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Asset performance and risk management: optimising maintenance in a data-focused world

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Oilfield Technology,


A reliable plant is a safe plant and a cost-effective plant.

When it comes to industrial assets, performance and risk go hand in hand. Business leaders need to be confident that threats to productivity, safety and the environment have been mitigated. Yet recent well-documented events, such a fire at a Philadelphia refinery which injured 37 people, or the ruptured gas line in Kentucky which proved fatal, are evidence that more needs to be done to avoid future catastrophic events.

When it comes to industrial assets, performance and risk go hand in hand. Business leaders need to be confident that threats to productivity, safety and the environment have been mitigated. Yet recent well-documented events, such a fire at a Philadelphia refinery which injured 37 people, or the ruptured gas line in Kentucky which proved fatal, are evidence that more needs to be done to avoid future catastrophic events.

Couple this with ever increasing scrutiny of performance and risk metrics and having a comprehensive view of asset performance and the risk of failure takes on even greater importance.

Asset owners/operators are seeking technologies that provide accurate, up-to-the-minute data for reporting and seamless decision making. They want assurance that any asset performance and risk repercussions have been identified and addressed.

When it comes to convincing management of the value of managing assets, taking a more focused approach to performance and risk management has clear rewards and nothing works better than demonstrated returns. Lloyd’s Register (LR) has helped clients do exactly that; for instance, one supermajor reduced OPEX by US$18 million and cut 74 000 hours of offshore labour in 12 weeks. Another offshore facility operator was able to increase safety while achieving a 30% reduction in planned maintenance. Both cases were achieved without compromising performance and safety.

It is efficiency on this scale that LR are striving to achieve industry-wide, which is why it is working with operators to help them develop a holistic, 360° understanding of their operating assets.

Unplanned downtime or worse, for example a containment incident, that harms people and/or the environment can set back production gains in an instant. Safe and reliable operations are business and moral imperatives. While technology to address these challenges has progressed and been made easier to deploy and connect to systems, adoption and implementation is often a struggle.

The result? An inability to fully address the causes of unplanned downtime, an overwhelming number of work orders and an inefficient use of maintenance staff time – putting their operations at risk.

A dynamic approach to asset performance and risk management

By applying new approaches that marry engineering excellence with technology and data, owners and operators are able to deliver actionable insights that pinpoint risk and enhance asset/plant performance. Examples of the most promising and impactful new technologies being implemented include:

Condition based maintenance

Condition-based maintenance is all about implementing maintenance strategies that monitors the actual condition of an asset (e.g. fixed equipment, piping, rotating, instrumentation) to decide what inspection and maintenance need to be done.

These strategies allow clients that both inspection and maintenance should only be performed when certain indicators show signs of decreasing performance, near future unreliability or upcoming failures.

Checking and validation of these indicators associated to such assets may include non-invasive measurements, visual inspections, real time performance data, integrity operating window data and scheduled interval tests.

Condition based maintenance can be applied to both safety critical and non-safety critical assets.

Predictive analytics

Predictive analytics and risk modelling capabilities are taking asset performance and maintenance to new levels, even more so with the use of artificial intelligence and machine learning.

The recent advances in big data, IIoT machine connectivity and cloud technology have created new opportunities to get actionable data from all types of industrial/plant assets. Currently, organisations in the heavy process industry are collecting vast amounts of structured and unstructured data; according to US data and software outfit Cisco, a typical offshore platform alone generates between one to two terabytes of data per day. However this data is often not being used, at least not to its full potential. AI-powered systems could have the ability to draw on this previously unmanageable amount of data to deliver unprecedented insights into the true state of assets.

Advanced analytics is another innovative use of data, which could enable automated interrogation of historic maintenance and inspection information, helping to derive forward-looking equipment reliability data and asset performance.

Typically performed manually or semi-automatically, a new, automated approach will allow large-scale maintenance optimisation and performance benchmarking at pace.

Quantitative maintenance optimisation

Quantitative maintenance optimisation allows modulation of maintenance activity, effort and cost in response to changing reliability, economic and safety factors.

In this way, a ‘live’ model of the facility is developed and planned maintenance activity can be scaled in response to changes in the technical and commercial environment. This means providing insight to perform the correct maintenance, at the correct time, in the correct way.

This helps address the hundreds, if not thousands of work orders generated for asset management teams who have limited ability to truly understand and prioritise the risk and consequence of failure. Budget reductions and an ageing workforce have also meant many organisations have lost the expertise needed to investigate why an asset failed, to prevent such failures in the future.

Using maintenance optimisation to cut through the noise and prioritise a huge volume of work orders has major benefits. It is LR’s experience that a net 30% reduction in planned maintenance can be delivered through these techniques when compared with conventional approaches.

Digital twins

Digital twins may have the potential to transform the future performance of assets by using new risk and reliability methodologies, failure data libraries, modelling tools and advanced analytics to process vast amounts of inspection and maintenance data.

Digital twins present a playground for asset teams, enabling enhanced scenario planning and allowing these teams to understand how business decisions could unfold. This technology is on the brink of becoming mainstream, with recent reports stating the market will grow at an annual rate of 33% over the next four years, therefore expect to see many more operational teams adopting digital twins in the coming months and years.

Integrating with Enterprise Asset Management (EAM) systems

Harnessing the power of these technologies can be a daunting task and an effective Enterprise Asset Management (EAM) system is critical. There are several factors, however, which may hinder the effectiveness of an EAM system and mean it is not possible to put these technology advancements to work.

This could include a lack of properly structured or accessible data; lack of access to subject matter expertise; a system set-up that is no longer relevant or helpful to ensure safe operations, minimise risk and manage OPEX efficiently; or the delivery of promised advanced analytical capabilities that are still in development is still yet to be complete.

Addressing these issues in order to reap the full rewards of maintenance optimisation may be seen as time-consuming, expensive and beyond your workforce’s capacity and capability.

But there are steps which can be taken to avoid these issues and increase the ROI of your EAM. These include:

  • Taking the time needed upfront to get design and implementation correct – this applies whether looking at a first EAM system, or re-structuring an existing set-up.
  • Using a multi-disciplinary team to design and implement the EAM, to correctly reflect how the system will be used in operations now, and in the future.
  • Specifying and optimising KPIs, so operational teams and managers can focus on workflow, corrective maintenance scheduling, work order prioritisation and backlog management.
  • Keeping total Cost of Ownership (TCO) as low as possible, even as equipment ages – even if production, product value or equipment configuration changes, there are simple but robust ways to optimise planned maintenance and OPEX to derive the lowest TCO.
  • Integrate an Asset Performance Management (APM) software platform to complement the EAM/CMMS investment.

It is also important that, with a properly functioning EAM system in place, to leverage the value in the available data. Terabytes of valuable information are often stored in these systems and full advantage must be taken of the insights and efficiencies on offer, to help inform optimisation decisions.

Setting up an EAM in an intelligent way can pay huge dividends and minimise OPEX. Combining this with a fully-functioning asset performance management solution will undoubtedly unlock enhanced analytical capabilities through a single platform for managing reliability and risk.

There is no denying that using the swathes of data intelligently in conjunction with an APM solution allows companies to tune their maintenance and inspection activity in response to changing reliability and commercial attributes of an asset or facility, minimise OPEX, reduce facility downtime and continuously demonstrate lowest TCO.

Author: Dr Neil Arthur, Lloyd’s Register

Read the article online at: https://www.oilfieldtechnology.com/digital-oilfield/23032020/asset-performance-and-risk-management-optimising-maintenance-in-a-data-focused-world/

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