Research on Intelligent Assisted Well-Killing System for Gas Influx and Blowout

Research on Intelligent Assisted Well-Killing System for Gas Influx and Blowout | 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 Research Article Research on Intelligent Assisted Well-Killing System for Gas Influx and Blowout Pu Liu, Chuanhua Ge, Ruifeng Tan, Ruiqi Zhang, Jianguo Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6884073/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As China's oil and gas exploration and development gradually shifts to deep layers, deep seas, and unconventional sources, the formation anomalies become more complex, which poses great well control risks for oil and gas production. Well-killing is the core of well control safety technology. The current technology has technical problems such as low prediction accuracy of wellhead pressure, long control response time, and low degree of intelligence, which restricts the development of well control technology. This article systematically develops a prediction model for the development of multiphase flow in the wellbore and an analysis method for pressure response based on the well killing conditions during gas invasion and overflow. It proposes an operational logic for solving the mathematical model of dynamic response of wellbore pressure, establishes a calculation model for unconventional dynamic pending boundary, and forms a smart assistant well killing system for gas invasion and overflow. The results show that the model in this article is closer to the measured data, with a maximum error of 1.26% (0.08MPa) in wellhead casing pressure value, and the peak time is only 2 minutes ahead of the measured data. At the same time, the response time of the smart assistant well killing system is less than 2 seconds. The analysis results are close to engineering reality, and the response time of the smart assistant well killing system is timely. This has important engineering significance for reducing well control risks, promoting oil and gas resource development, and ensuring energy security. Gas invasion and overflow Multiphase flow in the wellbore Wellhead pressure prediction Well control safety technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6884073","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483718947,"identity":"7da7cd55-ea6c-433d-82b3-b587dbbb3540","order_by":0,"name":"Pu Liu","email":"","orcid":"","institution":"Sichuan University of Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Liu","suffix":""},{"id":483718948,"identity":"2270af66-c78a-4159-b820-32ee3c88cfc9","order_by":1,"name":"Chuanhua Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACfv7G5gcJFf/k2NibDxCnRXLG4TaDD2cOGPPxHEsgTovBgfQGyZltBxLnSfgYEOmyAwcbjHnb7jC2SfB8vPGGwU5Ot4GADsbmxobHPOeeMbNJ9262nMOQbGx2gIAWZgagLTxlzGxsMme3SfMwHEjcRkgLG0NigzQPGzMPm0TOM+K08AC1SM5oOywB1MJGnBYJiYOgQE4zYOM5Zmw5x4AIv9ifb38MjEqb+vntzQ9vvKmwkyOoBdVKHmKjBkkLqTpGwSgYBaNgRAAA2URFziJ31OsAAAAASUVORK5CYII=","orcid":"","institution":"Sichuan University of Science and Engineering","correspondingAuthor":true,"prefix":"","firstName":"Chuanhua","middleName":"","lastName":"Ge","suffix":""},{"id":483718949,"identity":"1359748c-ccde-42dc-b70e-0fc2ba16012e","order_by":2,"name":"Ruifeng Tan","email":"","orcid":"","institution":"Sinopec East China Petroleum Engineering Co., LTD","correspondingAuthor":false,"prefix":"","firstName":"Ruifeng","middleName":"","lastName":"Tan","suffix":""},{"id":483718950,"identity":"9cf57a34-7882-4365-a6fa-fbe54c7b0af4","order_by":3,"name":"Ruiqi Zhang","email":"","orcid":"","institution":"Sichuan University of Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Ruiqi","middleName":"","lastName":"Zhang","suffix":""},{"id":483718951,"identity":"e18a9354-a117-4af5-a642-06db27636244","order_by":4,"name":"Jianguo Zhao","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-06-13 02:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6884073/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6884073/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99789462,"identity":"d733e78a-3882-45a8-b273-2faec785fa00","added_by":"auto","created_at":"2026-01-08 12:49:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674419,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonIntelligentAssistedWellKillingSystemforGasInfluxandBlowout.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6884073/v1_covered_655c23ee-dfdd-43c4-902f-35eab2982319.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Intelligent Assisted Well-Killing System for Gas Influx and Blowout","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gas invasion and overflow, Multiphase flow in the wellbore, Wellhead pressure prediction, Well control safety technology","lastPublishedDoi":"10.21203/rs.3.rs-6884073/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6884073/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs China's oil and gas exploration and development gradually shifts to deep layers, deep seas, and unconventional sources, the formation anomalies become more complex, which poses great well control risks for oil and gas production. Well-killing is the core of well control safety technology. The current technology has technical problems such as low prediction accuracy of wellhead pressure, long control response time, and low degree of intelligence, which restricts the development of well control technology. This article systematically develops a prediction model for the development of multiphase flow in the wellbore and an analysis method for pressure response based on the well killing conditions during gas invasion and overflow. It proposes an operational logic for solving the mathematical model of dynamic response of wellbore pressure, establishes a calculation model for unconventional dynamic pending boundary, and forms a smart assistant well killing system for gas invasion and overflow. The results show that the model in this article is closer to the measured data, with a maximum error of 1.26% (0.08MPa) in wellhead casing pressure value, and the peak time is only 2 minutes ahead of the measured data. At the same time, the response time of the smart assistant well killing system is less than 2 seconds. The analysis results are close to engineering reality, and the response time of the smart assistant well killing system is timely. This has important engineering significance for reducing well control risks, promoting oil and gas resource development, and ensuring energy security.\u003c/p\u003e","manuscriptTitle":"Research on Intelligent Assisted Well-Killing System for Gas Influx and Blowout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 09:55:38","doi":"10.21203/rs.3.rs-6884073/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a9451a3-a9d6-42e5-ad98-5499739b4059","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-03T05:38:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 09:55:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6884073","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6884073","identity":"rs-6884073","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}