Q1) What kind of data can be collected during remote monitoring and what information can be derived from this data?
Multiple types of data are collected. The most important data types to not list them all are:
Hyperspectral data: Information used to detect liquid or gaseous hydrocarbons on the ground and vegetation status.
Positioning data: High accuracy positioning and navigation information used to create visible or hyperspectral mosaics at high resolution and high precision down to a few centimeters to locate leaks, threats or geological data.
Visible images: High resolution imagery froorm a conventional RGB camera made for aerial surveys to detect threats and anomalies like missing markers or exposed pipes.
Q2) What are the key remote sensing technologies commonly used for detecting leaks in oil and gas pipelines, and how do they differ in terms of principle and application?
Typically, remote sensing divides in two categories: active and passive. Active systems use a light source (normally a laser, of which there are many types) to perform detection of specific molecules, whereas passive systems use an external power source (e.g. the Sun) and collect hyperspectral or multispectral data on which detection algorithms are applied.
Detecting leaks in oil and gas pipeline automatically is not a commonly adopted technology. Flyscan is a pioneer in this type of capabilityies for liquid pipelines and is pushing to have it adopted by operators considering its accuracy, robustness and sensitivity for crude oil and refined products. The technologies used by Flyscan are Short Wave Infra-Red Hyperspectral Push-Broom cameras for raw data acquisition, and Ultraviolet Raman Resonance for benzene detection, using a patented and proprietary UV laser active system with very high sensitivity (in ppb-m for benzene). For natural gas pipelines, active systems use infrared lasers.
Other technologies that are widely used are CPM (Computed Pipeline Monitoring, for larger leaks), fiber optic cables, acoustic sensors, floating sensors, or handheld gas detectors. Satellites can also be used for macroscopic leaks, for example using hyperspectral data. All those systems have their pros and cons and are complementary. Using them as a “system of systems” makes pipelines safe.
Q3) In terms of data analytics, how can machine learning algorithms be applied to remote sensing data to enhance the detection accuracy of pipeline leaks and reduce false positives?
Machine learning has been used for many years in remote sensing. It usually consists of training a model like a CNN (convolutive Neural Network) or other types on imagery of the targets of interest in various conditions. The model then uses a series of linear operation to highlight the targets of interest.
It is also widely used for image segmentation like detecting cars on images.
Flyscan leverages the power of machine learning on multiple products including threat detection and oil and gas detection. The threat detection machine learning algorithm can identify targets in images in a wide variety of object classess like heavy machinery trucks and help find un-authorized activity on the right of way. Hyperspectral machine learning algorithms help to discriminate between different types of hydrocarbons and also including plastics which are a common interferent for oil and gas detection.
These models take account of the contextual information meaning the images of the target but also what is around them.
Q4) Considering the need for timely response to pipeline leaks, how can remote sensing data be effectively integrated into emergency response protocols to ensure swift actions to mitigate environmental and safety risks?
Remote sensing data can be integrated in emergency response protocols by delivering high quality low latency actionable insights like position of a detected spill for example to allow for precise allocation of resources.
Flyscan also offers context like spill size, location and the level of contamination to help operators react quickly to these alerts. A robust notification system is also in place to signal spills. Our systems are either real-time or quasi-real-time, allowing for a fast response after natural disasters or spills. In addition, remote sensing data can be used to ensure clean up after an event has been successful by documenting the condition of the area and multiple follow ups give the ability to track restoration progress.
Q5) In what ways can cloud cover and extreme weather conditions interfere with remote sensing data acquisition, and what strategies are employed to mitigate these challenges?
Complete cloud cover and extreme weather conditions do not allow for passive remote sensing in the SWIR. In the case of satellites because they can not see through the clouds for certain bands, and in the case of airplane-based hyperspectral because there is not enough power from the sun light.
Partial cloud however can be dealt with using sophisticated algorithms like special filters or scattering compensation to improve signal strength.
Of course, in the case of airplane/helicopter data acquisition, if there is an extreme weather even such as a hurricane, the pilots will not fly, for safety reasons.
The best way to ensure reliable detection of spills is a regular patrol of the right-of-way. Like for medical examinations, routine frequent inspections find anomalies.
Eric Bergeron, Founder and CEO, Flyscan Systems
Eric Bergeron was Founder and CEO of Optosecurity, which developed, sold, and deployed airport security automation products around the world. The company was acquired by a division of
Toyota Industries. Prior to that he worked for start-ups, investment funds and large companies in Quebec, Virginia and The Netherlands. Eric is a senior member of the IEEE, and a member of the Quebec Order of Professional Engineers (P.Eng.). He has a BSc in Engineering Physics from Laval University, a MSc from the University of Quebec, and completed the Entrepreneurship Development Program at the MIT Sloan School of Management.
Alexandre Thibeault, Product Line Manager & Tech Lead,
Alexandre Thibeault leads the design and implementation of hyperspectral systems for hydrocarbon leak detection. This work empowers operators to proactively manage gas, oil, and diesel gas leaks, improving environmental safety. Previous roles involved satellite-based methane detection and the autonomous driving industry. He also worked at CERN, where he participated in antimatter laser cooling research that led to a co-authorship in Nature. He has a B.eng.in electrical engineering from university of Sherbrooke.