Predictive maintenance

Predictive maintenance in the context of the oil and gas sector is concerned with identifying subtle changes to assets before they become more significant issues, meaning firms can repair before leaks happen.

Why is better maintenance in the oil and gas industry a big concern?

Oil and gas firms are particularly interested in predictive maintenance for several reasons.

Maintaining oil and gas assets poses a huge ongoing head ache for the oil and gas industry. According to estimates, oil and gas producers suffer 32 hours of unplanned downtime each month, on average, at a cost of $220,000 per hour. As well as resulting in downtime and loss of product, leaks can result in costly fines from governments and regulators. Furthermore, because oil and gas assets are often in hard to access locations, sending teams of engineers to remote locations is expensive and poses logistical challenges.

As such, oil and gas firms increasingly cite predictive maintenance as a key strategic objective. But while the market is flooded with companies offering predictive maintenance services, few options have proved to be reliable and cost-effective.

The big opportunity in predictive maintenance is the ability to remotely monitor corrosion in real-time on an offshore oil field and identify new corrosion areas early on. This kind of monitoring is conventionally performed by engineers who physically inspect assets with equipment on average every two weeks, based upon integrity budgets. But while tried and tested, sending engineers to inspect pipelines is expensive and time-consuming for many firms and does not make use of the latest technological innovations. Our client sought a tailored technology blueprint and adoption roadmap for the next five years to help it harness enabling technologies.

What enabling technologies are there in predictive maintenance?

A range of enabling technologies are behind innovations in productive maintenance. These include real time spot sensors, which allow maintenance engineers to monitor previously identified corrosion spots, and robotics or crawlers. The latter of which can be effective but necessitate bulk buying, especially for oil and gas firms looking to monitor remote solutions.Machine learning algorithms can also predict corrosion spots as they develop over the next 6 months. Guided waves can also be used to identify new corrosion spots. These solutions put sensors along pipelines that can communicate with each other to determine if there is a change in wall thickness of the pipe, for example.

Predictive maintenance technology can come in a variety of forms. Our goal was to pinpoint what would make sense for our client. To assess the attractiveness of different options, we assembled a team of experts with detailed knowledge of fixed sensing solutions, robotics solutions and commercialising industrial applications of new predictive maintenance solutions.

After that, we pinpointed the right solutions and produced design for combining commercial vendors with original prototypes. Our experts conducted thorough technical analyses which assessed the error margins of metrics such as wall thickness, and operating temperature range.

The final work included a quantitative benchmarking analysis which looked at all the technical KPIs, their operational KPIs and their commercial KPIs. We assessed prototype technologies as well as technology that will be commercialised over the next 5 years. Our solution helped the client fulfil technology upgrades and performance improvements to help the client reduce their reliance on engineering services firms. The analysis demonstrated the possible upgrades to the solution and how it would improve our client’s KPIs.As a result, the company’s board authorised a pilot programme to execute a strategy in line with our suggestions.