From Reactive to Proactive: The Power of Predictive Maintenance in Offshore Installations
Published on : Friday 30-06-2023
Industrial Automation presents: Unlock the power of proactive asset management and seamless offshore installations using predictive maintenance in the offshore industry.
Photo by Dean Brierley on Unsplash
Are you a professional or practitioner working with offshore installations who want the infrastructure and installations to run smoothly? Discover how predictive maintenance plays a crucial role in offshore installations and operations. Also, examine the challenges in maintaining offshore installations as well as the latest applications of predictive maintenance in offshore installations and upcoming trends. So let’s get started.
The offshore industry is essential for supplying the world's energy requirements, advancing global trade, and utilising renewable energy sources. The sector supports a variety of industries' technological advancements, employment creation, and global economic growth. Alongside its importance across sectors, there are numerous challenges companies face in the offshore industry. Offshore installations or infrastructure, such as oil rigs, wind farms, and undersea structures, operate in harsh conditions where maintenance may be costly, time-consuming, and possibly dangerous.
In order to ensure the seamless and effective operation of these installations, it demands specialised equipment, technologies, and expertise. Predictive Maintenance is one of the crucial maintenance tools or strategies that utilises the concepts of data analysis, machine learning algorithms, and sensor technologies to predict when equipment or machinery is likely to fail. Predictive maintenance is crucial for maximising operational effectiveness, decreasing downtime, and ensuring worker safety in the context of offshore installations.
As predictive maintenance is built on condition-based monitoring to optimise the performance and lifespan of equipment by analysing its health in real time, it has been gaining traction across various industries. The MarketsandMarkets report suggests that the market size for predictive maintenance is forecast to reach US$15.9 Billion by 2026 from US$4.2 Billion in 2021, growing at a CAGR of 30.6% during the forecast period.
Challenges in the maintenance of offshore installations
Maintenance of offshore installations presents different challenges due to their unique operating environment and remote locations. These difficulties may affect the scheduling, execution, and general asset reliability of maintenance. It takes a multidisciplinary approach to tackle these problems, one that includes cutting-edge technology, smart risk management strategies, strong safety procedures, and the latest engineering and operational practice innovations. The following are some significant challenges to maintaining offshore installations:
• Transportation and logistics
• Harsh environmental circumstances
• Safety risks
• Remote controlled monitoring and analysis
• Constrained workspace and working conditions
• Marine growth and corrosion
• Logistics and cost management
• Regulatory compliance
• Asset reliability, and
• Technical complexity.
Apart from these challenges, minimising greenhouse gas emissions is a major issue for offshore companies to meet environmental compliance.
Carbon footprint challenges in offshore installations
While playing a crucial role in fulfilling global energy demands, offshore installations present certain challenges when it comes to carbon footprint. Addressing these challenges in offshore installations requires a multi-faceted approach.
A number of leading technology firms, like ABB, has taken revolutionary steps towards dealing with such issues. For example, ABB’s URAS system, which is an Ultra Red Absorption Recorder, inspects the composition of gases in real time utilising infrared measurement technology. “This technology was truly revolutionary from the day it was invented. Today, it is a benchmark for uncompromising environmental compliance. With URAS, numerous industrial companies around the world are able to monitor emissions and safeguard the environment,” said Ben Goossens, Global Product Line Market Manager, ABB Measurement & Analytics.
Another technology is ABB’s advanced OA-ICOS™, which is widely acknowledged for delivering reliable measurements of greenhouse gases. This emissions monitoring technology is mainly used by oil and gas companies and natural gas utilities to detect gas leaks.
In 2022, the United States Environmental Protection Agency published a report stating that its researchers built a new air monitoring technology that helps understand leaks and irregular emissions from sources. The technology, named prototype fenceline sensor pod (SPod), which is a low-cost, portable sensor system, was deployed near chemical facilities to help improve understanding of volatile organic compound (VOC) emissions in the area. Such technologies significantly help in reducing the carbon footprint in offshore installations and enhancing effective operations.
What benefits does predictive maintenance have for offshore installations?
Offshore installations or infrastructure can be found in industries like oil and gas, exploration and production, renewable energy generation (offshore wind farms), marine transportation, resource extraction, and subsea engineering and services. Since these sectors are subject to various regulations and pose environmental risks, predictive maintenance comes to the rescue.
Predictive maintenance systems determine when equipment will fail by using predictive algorithms and data from the equipment's sensors. These systems monitor equipment health to prevent failures during operation. Predictive maintenance is important as it can benefit businesses and organisations in a number of important ways. Here, we have discussed the significance of predictive maintenance in offshore installations.
Improved Equipment Reliability and Availability: Predictive maintenance helps increase the reliability and availability of equipment by foreseeing potential failures and addressing them beforehand. This lessens unanticipated failures and unscheduled downtime, enabling businesses to increase productivity and operational efficiency.
Cost Savings: By maximising maintenance activities, predictive maintenance can result in cost savings. Early problem detection and resolution can help organisations avoid expensive major repairs, lessen the need for emergency maintenance, and reduce production losses brought on by equipment failures.
Improved Safety: A safer workplace can result from predictive maintenance. By identifying and fixing potential equipment failures in advance, the risk of accidents or incidents caused by flawed equipment can be minimised. This reduces the possibility of environmental harm while preserving the safety of the workforce.
Resource allocation and improved maintenance planning: Predictive maintenance provides businesses with insights into the condition and health of equipment, allowing for more effective maintenance planning. With this, they can reduce the need for unnecessary preventive maintenance procedures by optimising maintenance schedules, effectively allocating resources, and scheduling maintenance work more frequently.
Longer Equipment Lifespan: Predictive maintenance helps increase the lifespan of equipment by spotting and resolving potential issues before they become serious. Proactive repairs and timely maintenance actions can hinder further deterioration and minimise the rate of wear and tear, ultimately enhancing the durability of assets.
Data-Driven Decision-Making: Predictive maintenance decision-making is based on advanced algorithms and data analysis. Companies that collect and analyse real-time equipment data can learn a lot about the performance, behavior, and health of their assets. This data-driven approach enables decision-making based on data and makes it simpler to continuously improve maintenance strategies.
Operational Efficiency: Predictive maintenance improves operational efficiency by lowering downtime, enhancing equipment dependability, and streamlining maintenance tasks. Organisations can increase output, cut operating expenses, and enhance overall equipment effectiveness (OEE) using predictive maintenance systems.
Therefore, predictive maintenance uses real-time data and analytics to predict and prevent failures before they happen instead of adhering to a set maintenance schedule or waiting for equipment to break down unexpectedly.
The use of technology in offshore installation
Technology is critical in offshore installations, particularly in inaccessible locations such as beneath the offshore platform. There are a number of technologies that can be applied to improve the efficiency and safety of such environments. Some of the revolutionary technologies are discussed below.
Robotics and Automation: In offshore installations, robotics and automation technologies are used to complete tasks that are challenging, hazardous, or inaccessible to humans. The use of remotely operated vehicles (ROVs) is indispensable for underwater maintenance, inspections, and repairs. Additionally, autonomous systems are being developed to complete rote tasks and minimise human involvement in dangerous environments.
Safety and Emergency Response System: Technologies like advanced monitoring systems, alarm systems, and safety equipment are crucial for ensuring safety and enhancing emergency response capabilities in inaccessible offshore locations. These systems enable early detection of potential hazards, which helps in enhancing emergency response times and facilitating swift evacuation and rescue operations.
Digital Twins: Digital twin technology creates virtual replicas of physical assets, allowing for a digital representation of offshore installations. Operators can monitor asset performance, predict behavior, and optimise maintenance strategies by combining real-time data, simulations, and analytics. Digital twins help in identifying problems early, optimising asset utilisation, and supporting informed decision-making for offshore installations.
Internet of Things (IoT): IoT technologies make it possible for the devices and sensors installed throughout offshore installations to connect and communicate with one another. Operators can monitor and manage various systems and pieces of equipment, gather real-time data, and enable data-driven decision-making by integrating IoT devices. For improved operational efficiency, IoT enables seamless communication, remote monitoring, and automation.
Communication Infrastructure: For offshore installations in remote locations, a reliable and strong communication infrastructure is crucial. Through satellite communications, wireless networks, and subsea cables, data can be transferred seamlessly between offshore platforms, onshore facilities, and distant monitoring centers. As a result, real-time communication, remote assistance, and data transmission are made possible for effective operations.
Thereby, from advanced monitoring and controlling systems to robotics, communication infrastructure, digital twin technology, and safety and emergency response systems, advancements in technology enable efficient operations, proactive maintenance, and improved safety measures in challenging offshore environments, especially in inaccessible locations below the offshore platform.
Companies utilising predictive maintenance in offshore installations
Predictive maintenance leverages advanced data analytics, machine learning algorithms, and real-time monitoring systems to forecast potential equipment failures, enabling proactive maintenance actions. By adopting predictive maintenance practices, companies operating offshore installations can gain several key benefits, including improved operational efficiency, reduced downtime, enhanced safety, and optimised maintenance planning. To understand the benefits and significance of predictive maintenance systems, we have discussed below some real-world case studies of companies operating in the offshore industry and using predictive maintenance.
TAQA
An international energy and water company, TAQA utilises predictive maintenance software for its North Sea oil and gas platforms. For this, the company collaborates with predictive analytics company VROC, whose AI predictive analytics software recognised numerous proactive interventions during the product trial.
“The technology allows industrial businesses to optimise their processes, improve the reliability of assets and production, whilst delivering OPEX savings to the business," according to VROC.
BP
Leading energy company BP has been exploring the use of predictive maintenance systems in its offshore facilities. In order to increase equipment uptime, the company has implemented predictive maintenance technologies at its upstream operations.
“Innovation is the key to addressing the future growth of the energy sector. With electricity demand increasing so rapidly, we have to be in a constant state of evolution in order to solve the problems of the future,” Lightsource BP’s chief operating officer, Kareen Boutonnat, said.
Shell
The international oil company Shell has been utilising predictive maintenance techniques in its offshore operations. The company has focused on leveraging data analytics, predictive models to optimise maintenance schedules and improve asset performance, and AI-assisted safety in operations.
In a recent event, Neisha Kydd, operations safety manager for Shell Gulf of Mexico, discussed Shell's deployment of exception-based surveillance. She said, “It’s a form of proactive monitoring … on steroids. It allows us to have early interventions before we actually have safety incidents.”
It is indispensable to keep in mind that different companies implement predictive maintenance in different ways and to different extents, using different methods.
Major players in the offshore industry adopted predictive maintenance systems
The predictive maintenance model can be used to monitor a variety of assets, including centrifugal pumps, motors, turbines, and wellheads, both onshore and offshore. In the early days, no one could have predicted that the offshore industry would rely on IT, particularly predictive maintenance. However, the industry has recently seen significant developments and advancements that have emphasised AI-based predictive maintenance systems and technologies. We have listed below some of the major players using predictive maintenance on offshore installations.
GE Digital: GE Digital offers a variety of predictive maintenance solutions, including their flagship software, Predix, which provides real-time monitoring, analytics, and predictive capabilities. The company’s solutions are intended to improve asset performance, maintenance planning, and reliability on offshore installations.
Siemens Energy: Siemens Energy provides advanced predictive maintenance solutions for offshore installations, with a focus on renewable energy. Data analytics, machine learning, and digital twin technology are used in their systems to enable condition-based monitoring, early fault detection, and optimised maintenance strategies.
ABB: ABB, a pioneering technology leader, offers predictive maintenance solutions based on artificial intelligence (AI) and machine learning (ML) algorithms. Their systems enable real-time monitoring, anomaly detection, and predictive analytics for offshore installations. ABB's solutions help in optimising maintenance schedules, reducing downtime, and improving asset performance.
Honeywell: A leading engineering and technology company, Honeywell provides predictive maintenance solutions based on advanced analytics, machine learning, and industrial internet of things (IIoT) technologies. The company’s systems enable real-time asset monitoring, failure prediction, and prescriptive maintenance recommendations for offshore installations, resulting in increased equipment reliability and operational efficiency.
DNV GL: DNV GL is a leading provider of predictive maintenance solutions and consulting services to the offshore industry. To optimise maintenance strategies, increase asset lifespan, and improve safety and compliance in offshore installations, the company’s systems integrate data analytics, risk-based methodologies, and asset integrity management.
AspenTech: AspenTech is a leading provider of asset management software worldwide. It offers predictive maintenance software solutions that integrate process data analytics, machine learning, and AI. Their systems offer offshore installations real-time monitoring, predictive modeling, and anomaly detection capabilities, enabling proactive maintenance and asset performance optimisation.
These are some of the key players that offer predictive maintenance solutions to the offshore industry. Predictive maintenance services are also provided by other businesses such as specialised niche players and sector-specific solution providers. These solutions are specifically geared toward the demands of offshore installations.
Upcoming trends in predictive maintenance for offshore installations
Predictive maintenance in offshore installations is an evolving technology that continues to witness advancements driven by technology and industry needs. Several upcoming trends are shaping the future of predictive maintenance in offshore installations. Companies can maximise the use of predictive maintenance technologies in offshore installations by keeping up with the latest as well as upcoming trends. We have discussed below some of the upcoming trends in predictive maintenance for offshore installations that every business must watch out for.
IoT and Sensor Technology: The Internet of Things (IoT) and sensor technology are revolutionising predictive maintenance in offshore installations thanks to their widespread adoption. IoT-enabled sensors are widely used to gather real-time information on the performance, health, and environmental conditions of equipment. These sensors allow for continuous monitoring and early anomaly detection, providing valuable information for predictive maintenance measures.
Big Data Analytics and Machine Learning: Big data analytics and machine learning are increasingly being used in predictive maintenance. Big amounts of data gathered from offshore assets, including sensor readings, previous maintenance logs, and external data sources are analysed by advanced algorithms. Machine learning models have the ability to recognise patterns, anticipate failures, and optimise maintenance approaches, improving asset reliability and decreasing downtime.
Digital Twins and Simulation: Predictive maintenance for offshore installations is witnessing increased use of digital twin technology. Through the development of digital twins of physical assets, real-time monitoring, simulation, and predictive analysis are made possible. Digital twins facilitate proactive maintenance planning and help operators make informed decisions to maximise asset efficiency by simulating asset behavior, maintenance scenarios, and performance optimisations.
Edge Computing and Real-time Analytics: As edge computing becomes more prevalent, it brings data processing and analytics closer to the data source, reducing latency and enabling real-time analysis. Edge computing allows offshore installations to perform data analytics on-site, resulting in faster anomaly detection, faster decision-making, and immediate action implementation for critical maintenance activities.
Artificial Intelligence (AI) for Predictive Analytics: AI is becoming increasingly important in predictive maintenance, allowing for more accurate and precise predictions. To improve failure prediction, AI algorithms can detect subtle patterns, perform complex data analysis, and continuously learn from data streams. AI-based predictive analytics enables offshore installations to transition from rule-based models to more advanced and dynamic models, thereby improving maintenance effectiveness.
Remote Monitoring and Augmented Reality (AR): For offshore installations, predictive maintenance strategies are increasingly incorporating remote monitoring solutions. With the help of remote monitoring, maintenance staff can keep an eye on the condition of their equipment, work with onshore specialists to resolve problems, and more. Augmented reality (AR) is also used to deliver training and remote assistance, enabling in-the-moment instructions for maintenance tasks and boosting productivity.
Integration with Asset Management Systems: To simplify maintenance procedures, predictive maintenance is being integrated with asset management systems. Companies can streamline maintenance scheduling, give work orders top priority, control spare parts inventory, and centrally track equipment history by integrating predictive maintenance data with asset management systems.
These upcoming predictive maintenance trends have the potential to reshape the offshore industry by increasing asset reliability, optimising maintenance practices, and increasing operational efficiency. By adopting these trends, companies operating in the offshore industry can reduce downtime, cut costs, and extend the life of their critical assets.
Conclusion
Predictive maintenance is a critical tool for offshore installations, allowing companies to manage assets proactively, improve operational efficiency, increase safety, optimise maintenance practices, and extend equipment lifespan. In a difficult and dynamic environment, offshore companies can gain a competitive edge, cut costs, and ensure the dependability and durability of their offshore assets by adopting predictive maintenance.
Although there are numerous challenges to overcome when implementing predictive maintenance in offshore installations, including data management, system integration, and cultural changes, the advantages outweigh these challenges by a wide margin. As predictive maintenance detects and addresses potential equipment failures before they occur, we at Industrial Automation believe that more businesses and service providers will equip themselves with AI-enabled predictive maintenance systems that will allow them to mitigate risks and maximise the return on investment in offshore assets.
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