10/29/2023 0 Comments Integrity plus pipelineJerry Worsley 1, Jason Reynaud 2, Tony McMurtrey 3, Adnan Chughtai 4, Josh May 4ġSchlumberger Midstream Production Systems, Dubai, UAE 2Schlumberger Midstream Production Systems, Houston, USA 3Midstream Integrity Services, San Antonio, USA 4Schlumberger Midstream Production Systems, London, UKįiber-optic sensing systems are becoming more commonly used for leak and third-party intrusion detection on pipeline infrastructure throughout the world. Inline Inspection Monitoring and Data Interpretation Using Fiber-Optic Sensing This presentation will provide a knowledge transfer on this subject through an overview of how to use the published guidance document and the lessons learned from its development.Ĩ7. This technical guidance document was developed to benefit industry from the experience of the JIP members and provide knowledge sharing. With recent changes to gas pipeline regulations in 49 CFR 192 associated with integrity assessments and MAOP Reconfirmation, the use of EMAT ILI for crack management is expected to increase by new and existing users. However, the use of EMAT has mostly been limited to early adopters and requires implementation of processes and procedures particular to EMAT for it to be used as an integrity assessment. EMAT ILI has been used for over two decades and has reached a level of maturity where both the performance specifications and response planning can be systemized. The INGAA Foundation formed a joint industry project (JIP), on behalf of INGAA, to develop an industry technical guidance document specific to the use of Electromagnetic Acoustic Transducer (EMAT) in-line inspection technology for management of cracks, with specific emphasis on stress corrosion cracking (SCC). EMAT Lessons Learned Using Assessment Findings KEYWORD(S) FOR SUBJECT AREA: Machine Learning, EMAT TechnologyĨ8. Particularly, how Deep Learning methods help ensuring the quality of EMAT-Crack detection services. This paper will provide an insight on how ROSEN uses AI-methods to enhance classification and sizing of metal-loss and crack indications in pipeline inspection data. Supported by a modern data engineering infrastructure, AI-powered data-driven applications can be used to enhance both the quality and efficiency of inspection data analysis. These efforts are directed towards building data driven applications to ensure reliability of inspection systems. The AI division at ROSEN Research Center invested significant time and resources to apply powerful machine learning in particular Deep Learning methods to ensure timely and accurate inspection results delivered to operators. However, the prerequisite for gaining insights from this data is provided by Artificial Intelligence (AI) methods and a corresponding Research-First structure of a company. By collecting data of increasingly higher resolution and quality, it is possible to achieve a more and more accurate representation of the integrity reality of oil and gas structures. 2Rosen USA, Houston, USAĮMAT crack detection technology is used worldwide by many oil and gas operators to detect and size cracks in liquid and gas pipelines. Stephan Eule 1, Thomas Beuker 1, Neil Pain 2ġRosen EU, Lingen, Germany. Enhancing EMAT Crack Detection Services Using State of the Art Deep Learning Exhibition details & marketing opportunitiesĨ9.INTEGRITY AND REPURPOSING OF HYDROGEN PIPELINES API 1163 VERIFICATION AND VALIDATION WORKSHOP ANALYTICS AND MACHINE LEARNING FOR PIPELINE INTEGRITY PIPELINE REPAIR METHODS, HOT TAPPING, AND IN-SERVICE WELDING NON-DESTRUCTIVE TESTING FOR MATERIAL VERIFICATION OF PIPELINE STEELS SELECTION, INTEGRATION AND IMPLEMENTATION OF PIPELINE INSPECTION AND ASSESSMENT TECHNIQUES ASSESSING CRACKS & LONG SEAM WELD ANOMALIES
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