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Oil and gas industry interest is surging in using remote survey technologies for more cost-efficient, safer, and lower-carbon certification, verification, and inspection of assets and operations. Amid COVID-19 travel restrictions in 2020, DNV GL has conducted more than 4,000 remote surveys for the sector. These have provided the supply chain with the assurance it needs to keep projects and operations running safely and on schedule. Remote surveys involve fixed and mobile cameras (e.g., smartphones) giving customers instant access to DNV GL experts worldwide for verification, classification, and certification of assets, verification of materials and components, inspection, and marine assurance. The growing track record for remote survey technology could soon make it the method of choice for inspections in some places and circumstances, according to a senior expert at one leading oil and gas exploration and production company.
Emerging inspection technologies, tools and platforms such as unmanned aerial vehicles (UAVs), remotely operated vehicles (ROVs), robotic crawlers, and wearable/handheld devices are creating actionable data to help enable more informed decision making and improve process efficiency during survey and inspection related activities. This paper will discuss ABS' initiatives to further understand and help define the use of and the integration of these tools and technologies to support the evolution of the maritime industry's transition to digitalization. ABS, in conjunction with technology equipment manufacturers and service providers, has been conducting feasibility trials to evaluate the pragmatic application and implementation of these technologies to support Class surveys. These trials have focused on areas such as the detection of coating breakdowns using high-definition optics to aid in closeup visual inspections (CVI) and leveraging mobile platforms (wearable and handheld devices) in conjunction with a collaborative software platform to execute survey activities virtually in real-time (connected) or near real-time (disconnected), capturing data as required by Class Rules. In support of these trials, ABS is actively involved in a joint development project (JDP) with academia focusing on the realization of image recognition (artificial Intelligence [AI]) into the survey decision-making process. As part of this JDP, an AI software was developed incorporating thousands of damaged structural coating images. These images were used for the training, testing and evaluation of the software's image recognition capabilities.