Artificial Intelligence in Floaters - Full Insights

Jul 7

In the offshore energy sector, floaters—such as FPSOs, FLNGs, and FSRUs—are complex and strategic infrastructures, often operating in remote environments with limited human resources. In this context, artificial intelligence (AI) emerges as a promising technology to reduce operational workload, enhance safety, and optimize performance, especially during process transients, failures, or emergency situations.

🚧 The Challenge: Limited Personnel

Many floaters operate with reduced crews, due to logistical, economic, or safety reasons. However, managing an offshore plant requires continuous monitoring of hundreds of process variables, rapid response to abnormal conditions, and critical real-time decision-making. Under these conditions, AI can become a key ally.

🤖 The Potential of AI

AI can be used to:

Detect anomalies before they become failures
Optimize transients (start-up, shutdown, production changes)
Support decision-making in complex or ambiguous situations
Automate responses to common or repetitive events

Machine learning models can learn from historical plant data to predict future behavior and suggest corrective actions.

🛡️ AI as a Barrier Health Screener

In addition to supporting operations and decision-making, AI can play a critical role in barrier management—a key aspect of safety in offshore facilities.

Barriers include systems such as:

Safety valves
Fire and gas detection systems
Emergency shutdown systems
Pressure relief devices
Instrumentation and control loops

AI can continuously analyze equipment and sensor data to detect degradation or failure, alert operators when a barrier is compromised, and prioritize maintenance based on risk exposure.

⚠️ The Challenges

1. Training the AI

Models must be trained with high-quality data that accurately represent real conditions. Transients and incidents are rare events, making them difficult to model effectively.

2. Operational Responsibility

Who is accountable if AI makes a wrong decision that leads to a plant shutdown? A clear governance framework is needed for AI use in operations.

3. Cybersecurity

AI systems can be vulnerable to external intrusion. Floaters are often strategic assets for companies and governments: a cyberattack could have geopolitical consequences.

📊 Insights from DNV

AI adoption is growing to improve cost efficiency and reduce emissions.
Market expected to grow from $3B in 2024 to $5.2B by 2029.
Only 15% currently use AI in live operations; 3% report advanced integration.
Trustworthy AI must be effective, efficient, secure, and safe.
Key applications include predictive maintenance, efficiency improvements, and sustainability.
Regulatory and cybersecurity challenges remain significant.

🌊 AI Use Cases Relevant to Floaters (from Apptunix)

🔧 Predictive Maintenance

Companies like Shell use AI to reduce equipment downtime and optimize maintenance schedules. This is especially critical for offshore units such as FPSOs and FLNGs, where access is limited and failures can lead to costly production interruptions.

🧪 Digital Twins in Offshore Facilities

Cairn (Vedanta) implemented AI-driven digital twins in its offshore operations, achieving:

30% reduction in flaring
18% optimization in fuel gas usage
These digital models simulate real-time operations, enabling operators to make informed decisions and improve energy efficiency.

🛡️ Leak Detection and Environmental Monitoring

AI systems use advanced sensors and satellite imagery to detect oil spills and hydrocarbon leaks quickly and accurately. This capability is particularly valuable for floaters, which operate in sensitive marine environments and must comply with strict environmental regulations.

AI spells opportunity and manageable risk for the oil and gas industry

Artificial Intelligence (AI) in Oil and Gas: Benefit and Use Cases