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Improve Asset Health and Productivity by Leveraging Predictive Analytics

Current Situation – Standalone Applications Usage

Typically operators working on these machines take care of day-to-day problems based on signals, alerts and data generated by SCADA systems. Maintenance planners and schedulers are often not aware of these day-to-day problems, as these are rectified by operators on a reactive and preventive basis. Generally, root cause analysis is not done for these alerts by the operators. Moreover, majority of these companies depend highly on external maintenance labor due to specific skills and certification requirements, to take care of peak workload.

Those day-to-day problems along with the corrective action taken by the operators are not captured in any system accurately and also do not reach the planners and schedulers on real time basis. Moreover, paper usage is still very high in this industry because of lack of connectivity at offshore platforms, hostile working conditions etc. Data is collected on paper and later fed into asset management applications resulting into data entry errors. Hence, quality data is not available, resulting in inefficient or delayed decision-making.

If those day-to-day issues along with historical and real time information from SCADA systems are captured and analyzed using various statistical and analytics solution, future failures can be predicted. These predictions can be used by planners and schedulers proactively, to prevent future asset failures, resulting in reduction of non-productive time and unexpected maintenance cost.

In order to predict asset failures in advance, companies need smarter technology enabled solutions. Though the sensors, SCADA, Historian etc. have been in existence for a long time and oil companies are using them but the benefits from these systems are still not visible due to following reasons:

  1. Alerts and findings from SCADA / Historian systems are not fed into Asset Management system on a real time basis.
  2. Previous asset failure data is not available in asset management systems, due to which companies cannot analyze the past pattern.
  3. Companies use Asset Management packages as a stand-alone system and those systems are being mainly used for entering data, post operations, thus not capturing the actual data (readings etc.).
  4. Companies lacks resources and knowledge on how information from SCADA can be used for prediction.
  5. Most of the businesses do not have the expertise to develop and apply statistical models for prediction.

Conclusion - An integrated Solution based on Statistical Tools

To predict potential asset failures, these companies need a predictive Analytics solution which can extract data (real time and historical) from systems like Asset Management, real time sensors, SCADA etc. and use it to predict potential asset failures by applying various statistical tools and optimization techniques. This solution can support oil and gas companies in addressing the challenge of asset performance and integrity along with eliminating HSE risks, thus enabling them to move from reactive to predictive maintenance.

Further as companies are already having all the required critical systems like SCADA, Historian and Asset Management software, there is not much capital investment requirement to develop such solution, making business justifications much easier.

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