Comparative Analysis and Optimization of Driller’s and Engineer’s Methods for Surface Pressure Prediction in Well Control: Case Study of the XYZ Niger Delta Field

Comparative Analysis and Optimization of Driller’s and Engineer’s Methods for Surface Pressure Prediction in Well Control: Case Study of the XYZ Niger Delta Field | 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 Comparative Analysis and Optimization of Driller’s and Engineer’s Methods for Surface Pressure Prediction in Well Control: Case Study of the XYZ Niger Delta Field Chukwudi Ohaegbulam, Kevin Igwilo, Ifeanyichukwu Onyejekwe, Anthony Chikwe, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7069032/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 This study presents the development and optimization of predictive models for annular surface pressure (ASP) during well control operations, integrating detailed gas composition analysis and advanced numerical modeling. Eight natural gas samples were collected from producing wells in the XYZ Field of the Niger Delta and analyzed via gas chromatography, following GPA 2286, to determine molecular compositions and compute gas densities and pressure gradients critical for accurate simulation inputs. Well data and mud rheology, including parameters derived from the Herschel-Bulkley model, provided the basis for calculating annular frictional pressure losses during influx circulation. Well control models for the Driller’s and Engineer’s Methods were formulated based on pressure balance principles and subsequently optimized using a Multi-Objective Genetic Algorithm (MOGA). The optimization aimed to identify the minimum wellbore pressures at specific depths under constraints of kick volume (30–100 bbl) and kick intensity (0.5–1.5 psi/ft). Simulation results revealed that the minimum kick pressure in the wellbore is strongly influenced by prompt detection and accurate pore pressure estimation, consistent with prior findings emphasizing early intervention in kick management. The Driller’s Method produced higher maximum surface pressures (peaking at 838 psi) and more significant standpipe pressure fluctuations compared to the Engineer’s Method, which maintained lower pressures and required shorter circulation times, corroborating earlier research. Simulations examining the effect of kick volume showed that a 50 bbl influx kept surface pressure below the Maximum Allowable Annular Surface Pressure (MAASP) of 758 psi, whereas a 100 bbl kick elevated surface pressure to 839 psi, exceeding the MAASP and resulting in simulated formation fracturing. Additionally, varying kick intensities demonstrated that lower kick intensities significantly reduce annular surface pressures and circulation time, underscoring the importance of diagnosing kick mechanisms accurately to avoid excessive mud weights and unnecessary formation stress. Overall, the study demonstrates that although the Driller’s Method is operationally simpler, the Engineer’s Method offers superior performance in maintaining lower annular surface pressures and standpipe stability. The optimized models and simulation outcomes provide valuable tools for predicting worst-case surface pressures during well control planning, contributing to safer and more efficient well designs in the Niger Delta and similar high-pressure environments. Well control Annular surface pressure Driller’s Method Engineer’s Method Gas kick Herschel–Bulkley model Multi-Objective Genetic Algorithm Niger Delta Blowout prevention 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-7069032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483719422,"identity":"2a871c28-0a7f-4c2c-a1da-a7f87fe82f13","order_by":0,"name":"Chukwudi 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Multi-Objective Genetic Algorithm, Niger Delta, Blowout prevention","lastPublishedDoi":"10.21203/rs.3.rs-7069032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7069032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents the development and optimization of predictive models for annular surface pressure (ASP) during well control operations, integrating detailed gas composition analysis and advanced numerical modeling. Eight natural gas samples were collected from producing wells in the XYZ Field of the Niger Delta and analyzed via gas chromatography, following GPA 2286, to determine molecular compositions and compute gas densities and pressure gradients critical for accurate simulation inputs. Well data and mud rheology, including parameters derived from the Herschel-Bulkley model, provided the basis for calculating annular frictional pressure losses during influx circulation. Well control models for the Driller\u0026rsquo;s and Engineer\u0026rsquo;s Methods were formulated based on pressure balance principles and subsequently optimized using a Multi-Objective Genetic Algorithm (MOGA). The optimization aimed to identify the minimum wellbore pressures at specific depths under constraints of kick volume (30\u0026ndash;100 bbl) and kick intensity (0.5\u0026ndash;1.5 psi/ft). Simulation results revealed that the minimum kick pressure in the wellbore is strongly influenced by prompt detection and accurate pore pressure estimation, consistent with prior findings emphasizing early intervention in kick management. The Driller\u0026rsquo;s Method produced higher maximum surface pressures (peaking at 838 psi) and more significant standpipe pressure fluctuations compared to the Engineer\u0026rsquo;s Method, which maintained lower pressures and required shorter circulation times, corroborating earlier research. Simulations examining the effect of kick volume showed that a 50 bbl influx kept surface pressure below the Maximum Allowable Annular Surface Pressure (MAASP) of 758 psi, whereas a 100 bbl kick elevated surface pressure to 839 psi, exceeding the MAASP and resulting in simulated formation fracturing. Additionally, varying kick intensities demonstrated that lower kick intensities significantly reduce annular surface pressures and circulation time, underscoring the importance of diagnosing kick mechanisms accurately to avoid excessive mud weights and unnecessary formation stress. Overall, the study demonstrates that although the Driller\u0026rsquo;s Method is operationally simpler, the Engineer\u0026rsquo;s Method offers superior performance in maintaining lower annular surface pressures and standpipe stability. The optimized models and simulation outcomes provide valuable tools for predicting worst-case surface pressures during well control planning, contributing to safer and more efficient well designs in the Niger Delta and similar high-pressure environments.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis and Optimization of Driller’s and Engineer’s Methods for Surface Pressure Prediction in Well Control: Case Study of the XYZ Niger Delta Field","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 08:52:26","doi":"10.21203/rs.3.rs-7069032/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":"238d6eea-d398-44b2-9975-e671f16218cd","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 08:52:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7069032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7069032","identity":"rs-7069032","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}