Our Products

We develop and manufacture Hyper-spectral Laser Induced Fluorescence (HLIF) based LiDAR (LiDAR i.e. Light Detection and Ranging) sensors for our monitoring systems. As oil is intrinsically fluorescent and emits light in the blue-green spectral range when illuminated by UV light it is easily distinguished from other substances in the water.

Using the Hyper-spectral curve of the emitted light, we can “fingerprint” the sample, and thus receive a real-time identification of oil category and type (crude oil, hydraulic oil etc) on the spot, with no samples or lab analysis required.

Our Oil in Water Locator (OWL™) sensors detect and differentiate between various organic matter in the sea, such as hydrocarbons, Colored Dissolved Organic Matter (CDOM), etc. It is the particular wavelength we use in our lasers that define what substances we are able to detect. For oil in water, we typically use a LiDAR configured at 308 nanometer in order to be able to differentiate and classify various types of oil.

For algae in the water we typically use a laser configured at 530 nanometer to be able to differentiate between algae and other organic matter.

Data are presented in the OWL™MAP online presentation module. All our OWL™ sensors use Global Positioning System (GPS) to mark in OWL™MAP the exact location of a particular sample made. OWL™MAP is a map layer-based, real-time user subscriber-module which easily provides an early warning and a live situation / incident report when a sensor detects an anomaly at sea. With its sensor database and oil sample database, OWL™MAP quickly presents findings of oil in water; the position, volume (ppm) and type of oil.

OWL™MAP can be connected to additional online services, such as Automatic Identification System for ships (AIS). AIS makes it possible to display what vessels were present at a detection point in the hours before a detection was made. Further, OWL™MAP can integrate with Meteorological data, presenting wind direction and speed, wave height and direction, and most importantly for submerged oil; sea current and direction.

When correlating data and with machine learning building prediction models and other Artificial Intelligence (AI) systems it is crucial to have access to relevant and high quality sensor data.
That's what Ocean Visuals is all about. 

SEA OWL™ sensor aboard a Platform Supply Vessel

 

 

Enabling machine learning, data correlation, and prediction modeling

 

Machine learning can have several applications in the area of oil spill detection and recovery. Here's how it can be helpful:

  • Oil Spill Detection: Machine learning algorithms, especially convolutional neural networks (CNNs) and other deep learning models, can analyze satellite images to identify oil spills. These models can be trained on large datasets to understand different patterns and indicators of oil spills. They can even detect minor changes that might be missed by the human eye. The benefit of using such algorithms is that they can monitor large areas in a short amount of time, provide real-time updates, and aid in immediate action. Once an observation is made, we provide the sensor system that can measure and classify the oil, in real-time.
     
  • Oil Spill Characterization: Machine learning can also assist in characterizing the oil spill, such as estimating the size, shape, and spread direction of the oil spill. This information is crucial for coordinating the recovery efforts effectively.
     
  • Prediction Models: Machine learning can be used to create models that predict the movement and behavior of an oil spill based on a variety of factors such as ocean currents, wind direction, temperature, and the properties of the oil itself. This predictive capability can be of great help in taking proactive measures and minimizing the environmental impact of the spill.
     
  • Recovery and Cleanup: Machine learning can optimize the oil spill recovery process by predicting the most effective cleanup methods based on the nature of the spill, environmental conditions, and available resources. Machine learning algorithms can also analyze past recovery operations and suggest improvements for future operations.
     
  • Wildlife Impact Assessment: Machine learning can also help assess the potential impact on wildlife and the environment. By analyzing past incidents and environmental data, machine learning models can predict which areas and species might be most affected and help authorities take preventive measures.
     
  • Oil Spill Prevention: Finally, machine learning can also contribute to preventing oil spills in the first place. It can be used to monitor the condition and performance of oil transportation systems (like pipelines and tankers) and predict potential failures or leaks.


 

Correlating data in the context of oil spill detection and recovery can help streamline the identification process, enhance predictive models, and optimize cleanup strategies. Here's a more detailed view:

  • Enhancing Detection: Correlation between different types of data such as satellite imagery, weather data, and sensor data can help identify patterns that are indicative of an oil spill. For instance, correlating the visual patterns in satellite imagery with sensor data on oil presence can help enhance the reliability of oil spill detection algorithms.
     
  • Improving Predictive Models: Data correlation can also enhance the accuracy of predictive models that forecast the spread of an oil spill. By correlating variables such as wind speed, ocean currents, oil viscosity, and temperature, these models can provide more accurate predictions about the spill's spread, which can inform the emergency response.
     
  • Optimizing Cleanup Efforts: By correlating data from past oil spills, including the techniques used, their effectiveness, the type of oil, the surrounding environment, and the weather conditions, machine learning models can suggest the most effective cleanup strategies for future spills. These correlations can lead to more informed and efficient decision-making.
     
  • Validating Cleanup Efforts: Validation of cleanup efforts: Once the cleanup operations are initiated, hyper-spectral LIDAR sensor systems can be used to assess their effectiveness. By comparing pre- and post-cleanup data, the systems can detect changes in the oil slick's distribution and characteristics. Reduced oil thickness, diminished fluorescence emissions, or the absence of certain spectral signatures indicate successful cleanup efforts.
     
  • Monitoring Residual Contamination: Even after the primary cleanup is completed, residual oil may still be present in the environment. Hyper-spectral LIDAR sensors can continue to monitor the area to detect any remaining oil patches or sheens. This ongoing monitoring helps ensure that all necessary measures are taken to mitigate the long-term impact of the spill.
     
  • Environmental Impact Assessment: Correlation of data on the location and severity of oil spills with data on local ecosystems can help predict the potential environmental impact more accurately. This could include impacts on marine life, coastal regions, and even human populations.
     
  • Preventive Measures and Maintenance: Correlating data from equipment monitoring with instances of spills can help identify potential signs of failure or weakness in systems that transport or store oil. This predictive maintenance can prevent some spills from occurring in the first place.

It's important to note that while correlating data can provide many insights, care must be taken to ensure that the correlations are meaningful. It's also important to verify the quality and reliability of the data used, as inaccurate data can lead to incorrect conclusions. As with all data analysis, it's essential to remember that correlation does not always imply causation.

OWL™MAP presents both lidar measurement data and camera data in an easy-to-use graphical user interface

 

 

Predicting oil spill occurrence and the trajectory of an existing oil spill using meteorological data can be a complex task, due to the variety of factors that contribute to the behavior of oil in the environment. Here is a general overview of the process:

  • Data Collection: The first step is gathering and understanding the data you have. Data on oil spill incidents should include factors like the time, location, volume of the spill, type of oil, and the cause if known. Meteorological data may include wind speed and direction, precipitation, air temperature, and possibly other factors depending on the region and specifics of the data you're working with.
     
  • Understanding the Relationship: Once you have the data, it's important to study the relationship between oil spills and various meteorological factors. For example, wind can influence both the likelihood of a spill (in the case of shipping accidents) and the spread of a spill once it occurs. In most cases, this would involve statistical analysis to understand the relationships between various factors.
     
  • Building the Model: Based on the relationships you've identified, you can begin to build a predictive model. This might be a statistical model, a machine learning model, or some combination of the two. The model should take into account the various factors you've identified and provide predictions about either the likelihood of a spill or the trajectory of a spill given certain meteorological conditions.
     
  • Validation: Once the model is built, it should be validated with independent data to ensure its accuracy. This typically involves using a portion of your data for testing the model after it has been trained.
     
  • Implementation and Iteration: The model can then be used to make predictions and guide decision-making. Over time, as more data is gathered and the model's performance is assessed, it can be further refined and improved.

Keep in mind that oil spill prediction is a complex task and these models are generally probabilistic, providing a likelihood rather than a certainty. This process requires expertise in several fields, including meteorology, oceanography, data analysis, and computer science, among others. Also, the behavior of oil spills can be influenced by a range of factors beyond just meteorological conditions, including ocean currents, temperature of the water, biological factors, and the specific characteristics of the oil involved. For this reason, these models often incorporate a range of data sources beyond just meteorology.


In Norway, all aquaculture licenses are marked with their GPS spot in the OWL™MAP as points-of-interest (POIs), making it possible to warn the salmon farming community of acute oil or organics in the water:

SEA OWL™, Super OWL™ and ALGAE OWL™ Marine Sensor Systems

 

The Oil in Water Locator; OWL™ is a ruggedized, autonomously operating HLIF LiDAR sensor class weighing 43 kg, with dimensions of 37x45x65 cm, and with a typical measuring range of 15-50 meters. The SEA OWL™ is weatherized to withstand harsh weather such as extreme cold and is well suited for Arctic conditions. SEA OWL™ has operated over longer periods in tropical conditions in the Gulf of Mexico and Brazilian waters, equipped with a cooling unit that brings the sensor weight up to 49 kg.
Thanks to the online presentation program OWL™MAP, the sensor data can be presented with other layers of data, e.g. automatic identification of ships (AIS) and weather data.

Thanks to the online presentation program OWL™MAP, the sensor data can be presented with other layers of data, e.g. automatic identification of ships (AIS) and weather data.

Ocean Visuals services provide GIS integration via Web Feature Service (part of Open Geospatial Consortium standards).


SEA OWL™ SENSOR


COOLING UNIT

  • Real-time data feed
  • Complete solution consisting of oil detector and oil spill management software
  • Parts per million (ppm) measurement sensitivity level for hydrocarbons
  • Parts per billion (ppb) measurement sensitivity level for phytoplanktons
  • Measures the thickness of the oil on water
  • Detects oil in the water column (submerged oil) down to 3 meters
  • Detects phytoplankton (algae) in the water column down to 30+ meters
  • Operates in light and complete darkness and in harsh weather conditions 24/7/365
  • No false alarms, as the Lidar detects oil by sensing light response induced by laser beam hitting organic molecules in the water
  • SEA OWL™ may be mounted on any vessel for monitoring purposes such as an OSRV, PSV or drillship to provide real-time data from ongoing operations. 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.
     
  • ALGAE OWL™ may be mounted as a fixed installation on the side of a vessel like a wellboat, a fish feed transport vessel or a service vessel to provide detailed data for an early warning system of algae in the vicinity of aquaculture farms.

Sensor data are visualized in real-time via OWL™MAP – a web-based graphical map user interface. 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. Sensing results are delivered to operation centres and directly to persons responsible via SMS, e-mail or mobile push notifications. The OWL™ MAP system allows administrators to set alarm thresholds for detection levels of interest and incident reporting as well as alerts on different user levels for easy reporting. All data are securely stored in a cloud 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.
OWL™MAP is a multi-layer, multi-sensor presentation software. In addition to presenting several sensors, we also present other real-time information systems such as Automatic Identification System for ships (AIS), meteorological data and points of interest (POIs), such as aquaculture sites or oil rig / drilling sites in the map layer.

Ocean Visuals services provide GIS integration via Web Feature Service (part of Open Geospatial Consortium standards):
https://www.ogc.org/
https://www.ogc.org/standard/wfs/

  • Robust casing for harsh environmental conditions
  • Waterproof casing with isolated LAN and electrical connections
  • Easy fastening system for open deck installation
  • Unattended, self-controlled operation 24/7/365
  • Remote set-up of operational parameters
  • Eye-safe operation
  • Requires minimal on-site maintenance
  • Sensing distance: up to 50 m above water
  • Sampling rate: 10 HLIF spectra per second
  • Laser wavelength – UV (308 nm)
  • Detection: 500 channels, UV & VIS
  • Power consumption: 250 VA
  • Dimensions (65 cm x 45 cm x 37 cm)
  • Weight: 43 kg (arctic), 48 kg (tropical)

ELF OWL™ and AIR OWL™ Airborne Sensor Systems

Ocean Visuals offer a range of sensors for use on multi-rotor or fixed-wing UAV (drones), helicopters, and airplanes to meet the needs of governments and operators in the environmental monitoring and oil recovery preparedness and response arena.

Our systems are designed for stand-alone use or as part of a multi-sensor platform, reducing response time and eliminating time spent on taking water samples in critical operations.

AIR OWL™ and ELF OWL™ sensors provide our customers with real-time detection, verification and classification of type of oil in the water on the fly. We offer integration with maritime mission management systems.


The ELF OWL™ sensor system - designed for drones

ELF OWL™ is the worlds most compact Hyper-spectral Laser Induced Fluorescence LiDAR sensor. It has been specially designed with footprint and weight in mind. Measuring 25x30x45 cm and weighing only 22 kg, the ELF OWL™ has the same detection capacity as a SEA OWL™ system.

We designed the ELF OWL™ with aerial operations in mind, particularly coastal monitoring from the air, but also for ocean monitoring. We designed the ELF OWL™ system based on the analysis of the technical requirements needed for operations over water.
The  construction of the sensing laser (a customized model of the excimer laser with 308 nm emission) was redesigned for weight minimization and lower electromagnetic interference (EMI) to other electronics equipment onboard of a UAV. The optical layout of the ELF OWL™ was optimized to increase the light aperture by applying the multi-module telescope with fibre cable output coupled with hyper-spectral detector. New detector schematics was implemented to get more intelligence and functionality.

The ELF OWL™ system consists of operational control, data processing and communication of LiDAR controller and external control software. The LiDAR controller modification includes functionality for in-flight operation and interfacing with on-board flight control system. The current software system OWL™MAP for maritime vessel-based SEA OWL™ sensors has been used as a base. In addition to the modification of existing software the following new components and/or software integration is specified:

Tracking and monitoring of drones, integration of Aeronautical Information Service(s), (not to be confused with Automatic Identification Services, used for maritime vessel tracking);
Support for new data collection and compression methods to account for high(er) frequency data collection (due to drone flying speed differences to maritime vessels);
Integration of new communication channels, in addition to wireless data services, e.g. satellites (usually less bandwidth and more expensive, but may be the only option in remote areas)
End-user software interface adaptations to include (near) real-time drone location and data visualizations.

 


The AIR OWL™ sensor system - designed for airplanes

AIR OWL™ (Oil-in-Water Locator) is a Hyper-spectral Laser Induced Fluorescence (HLIF) LiDAR (Light Detection and Ranging) for airborne remote detection, identification and quantification of oil spill in water measured from a fixed wing aircraft. The principal layout of AIR OWL™ consists of  modules providing sensing from the air into the water with a laser pulse and the spectral detection of the echo-signal with following analysis.

An Onboard Mission Management System (MMS) is not a part of AIR OWL™ but serves as an external operator console integrating the basic control of the AIR OWL™ and data reception and visualization. The MMS sends the commands and receive the observation data layer for the full picture. The AIR OWL™ display data in a map that adds qualitative and quantitative information on oil pollution for real-time observation and decision making.

A laser is responsible for releasing powerful UV light pulses in regulated time slots. The Hyper-spectral detector consists of an optical telescope, a spectral unit (polychromator), and a multi-channel optical detector (MOD). The detector serves for collecting Laser Induced Fluorescence (LIF) light flux from a distant water surface, to disperse that light into linear spectrum, convert that spectral distributed light into multi-channel electrical signal with the aid of a gated intensified multichannel detector, perform digital readout of each of the channel value and record these spectral data. The resulting spectrum serves as an input for identification and calculation of the spatial distribution of pollution.


A Real Time Controller (RTC) is responsible for proper handling of simultaneous and time critical events. There are more essential functions incorporated in RTC that simplifies the overall operation of the HLIF sensor. An Onboard Control and Command Server (OCCS) serves for evaluating organic pollution distribution, quantity, alarms, maintaining HLIF GIS database, providing organic pollution distribution map layer in real time for the MMS.


A Space & Time Awareness Module (STAM) serves for binding each spectrum to GIS database, making it possible to locate each spectrum/sample/alarm on the map and in the timeline.


 

  • Oil field / platform management
  • Coastline monitoring
  • Pipeline inspection
  • Oil spill clean-up (effects of dispersion/burn off; before, under and after)
  • Response operations
  • Satellite observation verification and classification
  • Public environmental monitoring
  • Port / harbor monitoring
  • Real-time verification and classification of potential oil-in-water observations detected by satellite systems at sea.
  • Reduced reporting of false alarms created by OSD radar sensor systems in the oil industry by verifying the substance in the water on a molecular level, even in harsh environmental conditions (strong wind, low temperatures, fog).
  • Early warning / real-time environmental monitoring for detection of the smallest concentrations of pollution and respond before it becomes a significant problem.
  • On-site oil-type classification – verification of the hydrocarbon substance in the water based on LiDAR spectral analysis capabilities.
  • Sensor fusion provides more information, such as – thickest part of the spill, submerged oil, dissolved fractions and emulsions.
  • Enable decision makers to apply the most efficient response method, quicker.
  • Covers larger areas in shorter time.
  • More cost-efficient than traditional gathering of samples for verification and classification (lab analysis) at sea.