How Data Analytics Transforms Planned Downstream Maintenance into Predictive Scheduling

How Data Analytics Transforms Planned Downstream Maintenance into Predictive Scheduling


With the recent recoveries in oil prices, most oil and gas companies have started to see a light at the end of the tunnel. They are now prepared to ramp up their investments to either grow or maintain their current output levels.

Production can increase by developing new fields or improving the uptime of assets from their existing fields. Asset downtime, with the help of planned or unplanned facility maintenance, is costing companies millions in lost revenue on an annual basis. According to a recent survey conducted by ARC, organizations have been set to lose 3 to 5 percent of production levels because of unplanned downtimes.

According to the data above, we can discuss a hypothetical situation. An oil production facility may be able to produce 10,000 barrels on a daily basis. A 1% gain on their utilization of assets can easily result in several millions of dollars of additional revenues. This is exactly why these companies need to address all the challenges they face because of unplanned downtimes. If they do so, then they will have the ability to reap huge awards.

For oil and gas companies operating at very high levels of efficiency, they have to increase productivity while keeping their costs in control. This is a very challenging task because these companies are rich in assets and emphasize equipment reliability and safety.

Therefore, to limit downtimes and minimize risks, oil and gas companies should leverage advanced analytics and industrial data. This leads to the execution of predictive maintenance, which consequently empowers these companies to act before their equipment actually fails.

Challenges of Keeping Assets Running Optimally in Remote Fields

Whether onshore or offshore, all the production assets of gas and oil fields tend to be situated in environments that are non-ideal. This means that they are exposed to harsh operational conditions on a constant basis. In such environments, keeping up with production goals can be a real challenge.

Changes in the profiles of asset loading is another issue oil and gas companies have to face, which is usually the result of declining production levels over time. As a result of this, operators find it harder to detect issues in their equipment with the help of their monitoring systems for current assets. Oftentimes, because of these problems, failures tend to happen while leading to outages in production.

This situation is avoidable with some appropriate maintenance strategies in place. These strategies can be based on asset criticality, type, maintenance history, and the businesses’ purpose and goals.

What Problems Do Outdated Maintenance Strategies Cause?

One of the most commonly adopted approaches with regard to preventative maintenance by oil and gas companies includes usage statistics – which is based simply on time. In simpler terms, this means that maintenance is carried out in these companies at regular intervals so as to keep the operations running and reduce the probabilities of asset failure.

In most cases, however, these strategies usually result in under or over maintenance of assets. This is mainly because of the differences in how equipment ages, unpredictability in their performance, and even the unpredictability of the operating environment.

Over-maintenance results in the costs of unnecessary production downtimes while under maintenance exponentially increases the risk of equipment failures. Under maintenance also results in the deployment of reactive maintenance, which is very costly.

In order to reduce asset maintenance costs without compromising on their output levels, oil and gas companies need to deploy more effective strategies. These strategies should include methods of optimized maintenance based on acceptable levels of risk. This can only be done with the perfect combination of predictive and preventative maintenance.

1.     Reactive Maintenance

Reactive maintenance is the most basic approach to maintenance in the oil and gas industry. The method involves letting the downstream assets run until they start to completely fail, and this type of maintenance is only suitable for operations that involve non-critical assets. These assets are those which have little or no impact on the safety and productivity of the organization. They also tend to have minimal replacement or repair costs. All in all, these non-critical assets do not need major investments in advanced technology.

2.     Preventative Maintenance

This kind of maintenance is implemented in the hopes that none of the assets being utilized by the organization will reach points of failure. The strategy of preventative maintenance works wonders if it is implemented in two cases. Firstly, preventative maintenance can be employed according to the industry’s best practices or according to the equipment manufacturer’s recommendations. Secondly, this method can be implemented when it comes to conducting inspections and repairs on the equipment.

3.     Condition Based Maintenance

This proactive approach focuses on the physical conditions of the equipment being used by the oil and gas companies and how they are operating. Condition-based maintenance is only a good practice when there are measurable parameters that indicate impending problems that might occur in the production process. This means that this kind of maintenance follows rule-based logic which defines some criteria of the well-being of the equipment in terms of ambient, loading, or operational conditions.

Stepping Up To Predictive Maintenance with the Help of Data Analytics

We have seen some promising advancements in technology in recent years. These include cloud platforms, computing powers, and analytics. All of these technologies can effectively focus on problems of reliability, which is why they can be seen adopted by everyone in the industry.

Predictive analytics has the power to improve the reliabilities of assets in the whole oil and gas industry. They do so by enabling empirical multi-variation modeling of all of the rotating equipment used in the industry. These include turbines, compressors, and pumps. Their performance is tracked with the help of machine learning algorithms and advanced pattern recognition so as to identify and diagnose as potential issues in operations. These are tracked days or even weeks before they can result in possible failure.

Operating models that include past loading in operational and ambient conditions are all used in order to create unique kinds of asset signatures for all of the equipment. Once this is done, operating data in real-time is used in order to compare performance against these operating models. In these comparisons, even the most subtle difference from expectations is identified. This is why predictive maintenance with the help of data analytics can allow the most effective and reliable monitoring of many different kinds of equipment.

Predictive maintenance has the ability to provide companies with early warning notifications, which are enough to alert the maintenance teams to perform assessments. This is when these maintenance teams identify the problems and resolve them. As a result, these companies tend to side-step all kinds of major breakdowns, which could cost them millions of dollars. By adopting this form of maintenance, companies have now completely avoided the stoppages and slowdowns in their downstream production.

How Do Predictive Asset Analytics Software Solution Work?

Predictive asset analytics software works in a manner where they learn the unique operating profiles of assets during ambient, loading, and operational times. This is all done with the help of some advanced modeling processes.

The result of these modeling processes is a signature of every unique asset, which can be compared to the real-time performance of these assets. When these comparisons are made using operating data, the software has the ability to determine performance and alert any kind of deviation as soon as they are detected. Even the most subtle detection is alerted to the maintenance team before these could grow to be significantly impactful for the operations of oil and gas companies.

An example of a large company that has employed predictive analytics in their production includes Shell Oil Company. While they are using Big data and other technologies in many aspects of their business, one of the tools used by the company is predictive analytics to determine when parts of equipment need repairs or replacements.

These types of software have the ability to identify any potential problem in operations in days, weeks or even months before they have occurred – hence providing the organization with early notifications of issues before they become consequential. When this is added to the downstream operations of oil and gas companies, their operations personnel will be seen to be quite proactive. As a result, they will reduce all kinds of unscheduled downtimes.

Employing predictive asset analytics requires strategic planning in these operations. Long-term planning will allow companies to preorder and ship any equipment deemed replaceable in the future. There will, therefore, be no need for the organization to rush while their assets continue to run.

Other benefits of this software include increased levels of asset utilization alongside the ability to identify all the underperforming assets in the organization. Not only does all of this lead to the overall profitability of these organizations but also lengthened windows for maintenance needed, extended equipment lives, and increased availability of assets.

Similarly, if we consider the costs of traditional kinds of maintenance, then organizations can save themselves for costs that ‘could have been’. These costs include additional man-hours, lost productivity, costs of replacement equipment, and of course, machine failures.

Another very important benefit of predictive asset analytics software is that it has the ability to capture and transfer knowledge. The predictive solutions, therefore, ensure that decisions of maintenance teams can be repeated when needed. This can also be done when these organizations are faced with changes like transitioning workforces or even the loss of the most experienced worker who had all the critical knowledge. In other words, the maintenance and operational procedures of these organizations become automated.

The Benefits of Predictive Analytics

  1. Extended life of the equipment and increased utilization of assets
  2. Reduced costs of maintenance and operations
  3. Reduced costs of unplanned downtimes alongside improving the availability of assets
  4. Improved return on investments from assets via the early warning, which prevents asset failures way before they happen.

Smart Operations

Other then predictive software, oil and gas companies can also be seen transitioning into the industrial internet of things. These technologies have the ability to create huge business value because they can enable the integration of smart equipment, which has the ability to store large amounts of data.

Oil and gas companies are usually faced with opportunities and challenges to leverage all relevant data that has the ability to mitigate potential risks whilst improving productivity. With the help of predictive analytics from the internet of things, these companies can comprehend and ascertain the expected and actual performance of downstream equipment. By doing so, they can collect information regarding:

  1. Prioritizing operational and capital expenditures
  2. Asserting the potential consequences and risks that are associated with each asset that is being monitored
  3. Measuring the impacts of all deficiencies in performance in terms of future and current operations.

Maintenance Management Transformation Isn’t Only About Technologies

Transforming a company’s strategy towards maintenance management requires a shift from a reactive to a proactive culture. This, in turn, can only be done if companies embrace change management while creating new models in business that fit all the required capabilities.

All of this can then allow the journey toward digital transformation in the organization via automation of workflows, work orders, and analytics. This also requires some behavioral changes in the workforce because these technologies change when, where, and how all the downstream work evolves and is performed in the organization.

Even though this seems like a very daunting task, making this transition can be very rewarding for an oil and gas organization. This is because even the slightest improvements in asset utilization have the potential to result in large sums of revenue and profits.

The world of technology is changing, and more and more businesses are moving toward automation. Unless oil and gas companies are able to leverage these technologies to increase their operational efficiencies and reduce bottlenecks, they will be at the losing end of the spectrum.

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Oil and gas operations are commonly found in remote locations far from company headquarters. Now, it's possible to monitor pump operations, collate and analyze seismic data, and track employees around the world from almost anywhere. Whether employees are in the office or in the field, the internet and related applications enable a greater multidirectional flow of information – and control – than ever before.

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