Matthieu Tremblay, ABS
Technological applications of artificial intelligence (AI) are reshaping the most critical aspects of offshore operations today. Advances in AI continue to accelerate, paving the way for a digital future that will improve complex decision-making around offshore assets.
Over the past decade, the offshore industry has pushed the boundaries of what is possible, using smart technology, machine learning and data analytics to design and deliver “smart rigs” and drilling units and high-specification production systems capable of streaming Big Data for faster results.
In this odyssey to optimize operational efficiency, security organizations like classification societies have a critical role to play in supporting the safe implementation of AI technologies.
Design Optimization
AI, machine learning and digital innovations are taking the offshore industry to new areas of remote design, testing and operations.
To support rapid decision-making during the design phase, generative AI can help engineers analyze over 10,000 different design variations to determine the optimal design of a structural asset and equipment by based on performance, maintenance and safety data. Synthesizing information with speed and scale, AI technologies can help offshore asset designers select better configurations for steel weight as well as systems used to optimize processing fluids based on specific loads on the site.
Once a simulation or model-based design for an offshore unit is ready for testing, AI can then enable engineers to robustly test and commission complex systems of systems. AI-assisted and domain-based testing programs performed in the digital space provide engineers with a spot check by running a simulation and obtaining 100% digital validation to confirm their technical analyses.
Autonomous and remote advances
Another area where AI, digital enablement and autonomous systems add efficiency is in helping operators reduce crew numbers on offshore units. Having fewer personnel on board could reduce the risk of human error, injury and, in rare incidents, loss of life.
Digital advanced operations deploy highly complex, self-aware and autonomous systems, using machine learning and AI pattern recognition to advance self-diagnosis and self-awareness, so that a system can either correct its own problems or send alerts ashore. The systems are capable of not only diagnosing and correcting what is happening in real time, but also correcting the problem themselves or requiring humans to intervene from an off-site control center to act quickly.
Applying digital tools to remotely assess the condition of an offshore unit and automatically detect structural or performance issues helps improve the safety and reliability of the offshore industry.
Predicting corrosion rates
The total cost of offshore and marine corrosion worldwide is between $50 billion and $80 billion per year. AI technology applied to offshore inspection has the potential to significantly reduce costs by providing a deeper level of information on the condition of assets and predicting the rate of corrosion over time.
ABS has launched a pilot project applying machine learning models to detect levels of corrosion and coating degradation on ships and offshore structures. The project successfully demonstrated the accuracy of machine learning models in identifying and evaluating structural anomalies commonly found during visual inspection using an image recognition tool.
Key lessons learned from this pilot project revealed that visual inspection data collected by remote inspection technologies such as drones, crawlers and remotely operated underwater vehicles has enormous potential to reduce costs and safety risks. Using machine learning technology, inspection data can be automatically evaluated to identify and segment defects such as coating defects, corrosion and structural damage.
Smarter, safer operations
Streaming data from an offshore asset is essential to prevent outages and avoid non-productive downtime. AI can help fill the gaps to enable prediction and action before a failure occurs to ensure assets are operating optimally.
Once the data is uploaded to the cloud, an analytics engine powered by both the use of AI and machine physics can find many nuances and patterns that may indicate a problem with the health or performance of the machines. assets. Asset owners and operators can use this vast reservoir of knowledge to develop predictive and proactive maintenance strategies.
The more AI systems learn, the smarter they will become to help the offshore industry diagnose and prognosticate problems to facilitate faster corrective actions. Having a better understanding of asset performance and health as well as the condition of the asset and its equipment promotes safety, accuracy and efficiency of offshore operations.
As AI advances in offshore applications, the role of a classification society is to help the industry find the best way to integrate these tools and techniques into operations with safety at the forefront.
AI models
Much of the technological evolution will come from training AI models, and ABS is proactively helping the industry ensure that the goals set for these AI tools incorporate more than just efficient operations: they must integrate the safety and protection of people. life, property and the natural environment. How AI helps operators analyze data and make decisions will need to be consistent with existing principles of offshore operations security that the industry has been promoting for 50 years.
Smart Tech Tips
Today’s asset owners and operators are leveraging smarter technologies to analyze data from maintenance software to optimize performance and efficiency. As they look to integrate more AI and machine learning tools overseas, security guidelines specific to these assets and equipment are essential.
Updated December 2023, ABS Guide to smart functions for marine vessels and offshore units is a publication that supports the implementation of smart technologies. This “Smart Guide” provides an objective-based framework and flexible approach for verifying smart features used in offshore applications. Establishing goal-based standards allows for more flexible adoption of these new technologies while maintaining the same level of security.
Common smart functions include structural and machinery health monitoring, asset efficiency monitoring, operational performance management, and crew assistance and augmentation to support offshore operations. These intelligent functions are enabled by data infrastructure and supported by robust software integrity and cybersecurity that facilitate the use of aggregated data from sensors and other sources, data analysis and data synthesis for reporting, decision-making and actions.
Such advice can be helpful to businesses that are beginning to explore AI tools and technologies to improve operational efficiency.
