Inspection/maintenance
SPE's new Asset Integrity Technical Section brings together industry experts to advance best practices in reliability engineering, asset integrity, risk reduction, and infrastructure performance. The global community will focus on the safe, reliable, and sustainable operation of energy assets across both traditional and emerging energy sectors.
The latest edition of ISO 20815 extends the petroleum industry's production assurance and reliability management framework to carbon capture, hydrogen, ammonia, and wind assets.
Shell has demonstrated drone-in-a-box operations from its Mars floating production unit, becoming the first operator in the region to secure FAA approval for self-approved offshore beyond-visual-line-of-sight flights. JPT Senior Technology Editor Jennifer Pallanich visited Shell Technology Center Houston to see the system in action.
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Tested within a full ballast water tank on a North Sea FPSO, the ROV collected visual inspection data, validating system performance including wireless navigation and wireless communication.
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The authors present an artificial-intelligence and machine-learning technology to obtain a high-level, comprehensive view of all equipment in a facility to detect and map corrosion.
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This paper describes a method to determine rig state from camera footage using machine-learning-based vision-analytics approaches.
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This paper presents a framework that advances 3D printing in the oil and gas industry, evaluating the challenges this technology is facing in a conservative industry and realizing its potential benefits.
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SponsoredDrowning in data? Can’t readily access key data for making critical decisions because it is in legacy systems, unstructured, or not digitized? We have solutions.
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Inspection and certification services provider will continue select asset work for Ithaca Energy through January 2025.
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SponsoredShell needed an easy-to-use, simple well monitoring solution. Could an IoT wireless solution measure up to a classic wired approach?
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The authors describe how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage prediction for marine risers.
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The complete paper discusses structural assessments for gravity-based structures, a topic that the authors write is underrepresented in the literature.
