Fiber Optics for Offshore Wind and Gas Storage Safety

Researchers at Berkeley Lab have have been awarded new grants to develop fiber optic cables for monitoring offshore wind operations and underground natural gas storage.

The new research builds on Berkeley Lab’s previous studies in fiber optic cables and their for use in carbon sequestration, groundwater mapping, earthquake detection, and monitoring of Arctic permafrost thaw.

The California Energy Commission has awarded Berkeley Lab $2 million for the offshore wind project and $1.5 million for the natural gas project.

Both projects are in collaboration with UC Berkeley, and for the natural gas project, Berkeley Lab will also collaborate with PG&E, Schlumberger, and C-FER Technologies, to carry out the tests.

“A fiber cable has a glass core that allows you to send an optical signal down at the speed of light; when there is any vibration, strains, or stresses or changes in temperature of the material that is being monitored, that information will be carried in the light signal that is scattered back,” said Berkeley Lab scientist Yuxin Wu, who is leading both projects.

Fiber Optics for offshore wind and marine mammal activity

According to Berkeley Lab, fiber optic cables can benefit floating offshore wind farms, which face several obstacles, including how to do maintenance and operations on remote installations in the ocean economically and how to monitor if hazards such as earthquakes or extreme weather conditions disrupt operations.

“One of the most expensive components of a wind turbine is the gearbox; they also tend to be the part that’s most vulnerable to failure,” said Wu, who is also head of Berkeley Lab’s Geophysics Department. “Often before they fail they produce abnormal vibrations or excessive heat due to increased or irregular friction. We intend to use fiber optic cables to monitor the vibrational, strain, and temperature signal of the gearbox, in order to pinpoint where problems are happening.”

“Wrapping fiber optic cables around the entire gearbox can provide a 3D map of changes with resolution at the millimeter scale. It could help identify problems with the gearbox at an early stage, which would trigger emergency management, before a catastrophic failure causing loss of the whole turbine,” Wu said.

The project also intends to explore how the fiber optic cables can be used to detect marine mammal activity. The sensitivity of the fiber signal could allow for differentiation between, say, crashing waves and a pod of whales swimming by.

Wu added that he is looking to learn more about whales and other marine mammals from marine biologists and also seeking a partner to collaborate with to test the sensors in the ocean.

Monitoring gas storage reservoirs

Wu and his research partners also hope to use fiber optic cables to monitor the boreholes of underground natural gas storage reservoirs.

Borehole integrity is currently monitored mostly using tools that are intrusive, expensive, and incapable of providing frequent, real-time data. “It is difficult to predict borehole degradation trajectory with the sparse data generated by traditional methods. Having higher frequency datasets covering the entire borehole is key to provide an early warning of potential borehole failures,” Wu said.

In the new CEC-funded project, Berkeley Lab will work with UC Berkeley, PG&E, Schlumberger, and C-FER to test a novel suite of technologies for autonomous real-time monitoring using two methods, one based on distributed strain, vibration, and temperature sensing in fiber optic cables and the other using electromagnetic wave reflectometry.

For both the offshore wind and natural gas projects, the scientific challenge, Wu said, is optimizing the technology design and sensitivity and developing real-time edge computing technologies. “In addition to using commercial systems, our team is developing new fiber interrogators that will allow us to not only get to the original raw data but also play with the physics to better design a system that can give us the most sensitive signal we want,” he said. “In addition, we will be developing machine learning-based edge computing methods to turn raw data into actionable intelligence quickly. This is key for real-time monitoring.”

Source: Berkley Lab