Today, satellite-based technology is taking the monitoring of industrial infrastructure to the next level. DirectIndustry caught up with Michael Hall, a senior specialist at Airbus Defence and Space, to discuss remote sensing for energy infrastructure.
What are the main challenges when it comes to oil and gas infrastructure inspection? Why is such an inspection necessary?
Michael Hall: Inspection of oil and gas infrastructure is necessary to identify issues that may affect infrastructure integrity before damage or harm occurs to equipment, personnel, or the environment. Such infrastructure includes refineries, well pads, pipelines, and other facilities.
There are a number of challenges associated with such inspection. These include the wide spatial extent of infrastructure, with multiple sites spread across a large geographical area. Infrastructure may be in remote locations and based on difficult terrain. There may be potential safety risks to personnel, which means time on the ground has to be minimized. And it is often critical to identify potential hazards to infrastructures, such as encroaching vegetation or development disturbance before they affect integrity.
How can satellite-based inspection services help oil and gas companies overcome these challenges? What benefits do they offer?
Michael Hall: Satellite-based methods can make a number of key contributions, particularly in terms of assessing pipelines and facility monitoring. New images can be acquired at specified intervals, allowing frequent and up-to-date assessment. The fact that images are collected in real-time – over a period of time – not only means a true picture of the situation on the ground is provided but enables long-term trends to be assessed. Locations can be inspected that are geographically extensive, that are challenging to access on the ground, or where ground-based methods need to be supplemented.
The processing of multiple radar images allows ground deformation around infrastructure caused by oil and gas extraction, or other natural hazards such as landslides, to be identified. Satellite approaches can also provide insight on the health of vegetation, which may be impacted by oil escapes. Digital elevation models generated by the Airbus Constellation can assist in assessing terrain for planning inspection activities. And if a major incident is reported at a facility, or to a key infrastructure component, imagery can be rapidly tasked to provide an up-to-date view of the situation on the ground.
Can you tell us about some of the satellite technology involved?
Michael Hall: Satellite technology is developing rapidly and very high resolution images are now available. Optical images are acquired with resolutions up to 30 cm (Pléiades Neo), and radar images up to 25 cm (TerraSAR-X). The recently launched Pléiades Neo HD15 imagery product now offers an incredible 15 cm resolution.
These very high resolutions allow really detailed inspection of pipelines, facilities, machinery, and equipment. Selection of the most appropriate sensor depends on the specific application and cloud cover levels, with radar sensors able to image through cloud cover, offering monitoring independent of weather and daylight conditions.
Satellites are increasingly being launched as a series of identical satellites, working together to fulfill tasking demands. The Pléiades Neo constellation, for example, will comprise four identical satellites when fully operational. This constellation approach has a number of advantages, including greater capacity for new image acquisition, and the capacity to acquire images over an individual location more frequently.
Does Airbus have any new technology or services in the pipeline? How do you see satellite technology evolving over the next few years?
Michael Hall: Currently two Pléiades Neo satellites are in orbit around the Earth. Two more are on track to be launched later this year, which will further increase image acquisition capacity and reduce revisit times for infrastructure monitoring applications.
Ongoing developments in automation and machine learning will increasingly enhance the analysis of source images, especially where traditional manual analysis may constrain project delivery frequency or area of interest.
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