SAG_Twitter_MEME_IoT_Beyond_880x440_May18IoT systems on top of SCADA technology will surely become a significant component in asset optimization for the oil and gas industries over time.

In our last post we discussed how SCADA technology has reached its limits and can’t be expected to take companies to the next level of operational excellence without help from a complimentary IoT presence.

SCADA systems can incorporate historians to collect and log data from various devices into a database that can subsequently be analyzed, but this “rear-view mirror” approach to analytics can only go so far. It can answer questions like, “What was the average valve cover temperature in device x yesterday?” It can’t answer questions like, “How likely is this valve to fail in the next eight hours?” much less facilitate (or even automate) the corrective action needed to avoid an unplanned shutdown or a serious accident.

This is where the IoT comes in to help your company achieve operational excellence, a well-defined approach to extending the life and optimizing the performance and reliability of assets.

The business value of longer asset life with fewer unexpected failures has been demonstrated again and again. In a typical refinery, for example, the cost of missed production due to unplanned shutdowns can range from $340,000 to $1.7 million per day, depending on profit margins at the time.

In the electric utility sector, Advanced Distribution Management Systems based on IoT technology have reduced outage durations by as much as 60 minutes by more accurately predicting incident locations and identifying nested incidents. In addition, according to Business Insider, companies will save $157 billion by 2035 through the use of IoT-based smart meters.

The Asset Optimization Maturity Model

Today, costly unplanned shutdowns are a fact of life. In order to avoid them, companies often resort to redundant systems or over-maintenance, replacing lubricants sooner than necessary or changing out bearings that still have life.

When companies can more accurately calculate the probability of an asset failure, they can safely postpone maintenance or replacement until there’s a real need backed up by data. In many cases, they can also eliminate unnecessary redundancies.

The Asset Optimization Maturity Model provides a clear path toward the increased operational excellence that can deliver benefits like these. It defines five levels of maturity:

  • M1: This is the run-to-failure level, or at best, run-to-near failure. It represents the ultimate in reactive asset management. Typically, operators have no information about an asset’s status until they’re notified by an alarm that says to replace it. While this run-to-failure approach is acceptable for some assets, it can result in serious consequences for others, including unplanned shutdowns.
  • M2: Real-time status monitoring. At this level, operators can monitor asset health in real time, and perhaps receive warnings when certain basic operational parameters are exceeded, such as temperature, pressure, voltage and the like. This is still a reactive approach, but at least it can provide advanced warning of a potential failure, hopefully in time to take remedial action.
  • M3: Rules-based predictive maintenance. At this level, operating norms are established through analysis of historical performance data. When deviations from these norms are detected, operators receive advanced notification of a need for maintenance based on static, pre-determined rules. This level also incorporates mobile workforce management.
  • M4: Predictive maintenance based on dynamic streaming. This level takes predicting the need for maintenance to a new level of accuracy because it relies on real-time data to prescribe maintenance that will ensure asset optimization and avoid the costs of both under- and over-maintenance. In addition, M4 incorporates documented standard operating procedure and compliance management.
  • M5: Production Optimization. The highest level of maturity improves the accuracy of its predictive analytics yet again through cross-correlation across asset types. The data set upon which predictions are based is enriched through external data. Drones can be incorporated into the data gathering mix if necessary. Workforce scheduling can be driven by the output of the system.

These five levels are often summarized as evolving from reactive to preventive to predictive maintenance. In our next post, we will discuss taking an evolutionary approach to operational excellence.

IoT systems on top of SCADA technology will surely become a significant component in asset optimization for the oil and gas industries over time.

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