Classifying Drilling Loss of Circulation in the Oil Fields of the Middle East through the Application of Neural Network Techniques

Classifying Drilling Loss of Circulation in the Oil Fields of the Middle East through the Application of Neural Network Techniques | 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 Classifying Drilling Loss of Circulation in the Oil Fields of the Middle East through the Application of Neural Network Techniques Reda Abdel Azim, Mohammed Namuq, Arkan Goma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7949548/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 One of the most common drilling operation problems is drilling fluid loss, which can cause an intensive increase in well expenditure as well as more complex drilling operation issues such as pipe sticking, blowout, and even the closure of the well. Identifying thief zones and the amount of fluid loss using analytical models is particularly difficult, and there are no robust equations available in the literature due to a wide range of influential parameters, both controllable and uncontrollable. These parameters include operational factors, as well as the physical properties of the rock and drilling fluid. This study presents an artificial intelligence-based model designed to predict the loss circulation and identify loss zones. The model accounts for various factors in the drilling process, as well as the physical properties of the rock, and the drilling fluid. In this research study, a total of 15000 data points were collected from oil wells in the Middle East. In the process of the artificial intelligence model development, 30 neurons and one hidden layer were employed in the training and testing phase with the optimum settings; 70% of the dataset is used for training, and 30% is used for testing and validation. The ANN model exhibited a remarkable ability to accurately predict the locations of lost circulation zones based on the collected data, achieving an impressive accuracy of 94.5%. This is a significant achievement when compared to existing ANN models in the literature. The results highlight the strength of the ANN model in predicting lost circulation locations across a wide range of data collected from various wells in the Middle East. Furthermore, this model takes into account a diverse set of drilling operational parameters, as well as rock characteristics and fluid properties, making it innovative compared to other available ANN models. Furthermore, this advancement will greatly facilitate future studies and make it possible to predict the lost circulation zones and volume, and plan in advance for the use of appropriate prevention and remediation methods in the well planning phase to reduce the risk of mud loss. Lost circulation of Oil/Gas well Predicting lost circulation zones ANN application for lost circulation problems 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-7949548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553631590,"identity":"3fa2c1c8-e500-42e5-82be-169c8064afc7","order_by":0,"name":"Reda Abdel 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circulation problems","lastPublishedDoi":"10.21203/rs.3.rs-7949548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7949548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the most common drilling operation problems is drilling fluid loss, which can cause an intensive increase in well expenditure as well as more complex drilling operation issues such as pipe sticking, blowout, and even the closure of the well. Identifying thief zones and the amount of fluid loss using analytical models is particularly difficult, and there are no robust equations available in the literature due to a wide range of influential parameters, both controllable and uncontrollable. These parameters include operational factors, as well as the physical properties of the rock and drilling fluid.\u003c/p\u003e\u003cp\u003eThis study presents an artificial intelligence-based model designed to predict the loss circulation and identify loss zones. The model accounts for various factors in the drilling process, as well as the physical properties of the rock, and the drilling fluid. In this research study, a total of 15000 data points were collected from oil wells in the Middle East. In the process of the artificial intelligence model development, 30 neurons and one hidden layer were employed in the training and testing phase with the optimum settings; 70% of the dataset is used for training, and 30% is used for testing and validation.\u003c/p\u003e\u003cp\u003eThe ANN model exhibited a remarkable ability to accurately predict the locations of lost circulation zones based on the collected data, achieving an impressive accuracy of 94.5%. This is a significant achievement when compared to existing ANN models in the literature. The results highlight the strength of the ANN model in predicting lost circulation locations across a wide range of data collected from various wells in the Middle East. Furthermore, this model takes into account a diverse set of drilling operational parameters, as well as rock characteristics and fluid properties, making it innovative compared to other available ANN models. Furthermore, this advancement will greatly facilitate future studies and make it possible to predict the lost circulation zones and volume, and plan in advance for the use of appropriate prevention and remediation methods in the well planning phase to reduce the risk of mud loss.\u003c/p\u003e","manuscriptTitle":"Classifying Drilling Loss of Circulation in the Oil Fields of the Middle East through the Application of Neural Network Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 23:48:54","doi":"10.21203/rs.3.rs-7949548/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":"0f25f411-b042-4f35-b435-47bd6e06a519","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T06:25:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 23:48:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7949548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7949548","identity":"rs-7949548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}