Smoothing techniques improve trend detection in rate-transient analysis (RTA) of tight gas reservoirs

Smoothing techniques improve trend detection in rate-transient analysis (RTA) of tight gas reservoirs | 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 Smoothing techniques improve trend detection in rate-transient analysis (RTA) of tight gas reservoirs Felix A. Munthe, W. John Lee, Rami Harkouss This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9361605/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 Production data used in rate-transient analysis (RTA) are often contaminated by noise and operational artifacts, obscuring diagnostic signatures and introducing uncertainty in flow-regime identification and reservoir characterization. While outlier detection (OD) methods are commonly applied, they cannot fully eliminate anomalous behavior, leaving residual noise that distorts rate-normalized pressure (RNP) trends. This study demonstrates that smoothing is not merely a visualization tool but a critical preprocessing step for preserving flow-regime signatures in noisy RNP data and improving the reliability of RTA interpretation. Smoothing methods enhance the visualization of scattered data and improve the accuracy of extracted flow-regime times and reservoir characteristics by predicting representative data points. This helps recover the underlying RNP trend, particularly when OD methods are limited. We evaluated five smoothing techniques using noisy synthetic RNP datasets generated from a homogeneous, single-layer, low-permeability gas reservoir produced from a single-fractured vertical well. Noise and outliers were introduced using additive white Gaussian noise (AWGN) at multiple noise-contamination levels and noise-presence scenarios to represent realistic production conditions. We assessed the performance of each method based on goodness-of-fit and the accuracy of the extracted flow-regime characteristics, including linear-flow times and permeability—squared fracture half-length ( \(\:k{{x}_{f}}^{2}\) ) estimation. The locally weighted scatterplot smoothing (LoWeSS) method demonstrates superior performance, achieving the lowest average deviation while effectively reducing noise and preserving underlying trends in the log-log RNP plot. An optimal window size of 19 provides the most reliable results, particularly in the boundary-dominated flow (BDF) regime. The application of LoWeSS results in significantly improved reservoir parameter estimation, with approximately 30% higher accuracy in \(\:k{{x}_{f}}^{2}\) derived from square-root time analysis. This is supported by the improved regression quality of the fitted line during linear flow. These findings establish smoothing as an essential component of RTA workflows, reducing subjectivity in the interpretation of diagnostic plots (associated with the start and end of flow regime times) and improving the robustness of reservoir characterization (associated with the \(\:k{{x}_{f}}^{2}\) ) and production forecasting (associated with the Arps \(\:b\) -factor), even when applied after OD methods such as the general mixture model (GMM). The findings are further supported by application to field data, demonstrating improved clarity in flow regime identification under real production conditions and enabling more reliable engineering decision-making. Petroleum Engineering Smoothing Rate-normalized pressure Rate-transient analysis Flow regime Reservoir characteristics Full Text Additional Declarations The authors declare no competing interests. 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-9361605","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619938743,"identity":"cac41940-c3b0-4980-b745-719174254ac7","order_by":0,"name":"Felix A. Munthe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBAC9gbmhgNglgQD4wMgxcNHSAvPAUaIFh4JBmYDEM1GjBYGqBY2CRCDsBb2xsbDvDmH8+ylm49Vfs2xk2FjYH746AY+LTwHGw7O3Ha4mEfmWNpt2W3JQIexGRvn4NFiL5HYcODjtsOJPRI5ZrcltzEDtfCwSePTwiP/sOFAIlhL/rdiyW31RGiRYITbwsYIZBChhScR5Jf0xJ4bacbSjNuO87AxE/ALD/vhw595t1knts9Ifvjx57Zqe3725oeP8WlBAcw8YJJY5SDA+IMU1aNgFIyCUTBiAACiv0ldrY2RKQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-1122-7919","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Felix","middleName":"A.","lastName":"Munthe","suffix":""},{"id":619938781,"identity":"2e53d3a1-de25-4f8c-9d95-c31bb37cdc7e","order_by":1,"name":"W. John Lee","email":"","orcid":"https://orcid.org/0000-0003-3748-0653","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"W.","middleName":"John","lastName":"Lee","suffix":""},{"id":619938825,"identity":"a5406a1c-80b4-4826-9e88-b6515d7d1c2c","order_by":2,"name":"Rami Harkouss","email":"","orcid":"","institution":"Beirut Arab University","correspondingAuthor":false,"prefix":"","firstName":"Rami","middleName":"","lastName":"Harkouss","suffix":""}],"badges":[],"createdAt":"2026-04-09 00:31:02","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9361605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9361605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106582660,"identity":"8de2695d-55d9-48f2-88cb-ccea0e7f5eb5","added_by":"auto","created_at":"2026-04-10 06:57:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2722346,"visible":true,"origin":"","legend":"","description":"","filename":"SmoothingPaperDraftFelixWJL03.30.26.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9361605/v1_covered_85a9c847-e308-4e29-8012-d7b82d73d529.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSmoothing techniques improve trend detection in rate-transient analysis (RTA) of tight gas reservoirs\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Texas A\u0026M University","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":"Smoothing, Rate-normalized pressure, Rate-transient analysis, Flow regime, Reservoir characteristics","lastPublishedDoi":"10.21203/rs.3.rs-9361605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9361605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProduction data used in rate-transient analysis (RTA) are often contaminated by noise and operational artifacts, obscuring diagnostic signatures and introducing uncertainty in flow-regime identification and reservoir characterization. While outlier detection (OD) methods are commonly applied, they cannot fully eliminate anomalous behavior, leaving residual noise that distorts rate-normalized pressure (RNP) trends.\u003c/p\u003e \u003cp\u003eThis study demonstrates that smoothing is not merely a visualization tool but a critical preprocessing step for preserving flow-regime signatures in noisy RNP data and improving the reliability of RTA interpretation. Smoothing methods enhance the visualization of scattered data and improve the accuracy of extracted flow-regime times and reservoir characteristics by predicting representative data points. This helps recover the underlying RNP trend, particularly when OD methods are limited.\u003c/p\u003e \u003cp\u003eWe evaluated five smoothing techniques using noisy synthetic RNP datasets generated from a homogeneous, single-layer, low-permeability gas reservoir produced from a single-fractured vertical well. Noise and outliers were introduced using additive white Gaussian noise (AWGN) at multiple noise-contamination levels and noise-presence scenarios to represent realistic production conditions. We assessed the performance of each method based on goodness-of-fit and the accuracy of the extracted flow-regime characteristics, including linear-flow times and permeability\u0026mdash;squared fracture half-length (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k{{x}_{f}}^{2}\\)\u003c/span\u003e\u003c/span\u003e) estimation.\u003c/p\u003e \u003cp\u003eThe locally weighted scatterplot smoothing (LoWeSS) method demonstrates superior performance, achieving the lowest average deviation while effectively reducing noise and preserving underlying trends in the log-log RNP plot. An optimal window size of 19 provides the most reliable results, particularly in the boundary-dominated flow (BDF) regime. The application of LoWeSS results in significantly improved reservoir parameter estimation, with approximately 30% higher accuracy in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k{{x}_{f}}^{2}\\)\u003c/span\u003e\u003c/span\u003e derived from square-root time analysis. This is supported by the improved regression quality of the fitted line during linear flow.\u003c/p\u003e \u003cp\u003eThese findings establish smoothing as an essential component of RTA workflows, reducing subjectivity in the interpretation of diagnostic plots (associated with the start and end of flow regime times) and improving the robustness of reservoir characterization (associated with the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k{{x}_{f}}^{2}\\)\u003c/span\u003e\u003c/span\u003e) and production forecasting (associated with the Arps \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e-factor), even when applied after OD methods such as the general mixture model (GMM). The findings are further supported by application to field data, demonstrating improved clarity in flow regime identification under real production conditions and enabling more reliable engineering decision-making.\u003c/p\u003e","manuscriptTitle":"Smoothing techniques improve trend detection in rate-transient analysis (RTA) of tight gas reservoirs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 06:55:40","doi":"10.21203/rs.3.rs-9361605/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":"a6280c19-bf76-4e72-878d-d7080d0a016c","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65973295,"name":"Petroleum Engineering"}],"tags":[],"updatedAt":"2026-04-10T06:55:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 06:55:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9361605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9361605","identity":"rs-9361605","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}