300× runtime improvement and 99% accuracy, followed by genetic-algorithm optimization of injection rate and duration under operational safety constraints.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nOptimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models  \nOriginal  \nOptimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models / Amiri, Behzad; Jahanbani Ghahfarokhi, Ashkan; Rocca, Vera; Shang Wui Ng, Cuthbert. -In: ALGORITHMS. -ISSN 1999-4893. -ELETTRONICO. -17:10(2024) . [10 .3390/a17100452]  \nAvailability:  \nThis version is available at: 11583/2993378 since: 2024-10-14T08:02:09Z  \nPublisher: MDPI  \nPublished  \nDOI:10.3390/a17100452  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n17 February 2025  \n algorithms  \nArticle  \nOptimization of Offshore Saline Aquifer CO 2 Storage in Smeaheia Using Surrogate Reservoir Models  \nBehzad Amiri 1, Ashkan Jahanbani Ghahfarokhi 2, *, Vera Rocca 3 and Cuthbert Shang Wui Ng 2  \nCitation: Amiri, B.; Jahanbani Ghahfarokhi, A.; Rocca, V.; Ng, C.S.W. Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models. Algorithms 2024, 17, 452. [https://](https://)[ ](https://)[doi.org/10.3390/a17100452](doi.org/10.3390/a17100452)  \nAcademic Editors: Szymon Łukasik, Piotr A. Kowalski  \nand Rohit Salgotra  \nReceived: 9 September 2024  \nRevised: 3 October 2024  \nAccepted: 9 October 2024  \nPublished: 11 October 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Energy Resources, University of Stavanger, 4021 Stavanger, Norway; [behzad.amiri@uis.no](behzad.amiri@uis.no)  \n2 Department of Geosciences, Norwegian University of Science and Technology, 7031 Trondheim, Norway; [cuthbert.s.w.ng@ntnu.no](cuthbert.s.w.ng@ntnu.no)  \n3 Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Torino, Italy; vera.rocca@polito.it  \n* Correspondence: [ashkan.jahanbani@ntnu.no](ashkan.jahanbani@ntnu.no)  \nAbstract: Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multiphysics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs' 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 CO 2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO 2 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 3–5 hidden layers with 128–512 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 CO 2 while ensuring storage operations'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 CO 2 .  \nKeywords: geological carbon storage; surrogate reservoir model; artificial intelligence; deep learning; optimization  \n1. Introduction  \nThe main target of the Paris Agreement in December 2015 was to concentrate efforts on limiting temperature in","cbCaijPagrXoZg2L","https://ap.wps.com/l/cbCaijPagrXoZg2L","pdf",1145286,1,27,"English","en",105,"# Introduction\n## Climate-change context and CCS motivation\n## CO2 geological storage risks and optimization goals\n# Methods (SRMs and optimization workflow)\n## Deep neural network surrogate reservoir models\n## Genetic algorithm for injection rate and duration\n# Results and implications\n## Runtime and accuracy comparison with numerical simulation\n## Long-term optimization outcomes for Smeaheia storage","[{\"question\":\"What problem do surrogate reservoir models address in CO2 storage simulation?\",\"answer\":\"They replace or augment multiphysics numerical simulations by reproducing reservoir simulation results with greatly reduced computational effort while maintaining accuracy.\"},{\"question\":\"How were the surrogate reservoir models built for the Smeaheia saline aquifer?\",\"answer\":\"Two deep neural network SRMs were trained to predict CO2 saturation and pressure over all grid blocks for 50 years using 18 million samples and 31 input features.\"},{\"question\":\"What optimization method was used to determine injection strategy?\",\"answer\":\"A genetic algorithm optimized injection rate and duration to maximize injected CO2 volume while enforcing constraints to ensure safe storage operations.\"}]","Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models | 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