Oil Algorithms: Improving Oil Spill Detection with Artificial Intelligence

Inspenet, June 14, 2023. Artificial Intelligence (AI) is projected as an effective tool to improve the prevention and detection of oil spills. Oil spills are a global phenomenon that represent a constant threat to the sustainability of the environment, society and also generate multiple financial losses.

Mayuly Rodríguez.

Published: June 14, 2023

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Author: Ing. Mayuly Rodríguez, June 14, 2023

Introduction

Artificial Intelligence (AI) is projected as an effective tool to improve the prevention and detection of oil spills. Oil spills are a global phenomenon that represent a constant threat to the sustainability of the environment, society and also generate multiple financial losses. Predicting or automatically detecting these incidents represents a fundamental action for rapid informed decision making.

Algorithms that detect oil spills in conjunction with the potential of artificial intelligence are topics of progress in the field of risk management and environmental protection. The use of AI is on the rise, because it has proven to be a tool capable of analyzing large volumes of data in real time, identifying patterns, early signs and historical trends that make it possible to predict the spill or act immediately upon detection.

The technological trend of AI is promising, regarding the ability to predict or detect oil spills in any area. Algorithms and machine learning techniques anticipate risks and establish preventive measures, which significantly reduce the number of oil spills currently occurring.

This not only safeguards the safety of operations, but also contributes to environmental sustainability and considers marine and terrestrial ecosystems that are so dependent on proper oil management.

Predictability and detection of oil spills

Identifying an oil spill accident on any surface as soon as possible is timely to implement monitoring and cleanup operations strategies. Understanding the factors and causes behind these incidents is essential for their prevention and effective controls.

The best way to address this problem is by examining the elements that contribute to the occurrence of spills and understanding what have been historical trends that serve as experience and lessons learned to anticipate these catastrophic events.

Oil spills occur due to factors such as operational or human errors arising from negligence, lack of training, economic interest, or failure to follow safety procedures. In addition, failures in the equipment used during oil operations, whether mechanical, electrical or structural, also give rise to spills.

Likewise, adverse environmental conditions, such as storms or hurricanes, represent an additional risk, since they increase the vulnerability of the facilities and hinder operations, increasing the probability of spills.

Analysis of historical spill patterns and trends effectively prevents these incidents. By considering the geographic location, types of spills and temporality, valuable information is obtained to identify high-risk areas, understand the causes and improve preventive measures.

To achieve this, it is necessary to implement monitoring and surveillance through real-time detection technologies. Train professionals and raise awareness about best practices and the importance of prevention. Likewise, regular maintenance and updating of equipment and infrastructure are necessary to prevent failures that could cause spills.

A review of artificial intelligence oil spill detection

Artificial Intelligence and its techniques can be called “Algorithms against Oil”, to analyze data in real time, obtain satellite information, radar images and data from underwater sensors, to detect anomalies and changes in environmental conditions, giving us a competitive advantage. in the race against oil spills.

In this digital age, where algorithms can process large volumes of data at high speed, early identification of negative scenarios is facilitated to offer an immediate response, making oil industries more productive and sustainable businesses.

In addition, AI has fueled the development of advanced spill simulation and prediction models. These models use AI techniques, such as neural networks and genetic algorithms, to simulate spill scenarios and assess their spread and potential impact. providing experts with valuable information to make decisions based on accurate data and design effective strategies.

Recent advances in AI have also enabled the development of early warning systems that automatically notify spill response teams if signs of incipient spills are detected.

Stories in the detection of oil spills with the support of Artificial Intelligence

Companies bet on innovative projects with more advanced technologies, with the aim of optimizing their resources and maintaining confidence in their operations.

Port of Antwerp-Bruges, Belgium: It has recently launched a network of six autonomous drones that carry out daily flights over the port area, covering various functions, including spill detection, in order to maintain better management in its operations. They are working with partners DroneMatrix, SkeyDrone and Proximus for the D-Hive drone in a network that will perform 18 daily flights of BVLOS (Beyond Line of Sight) drones that will be controlled remotely from command and control ¹ .

Copernicus Sentinel-1 data enables oil spill detection in the southeastern Mediterranean Sea: A German-Israeli research team used Copernicus Sentinel-1 data to train a learning-based oil spill detection system deep in the southeastern Mediterranean Sea, which can be used for early-stage warnings of oil contamination.

Researchers from the SAR Oceanography Team at the Institute for Remote Sensing Technology, German Aerospace Center (DLR), aim to aid surveillance of early-stage oil contamination with their detection system. The researchers chose the south-eastern Mediterranean Sea as their study area, as it is a known hotspot for oil spills ² .

Kongsberg Satellite Services (KSAT): In 2021, they made 11,000 detections of potential oil spills at sea worldwide using Synthetic Aperture Radar (SAR) ³ . Which is a radar system that consists of processing the information captured by the radar antenna through algorithms. This processing seeks to combine the information obtained in several sweeps of the antenna to recreate a single “virtual sweep”.

ARIEL – Repsol: The ARIEL project (Autonomous Robot for the Identification of Emulsified Liquids) aims to detect hydrocarbons in the sea through a multi-drone system that relies on the use of special cameras. In this way, we obtain a detailed analysis, which allows us to better target mitigation actions if necessary ⁴ .

Benefits and challenges of implementing AI in spill prevention

The trend of using Artificial Intelligence in the prevention and early detection of oil spills has shown significant benefits , but also poses important challenges and considerations. Let’s understand the key aspects of this implementation:

The use of AI-based systems in oil operations leads to a notable reduction in response times and effective damage mitigation. Having the ability to accurately and timely detect any indication that may lead to a possible spill, it is possible to intervene quickly and efficiently to prevent the occurrence and be more sustainable.

The economic savings and protection of the corporate image are strengthened with the prevention and detection of spills through Artificial Intelligence, reducing costs related to cleaning, repair and compensation. Avoid or mitigate negative impacts on the environment, which protects the image of companies, avoiding significant sanctions and fines.

In addition to the benefits, there are limitations due to the need to have a solid infrastructure and accurate updated data to guarantee the effectiveness of AI in preventing this type of accident; On the other hand, ethical considerations must be addressed in the best way, because concerns related to data privacy, transparency in the algorithms used and responsibility in automated decision making are generated.

It is essential to continue advancing the research and development of AI solutions to ensure timely management regarding oil spills in the future.

Conclusion

In the field of oil spill management, artificial intelligence emerges as a powerful ally, capable of radically transforming existing spill detection strategies. The application of advanced algorithms and the analysis of massive data allow rapid detection of spills, in favor of the environment, society and companies.

However, to make the most of the potential of AI, it is necessary to invest in cutting-edge technologies and foster the solid knowledge of the professionals involved, who must be prepared to successfully implement these technologies, understanding the challenges and limitations, and being aware of the ethical considerations involved.

The transformation that promotes the use of Artificial Intelligence in spill management drives a more proactive and safe approach in the industry globally. By incorporating cutting-edge prevention technologies, important steps are taken to contribute to a safer and more sustainable future.