Investigating the performance of proxy and artificial intelligence models to optimize the injection and production strategy in modified Punq-S3 benchmark reservoir under water flooding operation

Investigating the performance of proxy and artificial intelligence models to optimize the injection and production strategy in modified Punq-S3 benchmark reservoir under water flooding operation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigating the performance of proxy and artificial intelligence models to optimize the injection and production strategy in modified Punq-S3 benchmark reservoir under water flooding operation Yaser Abdollahfard, Jalal Fahimpour, Mohammad Ahmadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6234141/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 18 You are reading this latest preprint version Abstract Today, even after decades of oil production from hydrocarbon reservoirs and practicing various EOR methods in the industry, water flooding is still one of the most widely used enhanced recovery techniques. Because real reservoir models are complex and their implementation leads to high computational cost and is time-consuming, hence researchers use artificial intelligence and neural network models as proxy models to solve this problem. The use of proxy models solves the problem of high computational cost and time consumption, but due to the uncertainty in predicting the behavior of the reservoir, it becomes a challenge. In this study, considering the recent challenge, the performance of proxy models has been investigated. To investigate this issue, a Punq-S3 benchmark reservoir model was selected to optimize well control parameters and maximize the net present value, and optimization was carried out using two approaches: optimization of control parameters of production and injection wells using proxy models and optimization using a real reservoir model. Three deep learning algorithms (ANN, LSTM and GRU) and two design of experiment method (Taguchi and Latin hypercube sampling) were used to create six proxy models. The particle swarm optimization algorithm was selected to optimize the injection and production strategy for the two approaches, and after optimization and comparison of the results, it was observed that despite the high accuracy of the proxy models, the optimal states in the proxy model-based methods and the real reservoir model-based method were completely different from each other, and the actual net current values ​​for these three cases indicate that the proxy models cannot accurately predict the optimization behavior. The use of proxy models cannot necessarily be reliable and trustworthy. Despite the existing challenge, by examining and analyzing proxy models and optimization results, proxy models with higher reliability can be created, and it was concluded that using the Taguchi design of experiment method and the use of artificial neural networks can achieve more reliable proxy models. Earth and environmental sciences/Solid earth sciences/Geology Physical sciences/Physics/Fluid dynamics Water flooding optimization proxy models net present value deep learning and particle swarm optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 24 May, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviews received at journal 17 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 21 Mar, 2025 Editor invited by journal 18 Mar, 2025 Submission checks completed at journal 16 Mar, 2025 First submitted to journal 15 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6234141","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435226280,"identity":"5a1771b8-af8f-454f-99e0-ec7e73730b3a","order_by":0,"name":"Yaser Abdollahfard","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yaser","middleName":"","lastName":"Abdollahfard","suffix":""},{"id":435226283,"identity":"ea6f0bc6-e9c1-4476-9b45-b128d08b6934","order_by":1,"name":"Jalal Fahimpour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBAC9gYehgNQFlSImYAWngMwLTxA6gCxWiBAIgGqhRDgYT978NDNHTaJ22c+fib9gcFOnoGd9wF+LTx5CYdzz6QlzrmdZiZxgCHZsIGZ3QCvFnuGHIPDuW2HE2dIJ4C0MCcwMLMRcBj/G6gWyePfgFrqidAiAbNFggdky2FitIBsOZNmPIMnp9jijMFxwzbCDssx/py7w0Z2BvvxjTcqKqrl+fmP4dcCBowNMBYwrAjYgaFlFIyCUTAKRgEWAACTHT/hWE3LPwAAAABJRU5ErkJggg==","orcid":"","institution":"Heriot-Watt University","correspondingAuthor":true,"prefix":"","firstName":"Jalal","middleName":"","lastName":"Fahimpour","suffix":""},{"id":435226284,"identity":"d7cc63aa-d5c3-4f7d-a975-4d2506d4af8a","order_by":2,"name":"Mohammad Ahmadi","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Ahmadi","suffix":""}],"badges":[],"createdAt":"2025-03-15 17:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6234141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6234141/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-19256-4","type":"published","date":"2025-10-10T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93419695,"identity":"cf12a8f7-d994-4c39-b564-91d5c0f02a64","added_by":"auto","created_at":"2025-10-13 16:06:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2594065,"visible":true,"origin":"","legend":"","description":"","filename":"WaterfloodingOptimizationrev05jf1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6234141/v1_covered_37530be4-7e75-42ca-b891-053384c307a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the performance of proxy and artificial intelligence models to optimize the injection and production strategy in modified Punq-S3 benchmark reservoir under water flooding operation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Water flooding optimization, proxy models, net present value, deep learning, and particle swarm optimization","lastPublishedDoi":"10.21203/rs.3.rs-6234141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6234141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eToday, even after decades of oil production from hydrocarbon reservoirs and practicing various EOR methods in the industry, water flooding is still one of the most widely used enhanced recovery techniques. 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