SEA OWL™ and Super OWL™

Marine Sensor System. Real-time monitoring of produced water discharge area. Ruggedized, autonomously operating HLIF LiDAR.

Sea OWL™ - Ocean Visuals

Complete solution

Oil detector integrated with our oil spill management software OWL Map™.

Real-time data feed

Get data as events occur, for better decision making and operational efficiency.

No more false alarms

Selective sensing of light response induced by laser beam hitting organic molecules.

Weight

43 – 49 Kg

Dimensions

65 x 45 x 37 cm

Sensing distance above water

50 m
Sea OWL™ - Ocean Visuals

Imaging technology

Hyperspectral LiDAR

Sampling rate

10 HLIF spectra/second

Laser wavelength

UV (308 nm)
Ocean Visuals Sea OWL™ mounted on ship railings.
Versatility

Use cases

The SEA OWL™ can be mounted on various vessel types, enabling real-time monitoring and data collection from OSRVs, PSVs, and drillships amongst others.

SEA OWL™ is capable of real-time monitoring of produced water discharge area. It is also capable of monitoring an oil storage facility like a loading buoy or terminal area.

Super OWL™ is designed with a pan/tilt gimbal, enabling reach within the 500 meter security perimeter surrounding a rig, FPSO or drillship.

Read case studies
Ocean Visuals marine sensor systems

Features overview

Real-time data feed
Complete solution of hardware and software
Detects oil in the water column down to 3 meters
Detects organics (CDOM) in the water column
Measures the thickness of the oil on water
Parts per million (ppm) sensitivity level for hydrocarbons and phytoplanktons
Parts per million (ppm) sensitivity level for hydrocarbons and phytoplanktons
Operates in light and complete darkness
Eye-safe operation
Remote set-up of operational parameters
Unattended, self-controlled operation 24/7/365
Easy fastening system for open deck installation
Ship in rough weather
rugged design

Weatherized to withstand harsh conditions

The SEA OWL™ can handle rough weather such as extreme cold and is well suited for Arctic conditions. SEA OWL™ has also operated over longer periods in tropical conditions in the Gulf of Mexico and Brazilian waters, equipped with a cooling unit.

Lineup of hardware.
Man looking at computer code

Machine learning, data correlation, and prediction modeling

Algorithms, convolutional neural networks and deep learning models can assist in the analysis of satellite images for early detection. Prediction models can provide useful event forecasts and help in the cleanup and handling process.

Monitor large areas quickly, identify subtle patterns and provide real-time updates for immediate action. By utilizing these models we can predict oil spill movement based on factors like ocean currents and wind, allowing for proactive measures.

Machine learning can also suggest the best cleanup methods and improvements by analyzing past operations and environmental conditions.When developing predictive models and AI systems using machine learning, access to relevant, high-quality sensor data is essential for accurate analysis and meaningful correlations.

Screenshot of OWL MAP™

Software

Sensor data are visualized in real-time via OWL™ MAP. Combine data with other layers of information, e.g. automatic identification of ships (AIS) and weather data.

For low bandwidth situations

The map user interface is built to handle low bandwidth situations, with possibilities for offline maps combined with online up-to-date information. The solution can be used on workstations onboard vessels or oil rigs as well as being a strategic management tool on shore.

Fast and reliable on-site data handling

Sensing results are delivered to operation centres and directly to persons responsible via SMS, e-mail or mobile push notifications.

Real-time matching to sample database

OWL™ sensors can match real-time findings of oil with an existing sample database, thus able to provide accurate classification of oil type in real-time. This saves the need for travel, man-hours and costly transportation to lab of traditional water sampling.

Learn about OWL™ MAP

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