Robotic digital X-ray scanning system for deep water flexible riser inspection (RobotX)
Robotic digital X-ray scanning system for deep water flexible riser inspection (RobotX)
批准号:
104088
负责人:
金额:
$50.2万
依托单位国家:
英国
项目类别:
Feasibility Studies
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --
中文摘要
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英文摘要
"New challenges are present for offshore oil and gas operators to provide adequate integrity assurance of their assets as the production facilities are reaching for the deep-water areas. Challenging conditions arise from more corrosive environments, higher pressures and temperatures. In deep water and hostile environments, where loading is high and complex and often design methods are pushed to the limit of current industry capability and experience, the riser systems have received an increased focus, more than ever in the light of several operational incidents (like the Deepwater Horizon accident in the Gulf of Mexico). These accidents have caused operators and regulators to question and update codes of practice. Flexible risers pipes are by nature complicated in design with many varying material types, corresponding to challenges in the inspection and integrity evaluation. The inspection techniques currently available in the market consist of only irregular diver or Remotely Operated Vehicle (ROV) inspections and are able to inspect only the near side layers for wire disruptions, with the far side layers remaining uninspected.The RobotX project will investigate the feasibility of a robotic digital x-ray scanning system that will address the needs and challenges of deep water flexible risers inspection. The robot and digital radiography equipment would have to withstand harsh environmental conditions i.e. high pressure (100bar). The system will perform a see--through quick scan as it crawls and process the data using innovative image processing methods and categorise them using machine learning. If defects are detected the robotic system will be able to turn around the riser and perform a more thorough scan. The defect will be correctly identified, using images taken at several angles. These innovations will allow not just to detect and locate the defects, but also classify them according to an existing historical database and automatically decide on bespoke scans for assessing the severity and needs for future intervention."
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