Matthew Tremblay, ABS
Artificial intelligence (AI) technology applications are reshaping the most critical aspects of offshore operations today. Advancements in AI continue to accelerate, setting the course for a digital future that will enhance complex decision-making for offshore assets.
Over the last decade, the offshore industry has pushed the limits of what’s possible, utilizing smart technology, machine learning and data analytics to design and deliver “smart rigs” and high-specification drilling and production units capable of streaming big data for faster results.
On this odyssey to optimize operational efficiency, safety organizations like classification societies have a vital role in supporting the safe implementation of AI technologies.
Design optimization
AI, machine learning and digital innovations are taking the offshore industry into new realms of design, testing and remote operations.
To support rapid decision-making during the design phase, generative AI can assist engineers in analyzing more than 10,000 different design variations to determine the optimal design of an asset and structural equipment 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 process fluids based on site-specific loads.
Once a simulation or model-based design for an offshore unit is ready to test, AI then can enable engineers to robustly test and commission complex systems of systems. AI and domain assisted test programs performed in the digital space offer engineers a spot check by running a simulation and achieving 100% digital validation to confirm their technical analyses.
Autonomous and remote advancements
Another area where AI, digital enablement and autonomous systems add efficiency is in helping operators reduce crew numbers on offshore units. Having fewer personnel onboard could reduce the risk of human error, injury, and in rare incidents, loss of life.
Digitally enabled advanced operations deploy highly complex, self-aware and autonomous systems using AI’s machine learning and pattern recognition to advance self-diagnosis and self-awareness, so that a system can either correct its own issues or send alerts to shore. Systems are able to not only diagnose and correct what is happening in real time but can course correct on their own or have humans intervene from an offsite control center to act swiftly.
Applying digital tools to remotely assess the condition of an offshore unit, and automatically detect structural or performance issues, helps to improve safety and reliability for the offshore industry.
Predicting corrosion rates
The total cost of offshore and marine corrosion worldwide is between $50 billion to $80 billion per year. AI technology applied in offshore inspection has the potential to drive costs down significantly by offering a deeper level of insight into asset condition and predicting the rate of corrosion over time.
ABS initiated a pilot project applying machine learning models to detect levels of corrosion and coating breakdown on vessels and offshore structures. The project successfully demonstrated the accuracy of machine learning models in identifying and assessing structural anomalies commonly found during visual inspection using an image recognition tool.
Key learnings during this pilot project revealed that visual inspection data gathered by remote inspection technologies such as drones, crawlers, and remotely operated underwater vehicles have enormous potential to reduce costs and safety risks. Using machine learning technology, inspection data can be assessed automatically to identify and segment defects such as coating failures, corrosion, and structural damage.
Smarter and safer operations
Streaming data from an offshore asset is critical to help prevent failures and avoid nonproductive downtime. AI can help fill in the gaps to help predict and act before a failure can occur to keep assets running optimally.
Once data is uploaded in the cloud, an analytics engine powered by both the use of AI and the physics of the machine can find many nuances and patterns that might indicate a problem with asset health or performance. Asset owners and operators can use this vast reservoir of knowledge for predictive and proactive maintenance strategies.
The more AI systems learn, the smarter they will become in helping the offshore industry diagnose and prognose problems to facilitate faster corrective actions. Having a better understanding of asset performance and health and the condition of the asset and its equipment promotes safety, accuracy and efficiency in offshore operations.
As AI advances in offshore applications, a classification society’s role is to help the industry find the best way to incorporate these tools and techniques into operations with safety at the forefront.
AI models
A lot of the technological evolution is going to come from training AI models, and ABS is proactively assisting the industry with making sure that the goals that are set for these AI tools incorporate more than just efficient operations—they must incorporate safety and the protection of life, property and the natural environment. The manner in which AI helps operators analyze data and make decisions will have to be in line with the existing principles of safe operations offshore that the industry has promoted for the past 50 years.
Smart technology guidance
Today’s asset owners and operators are leveraging smarter technologies to analyze data from maintenance software to drive optimized performance and efficiency. As they look to integrate more AI and machine learning tools offshore, safety guidance specific to these assets and equipment is essential.
Updated in December 2023, the ABS Guide for Smart Functions for Marine Vessels and Offshore Units is a publication that supports smart technology implementation. This “Smart Guide” provides a goal-based framework and flexible approach to verify smart functionalities used in offshore applications. Setting goal-based standards allows for more flexible adoption of these new technologies while still maintaining the same level of safety.
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 smart 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 analytics, and data synthesis for reporting, decision making and actions.
Such guidance may be useful for companies starting to investigate AI tools and technologies to enhance operational efficiency.
