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Asset Performance Management (APM) with Predictive and Prescriptive Analytics enables hydrocarbon producers and refiners to not only remotely monitor asset health but also improve asset reliability. By Dr Ravi Gopinath, Chief Product Officer for AVEVA

The current pandemic has struck the oil and gas industry reducing demand due to global lockdowns combined with oversupply that has impacted a 20 per cent imbalance, causing the plunge in oil and gas prices moving from +SG$83/barrel to -SG$51/barrel within weeks.

For safety, efficiency, and operations, business continuity is becoming the top focused imperative for most to sustain functional operations amid the chaos. 

That is why digital transformation is absolutely critical today more than ever. Keeping critical assets productive is even more challenging due to the changes in maintenance schedules and utilisation in this heightened uncertainty period. 

Only with the right digital tools and software capabilities will successful companies manage the de-manning of production facilities, while keeping their assets productive.  

Accelerate Your Operations Through Asset Performance Management (APM) 

Today’s technology is helping to drive measurable and immediate results to your workforce that are improving business performance.

Traditional Asset Performance Management focuses on reliability engineering methods and connects the asset to the person in the different stages of the asset lifecycle through several layers of enabling technologies. 

Today, many industries need new data to improve reliability, reduce downtime, and increase productivity. 

Powering With Predictive Alerts

APM 4.0 applies predictive analytics to the time-series data to create meaningful, accurate, and specific indicators and alerts that enable decision-makers to influence the asset’s performance before a failure happens. Predictive analytics includes the following three strategies to create alerts:

  1. Condition monitoring: Requiring basic implementation, condition monitoring involves taking one or more sensors, defining a meaningful indicator as an explicit function of the input, and setting a fixed threshold. Should the indicator breach this defined threshold, then it is considered indicative of a problem or failure, and an alert is triggered. The indicator is set and calculated in the historian. 
  2. Anomaly detection: This strategy requires Artificial Intelligence (AI), or more specifically, machine learning technology. The algorithm learns a set of time-series training data that reflects the ‘normal’ operation of the asset. The accuracy of the algorithm runs on a new collection of data to test if it can reliably pick up any anomalies. The significant advantage of this strategy is not having the need to define a mathematical function between the indicator and the sensors to monitor many different sensors simultaneously.
  3. Failure mode prediction: The ultimate goal in predictive and prescriptive analytics is the accurate and reliable prediction of specific failure modes. It enables precise preparation and a swift follow-up to solve the problem. The goal is achieved with monitoring fixed thresholds of sensors. This advanced method is an extension of the machine learning algorithm used to detect anomalies. 

Add-On Prescriptive Analytics

The right APM 4.0 solution enables you to make prompt decisions on the prioritisation and scheduling of alerts so that you can prepare tasks and ensure spare parts are available. By adding prescriptive analytics to the predicted signals, you maximize the benefits of APM 4.0 and successfully follow up with actions that will improve asset performance. Each triggered alert are linked to prescriptive actions that consist of four attributes:

  • Criticality: What is the financial impact of this predicted failure? The impact is the total effect of the event if the expected failure mode occurs, as defined in terms of financial loss.
  • Urgency: How much time do you have? Urgency is a reliable prediction of when the failure mode will occur once the indicator alarm is triggered. 
  • Action: What action will you take? This attribute defines your ability to prepare for and respond to a triggered alert. It describes the tasks you need to execute and includes the required skills, tools, permits, work instructions, and safety of asset-need isolation. 
  • Spare Part Management: Having the right spare part on hand determines whether or not a predicted failure can be avoided before it occurs. 

The End Of Unplanned Downtime?

APM with Predictive and Prescriptive Analytics enables hydrocarbon producers and refiners to not only remotely monitor asset health but also improve asset reliability by providing early warning notification and diagnosis of equipment issues days, weeks, or months before failure.

This helps reduce equipment downtime, increase availability, and improve performance while reducing operations and maintenance expenditures.

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