Organizations are collecting more information than they ever have in the past, and energy companies are no exception. One estimate puts the amount of data collected on an offshore oil rig at 2 terabytes per day. More and more, these companies are turning to analytics service providers to make sense of their data within their business processes. Now, those analytics providers are looking at ways to translate their experience in oil and gas to the growing offshore wind energy market.
ABS Group works with offshore oil and gas companies to deliver analytics services that help answer operational questions with the data those companies collect. The Houston-based solutions provider also works in the project certification phase for offshore wind to help developers identify and mitigate risk. ABS Group Vice President of Power Business Development Tom Adams believes that the company can leverage its experience in risk mitigation for offshore wind and its work with offshore oil and gas analytics to bring big data analytics to the operational phase of offshore wind projects.
“Our involvement in offshore wind in this particular area is early days – it’s something of great interest to us and we think a growing focus and priority for the offshore wind industry globally,” Adams said. “There are several drivers that lead us to believe that a high level of sophistication and analytical ‘horse power’ will be needed.”
One of the main drivers he sees in the latest large-scale offshore wind projects is the very aggressive cost that the developers are bidding for their power.
“We see that on the front end on the capital investments side and the EPC phase, which we’re more heavily involved in these days as a project certification service provider,” he said.
The cost of building offshore wind farms is coming down tremendously as the scale of new projects has grown. Those costs are moving very aggressively through all parts of the supply chain, and ABS Group sees the same cost pressures and aggressive targets translating to the O&M phase of projects as well.
“Anything that can help achieve those targets or make them more certain to be achieved is going to be in demand,” Adams said.
Srikanta Mishra, Ph.D., Institute Fellow and chief scientist for energy at Battelle, believes the application of data analytics in three specific areas of offshore oil and gas environments — forecasting, equipment performance and predictive maintenance — is “readily translated” to offshore wind energy.
In terms of forecasting, Mishra said, offshore wind farm operators would be interested in making predictions of weather, temperature or precipitation, for example, together with inputs such as wind speed and wind direction.
“You can learn from the past and build models that can say with some degree of accuracy what is going to be the wind speed and direction in the next hour and the next 24 hours,” he said. “Those can be important inputs with respect to how the grid accepts what is coming from the wind energy generator.”
With respect to performance, having sensors that monitor the behavior of turbines on an individual basis, and the wind farm collectively, can be a valuable process, he said. Analysis of that data can determine the conditions in which the turbines have under-performed in the past and be applied to operations in the future.
He said that the same type of analyses can be applied for predictive maintenance of individual turbine components to identify conditions, such as pressure, temperature, wind speed, and precipitation, when equipment has failed.
Battelle works with companies to bring multiple sources of data into one easy-to-use platform that allows operators to learn from their data and then use that knowledge to make decisions. Through its Elucidata service, Battelle gives energy companies access to a large group of subject matter experts across a variety of domains.
Data management provider OSIsoft’s experience working with offshore oil rigs on condition-based maintenance helped the company support Dong Energy in using data and analytics to understand how to do the maintenance that was needed on its offshore wind turbines at the right time and the lowest cost.
According to Chris Crosby, principal – global nuclear and renewable energy for OSIsoft, Dong Energy took the data that was available on its wind turbine generators and moved it into OSIsoft’s scalable, open data infrastructure, called PI, to do condition-based maintenance. Dong Energy also moved that data into its asset management system so that its managers could create work orders. In addition, the company used its data to established a map that provided a visual status of the wind turbines.
Integrating its data into the three technologies allowed the wind farm managers to have a visual of what was happening in a particular wind farm, and drill down into a specific wind turbine to see the work flow status on that machine, Crosby said. Dong Energy saved 20 million euro per year as a result of that integration initiative.
According to Crosby, monitoring and understanding the health of an asset, and bringing that visually to managers within an organization, supports similar business problems, no matter the business.
“One is oil rigs and one is wind turbines,” he said.
One of the real strengths of PI is that it allows managers to perform predictive analytics – projecting and forecasting before an event, according to Kevin Walsh, industry principal – T&D, for OSIsoft.
Dong Energy’s Gunfleet Sands offshore wind farm, south-east of Clacton-on-Sea, Essex, England. Credit: Steve Graby | Flickr.
PI has different modules that allow for the input of mini-analytic calculations based on certain attributes or time frames to create an operational “band,” he said. If operations go outside of that band – for example, plus or minus 1 percent – for more than three minutes in a row, PI notifies managers that an alarm-level event is imminent. This feature gives managers a visual on possible problems before they happen – a huge advancement in data functionality.
What’s Next for Big Data?
Down the road, there are all sorts of applications for business analytics, according to ABS Group’s Matt Mowrer, director of applied technology and data analytics.
“I think we’ll move beyond just looking at historical data to predictive analytics and then on to prescriptive analytics,” he said. “I really see that on the risk side, where I’m not just providing alarms to operators about anomalous conditions based on multiple data feeds, but actually giving them the recommended action to minimize the time it takes them to make the decision and also hopefully eliminate bad decisions.”
Also down the road, he added, could be the development of augmented reality for operations and maintenance workers, where they are in the physical environment and they have access to a virtual environment.
He said that, from a wearable technology perspective, such as smart glasses, operations and maintenance workers can access maintenance procedures, and have an interface where they interact with equipment to minimize time spent troubleshooting or identifying tools and procedures.
“You’re seeing these things on the consumer side, and I think there’s some natural industrial applications for them,” said Mowrer.
