Lisa Chines
2024 HMEIC Chair
Senior Client Advisor, Global Energy and Power at Marsh
The offshore energy sector has been increasing their use of advanced technologies to improve efficiency, safety, and sustainability. Artificial Intelligence (AI) is at the forefront of this technological revolution, offering transformative potential across various aspects of offshore operations. Artificial Intelligence (AI) involves a broad range of technologies, including machine learning, neural networks, natural language processing, and robotics. In the offshore energy sector, AI is being utilized to improve operational efficiency, reduce operating costs, improve safety, and support the transition to renewable energy sources.
Enhanced Operational Efficiency
- AI algorithms can predict equipment failures before they occur, allowing for proactive maintenance and reduced downtime.
- AI-driven analytics can optimize drilling and production processes.
Improved Safety
- AI systems can analyze vast amounts of data to identify potential hazards and improve risk management strategies.
- AI-powered drones and robotics can perform inspections in hazardous environments, reducing the need for human presence.
Cost Reduction
- AI can streamline operations and reduce inefficiencies, which could lead to significant cost savings.
- AI systems can optimize energy use on offshore platforms, reducing operational costs and environmental impact.
Environmental Monitoring
- AI can monitor and analyze emissions in real-time, ensuring compliance with environmental regulations.
- AI tools can assess the impact of offshore activities on marine life and help in developing mitigation strategies.
Key Applications of AI in Offshore Energy
- Exploration and Production
- AI can analyze seismic data more accurately and quickly than traditional methods, improving the identification of oil and gas reserves.
- Machine learning algorithms can optimize drilling parameters to enhance efficiency and reduce non-productive time.
Asset Management
- AI models can use historical data to forecast equipment failures and identify patterns, leading to timely maintenance interventions. A leading oil and gas company implemented AI-powered predictive maintenance on its offshore platforms. By analyzing sensor data, the AI system accurately predicted equipment failures, reducing unplanned downtime by 20% and saving millions in operational costs.
- Creating digital replicas of physical assets allows for real-time monitoring and simulation, improving asset management and decision-making.
Safety and Risk Management
- AI-powered sensors and computer vision systems can detect leaks, structural weaknesses, and other safety hazards.
- AI can enhance emergency response procedures by predicting the outcomes of different scenarios and optimizing resource deployment.
It is also important to note, that while AI has many positive attributes, there are also a number of challenges to consider.
Data Quality and Integration
- Offshore energy operations can generate vast amounts of data from various sources. Integrating the data into one system can present a challenge due to the differences in formats, quality, and accessibility.
- In order for AI to operate effectively, the data quality must be accurate. The data needs to be vetted and reviewed.
- The existing legacy systems and current technological systems may not have the ability to understand each other, thereby requiring potential significant investment in upgrades and integration efforts to ensure that AI is working properly.
Reliability
- AI systems can fail due to software bugs, hardware malfunctions, as well as cyber-attacks, potentially leading to operational disruptions and lost time.
High Initial Costs
- Implementing AI solutions requires significant upfront investment in technology, infrastructure, and training, which can be an obstacle for many companies, especially smaller operators.
- The return on investment for AI projects can be uncertain, making it challenging to justify the initial expenditure without clear and immediate benefits.
Maintenance and Upgrades
- AI systems require continuous maintenance, updates, and training to remain effective. These ongoing costs can strain budgets, especially if the technology changes rapidly.
Security and Privacy Concerns
- AI systems can be vulnerable to cyber-attacks, which could compromise sensitive data and disrupt operations. Vigorous cybersecurity measures are essential to protect AI infrastructure.
- The extensive use of data in AI applications raises concerns about the privacy and security of sensitive information. Compliance with data protection regulations is necessary to mitigate these risks.
Regulatory and Legal Challenges
- The regulatory landscape for AI is still evolving. Ensuring compliance with existing and future regulations can be challenging and may require significant adjustments to AI systems and practices.
- Determining liability in case of AI system failures or malfunctions can be complex, especially when multiple parties (e.g., AI developers, operators) are involved.
AI has the potential to revolutionize the offshore energy sector by improving efficiency, safety, and sustainability. By strategically implementing AI solutions, companies can navigate the challenges of the energy transition and capitalize on new opportunities. However, it also presents a range of downsides that must be carefully managed. Addressing these challenges requires a balanced approach that includes robust technical solutions, ethical guidelines, regulatory compliance, and proactive workforce management. By recognizing and mitigating these downsides, the offshore energy sector can harness the benefits of AI while minimizing potential risks and adverse impacts.
Client advisors and risk professionals must stay informed about the latest AI technologies that are being utilized by their Offshore Energy clients. They should develop expertise in AI-related risks and offer proactive advisory services for technology integration and risk mitigation. Collaboration with AI providers and industry experts, along with providing tailored solutions, will help clients navigate AI adoption. Promoting the ethical use of AI ensures transparent, fair, and accountable practices, helping clients benefit from AI while managing its challenges.
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About the Author:
Lisa Chines
2024 HMEIC Chair
Senior Client Advisor, Global Energy and Power at Marsh
Lisa Chines has over 21 years of underwriting experience within the Energy sector. Currently, she serves as a Senior Client Advisor, Global Energy and Power at Marsh Specialty, one of the leading specialty insurance brokerage groups in the world. Lisa is currently the President of the Houston Marine & Energy Insurance Conference. She has served as Chairman of the AIMU Offshore Energy Committee and also was a member of the IUMI Offshore Energy Committee.
