{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T12:29:21Z","timestamp":1742387361243},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"The sliding sleeve<\/jats:italic> holds a pivotal role in regulating fluid flow during hydraulic fracturing within shale oil extraction processes. However, concerns persist surrounding its reliability due to repeated attempts at opening the sleeve, resulting in process inefficiencies. While downhole cameras can verify sleeve states, their high cost poses a limitation. This study proposes an alternative approach, leveraging downhole data analysis for sleeve incident detection in lieu of cameras. This study introduces \u201cXGSleeve,\u201d a novel machine-learning methodology. XGSleeve amalgamates hidden Markov model-based clustering with the XGBoost model, offering robust identification of sleeve incidents. This method serves as an operator-centric tool, addressing the domains of oil and gas, well completion, sliding sleeves, time series classification, signal processing, XGBoost, and hidden Markov models. The XGSleeve model exhibits a commendable 86% precision in detecting sleeve incidents. This outcome significantly curtails the need for multiple sleeve open-close attempts, thereby enhancing operational efficiency and safety. The successful implementation of the XGSleeve model rectifies existing limitations in sleeve incident detection, consequently fostering optimization, safety, and resilience within the oil and gas sector. This innovation further underscores the potential for data-driven decision-making in the industry. The XGSleeve model represents a groundbreaking advancement in sleeve incident detection, demonstrating the potential for broader integration of AI and machine learning in oil and gas operations. As technology advances, such methodologies are poised to optimize processes, minimize environmental impact, and promote sustainable practices. Ultimately, the adoption of XGSleeve contributes to the enduring growth and responsible management of global oil and gas resources.<\/jats:p>","DOI":"10.3389\/frai.2023.1243584","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T20:09:40Z","timestamp":1694635780000},"update-policy":"http:\/\/dx.doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["XGSleeve: detecting sleeve incidents in well completion by using XGBoost classifier"],"prefix":"10.3389","volume":"6","author":[{"given":"Sahand","family":"Somi","sequence":"first","affiliation":[]},{"given":"Sheikh","family":"Jubair","sequence":"additional","affiliation":[]},{"given":"David","family":"Cooper","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"B1","unstructured":"CEC Fact Sheet2022"},{"key":"B2","doi-asserted-by":"crossref","first-page":"8488","DOI":"10.23919\/CCC52363.2021.9550095","article-title":"\u201cImage classification of time series based on deep convolutional neural network,\u201d","volume-title":"2021 40th Chinese Control Conference (CCC)","author":"Cao","year":"2021"},{"key":"B3","article-title":"\u201cHigh density CCL memory tool to confirm SSD sleeve position,\u201d","volume-title":"SPE\/ICoTA Well Intervention Conference and Exhibition","author":"Daniels","year":"2001"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2308.02395","article-title":"ECG classification using deep CNN and Gramian angular field","author":"Elmir","year":"2023","journal-title":"arXiv preprint arXiv:2308.02395"},{"key":"B5","unstructured":"Options to Cap and Cut Oil and Gas Sector Greenhouse Gas Emissions to Achieve 2030 Goals and Net-Zero by 20502023"},{"key":"B6","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/HIS.2012.6421385","article-title":"\u201cImproving time series classification using hidden Markov models,\u201d","volume-title":"2012 12th International Conference on Hybrid Intelligent Systems (HIS)","author":"Esmael","year":"2012"},{"key":"B7","doi-asserted-by":"publisher","first-page":"2741","DOI":"10.3390\/ijerph110302741","article-title":"Clustering multivariate time series using hidden Markov models","volume":"11","author":"Ghassempour","year":"2014","journal-title":"Int. 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