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Woodside Tests New Passive-Active RFID Tag

The Australian oil and gas company is trialing Omni-ID's Power 400 RFID tag, branded as the Woodside Smart Tag (or W-Tag), at its LNG operation site.
By Claire Swedberg
Jul 01, 2014

With the goal of making mega-construction projects leaner and more efficient, Australian oil and gas company Woodside Energy Ltd. is testing a dual-frequency passive-active RFID tag developed specifically for use by Woodside and other oil and gas firms. The W-tag, certified as intrinsically safe, combines long and short frequency ranges, thereby allowing components to be tracked within a petrochemical facility's battery limits.

Woodside operates six out of seven liquefied natural gas (LNG) processing trains in Australia. The oil and gas industry in that region has been facing numerous productivity challenges, as the country is poised to become one of the world's largest LNG exporters during the coming years. The cost of LNG plant construction is high, however, and includes complex logistical issues unique to the harsh Australian environment. Woodside is the nation's largest independent oil and gas operator, with a decades-long track record of delivering LNG to its customers in Asia.

The W-Tag in action at the North West Shelf Gas LNG Project in Karratha, Western Australia
During the engineering, construction and operating phases of an oil and gas facility, difficulties can arise that may have significant consequences for a client. Some of these problems include out-of-sequence delivery of construction materials, damage incurred during transit, and errors and omissions in details that can result in material being unavailable when needed, and in workers standing by non-productively while being paid. Such issues can be expensive, the company notes, both for the construction contractor and for the client.

Some of these issues could be resolved by implementing radio frequency identification; one example would be the use of RFID to quickly capture the long-distance identification of a piece of equipment. This could drastically reduce the need for workers to walk around a site searching for materials and tools, particularly at sites spanning several square kilometers, where temperatures can average around 40 degrees Celsius (104 degrees Fahrenheit).

Woodside explored several existing RFID tags, but found none suitable with the necessary IECEx certification (a standard related to the safety of equipment used in explosive atmospheres). What's more, they typically consisted of passive ultrahigh-frequency (UHF) tags that could be read only at a relatively short range. Woodside sought a tag that was suitable during a megaproject's five-year construction phase, when materials constantly change location and active tag functionality is required to support a longer read range. Upon a project's completion, the facility is handed over to the operator with most materials at their final destination, so that the passive functionality would be sufficient for day-to-day inspection and maintenance. Therefore, Woodside went about seeking a way to create its own intelligent tracking system that would operate on its vast construction sites.

In December 2011, Woodside initiated a lean construction research program known as Project Echo, with support from government and local educational institutions Curtin University and the Challenger Institute of Technology. The program is focusing its efforts on technological solutions to make LNG construction projects leaner and more efficient, with RFID as well as other technologies. Since its inception, Project Echo has attracted some 15 high-tech companies that joined the alliance to further research lean-construction solutions. The research explores a unique system combining Woodside's Smart Tags with a range of other technology solutions, such as 3D models and augmented reality, in order to provide the appropriate technical data to authorized end users via mobile computing. This unique concept can be described as a Google Maps solution for the oil, gas and construction industries, drawing only the necessary information from a vast Big Data set.

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