Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study
<p>Leak detection in oil and gas pipelines plays a crucial role in ensuring flow assurance. Numerous analytical and experimental methods are employed in the industry for leak detection. However, many of these techniques are costly or suffer from poor performance, including high false alarm rat...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| _version_ | 1864513534668308480 |
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| author | Wahib A. Al-Ammari (17191519) |
| author2 | Ahmad K. Sleiti (14778229) Mohammad Azizur Rahman (4803336) S. Rezaei-Gomari (21797489) I. Hassan (22466110) R. Hassan (6544772) |
| author2_role | author author author author author |
| author_facet | Wahib A. Al-Ammari (17191519) Ahmad K. Sleiti (14778229) Mohammad Azizur Rahman (4803336) S. Rezaei-Gomari (21797489) I. Hassan (22466110) R. Hassan (6544772) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wahib A. Al-Ammari (17191519) Ahmad K. Sleiti (14778229) Mohammad Azizur Rahman (4803336) S. Rezaei-Gomari (21797489) I. Hassan (22466110) R. Hassan (6544772) |
| dc.date.none.fl_str_mv | 2025-03-22T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.aej.2025.03.054 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Digital_twin_for_leak_detection_and_fault_diagnostics_in_gas_pipelines_A_systematic_review_model_development_and_case_study/30405217 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Mechanical engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Digital twin Oil and gas pipeline Leak detection Leak localization Anomaly detection Fault diagnostics |
| dc.title.none.fl_str_mv | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Leak detection in oil and gas pipelines plays a crucial role in ensuring flow assurance. Numerous analytical and experimental methods are employed in the industry for leak detection. However, many of these techniques are costly or suffer from poor performance, including high false alarm rates. This research aims to develop a robust digital twin (DT) to address these issues. A comprehensive review is conducted, focusing on evaluating the performance of existing leak detection methods, including DT-based models. Furthermore, an overview of machine learning techniques used in the development of pipeline DT models is provided. The review reveals that current DT models are primarily focused on leak detection but do not adequately identify leak size and location. This study proposes a more comprehensive DT model capable of detecting pipeline abnormalities, such as leaks, equipment failure, and damage. The proposed DT is applied to a real-field gas pipeline case study to validate its feasibility. The results demonstrate that the developed DT model successfully detects leaks with a zero false alarm rate and accurately identifies leak size and location with an absolute relative error of less than 3.21 %. This work serves as a reference for future DT development and research in pipeline monitoring.</p><h2>Other Information</h2> <p> Published in: Alexandria Engineering Journal<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.aej.2025.03.054" target="_blank">https://dx.doi.org/10.1016/j.aej.2025.03.054</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0a35c44019e163e03448c0334e6c3c57 |
| identifier_str_mv | 10.1016/j.aej.2025.03.054 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30405217 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case studyWahib A. Al-Ammari (17191519)Ahmad K. Sleiti (14778229)Mohammad Azizur Rahman (4803336)S. Rezaei-Gomari (21797489)I. Hassan (22466110)R. Hassan (6544772)EngineeringMechanical engineeringResources engineering and extractive metallurgyInformation and computing sciencesArtificial intelligenceMachine learningDigital twinOil and gas pipelineLeak detectionLeak localizationAnomaly detectionFault diagnostics<p>Leak detection in oil and gas pipelines plays a crucial role in ensuring flow assurance. Numerous analytical and experimental methods are employed in the industry for leak detection. However, many of these techniques are costly or suffer from poor performance, including high false alarm rates. This research aims to develop a robust digital twin (DT) to address these issues. A comprehensive review is conducted, focusing on evaluating the performance of existing leak detection methods, including DT-based models. Furthermore, an overview of machine learning techniques used in the development of pipeline DT models is provided. The review reveals that current DT models are primarily focused on leak detection but do not adequately identify leak size and location. This study proposes a more comprehensive DT model capable of detecting pipeline abnormalities, such as leaks, equipment failure, and damage. The proposed DT is applied to a real-field gas pipeline case study to validate its feasibility. The results demonstrate that the developed DT model successfully detects leaks with a zero false alarm rate and accurately identifies leak size and location with an absolute relative error of less than 3.21 %. This work serves as a reference for future DT development and research in pipeline monitoring.</p><h2>Other Information</h2> <p> Published in: Alexandria Engineering Journal<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.aej.2025.03.054" target="_blank">https://dx.doi.org/10.1016/j.aej.2025.03.054</a></p>2025-03-22T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.aej.2025.03.054https://figshare.com/articles/journal_contribution/Digital_twin_for_leak_detection_and_fault_diagnostics_in_gas_pipelines_A_systematic_review_model_development_and_case_study/30405217CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304052172025-03-22T12:00:00Z |
| spellingShingle | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study Wahib A. Al-Ammari (17191519) Engineering Mechanical engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Digital twin Oil and gas pipeline Leak detection Leak localization Anomaly detection Fault diagnostics |
| status_str | publishedVersion |
| title | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| title_full | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| title_fullStr | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| title_full_unstemmed | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| title_short | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| title_sort | Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study |
| topic | Engineering Mechanical engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Digital twin Oil and gas pipeline Leak detection Leak localization Anomaly detection Fault diagnostics |