Edge Analytics as Key Enabler for the Success of Oil & Gas 4.0
Authors: Sharma Pankajkumar 
Published: November 2020

Objectives/Scope

Corporations & plant operators are focused on improving profitability by every possible $ (Dollar). With no control over the feed stock and finished material pricing, which are mostly governed by regulations, operators must look with in their Operation & Maintenance (O&M) for possible areas of optimization. In modern days, CIOs are greatly empowered to drive Digital Transformation strategy and contribute in improved profitability. Many of such initiatives fail to deliver the desired outcomes in timely manner. This paper discusses the essential elements of EDGE analytics embedding the fundamental aspects of plant O&M in such Digital Transformation initiatives, and thus enabling faster value realization of the investment.

Methods, Procedures, Process

Every industry can be looked in to three different tiers as Assets, Process & systems and Business. Each of them has different outcomes to contribute in overall profitability. Moreover, technology, methodology, people and their collaborative adoption play a big role in achieving those outcomes.

Asset performance is evaluated using KPIs like availability, reliability, cost & Safety. Process involves interaction of people & process fluids with those assets. However, the fundaments remain same as the source of information, based on which the O&M decisions are taken.

Transducers & measurements around plant assets are the single biggest source of information for such decision making. Often, this information is not best utilized in terms of generating proactive insights. Information from these measurements can be converted into simple analytics. E.g. Proximity type vibration transducer measures vibration amplitude which is used for equipment safe operation and shutdown when exceed the defined threshold. Same transducer also provides the gap voltage and spectral data. Analytics definition using proximity probe gap voltage will identify rotor position within the bearing and then comparing it across the machine train along with orbit preload can give significant information about rotor dynamics. Such analytics available at EDGE are often overlooked in enterprise level predictive analytics deployed by Artificial Intelligence/ Machine Learning. One of the reasons for it is cyber security concern for the data exchange across IT-OT network layers.

This paper talks about implementing comprehensive strategy utilizing the information from EDGE devices for EDGE analytics, various challenges and possible way forward in complying to cyber security requirements for data exchange from OT-IT systems and thus enhancing value of big data in Data Lakes.

Results, Observations, Conclusions

EDGE analytics adds immense value for assets in achieving best of performance KPIs. With increased adoption of cyber security practices in plant operations, there are many IT approved architectures exist today to share these analytics insight to L4 - business network. With focus on industry domain experience around systems of systems and progressive thinking, EDGE analytics will play inevitable role in the overall success of O&G 4.0 in coming years.

Novel/Additive Information

"Data is the new oil". Corporates in energy sectors are driving forth industrial revolution with this belief and investing heavily in big data-based projects. Asset condition monitoring systems at EDGE in OT domain remains in silos. High resolution static & dynamic data in various operating modes collected in these Condition monitoring systems hasn't been historically much interfaced with such big data projects. Use cases of such interfaces with static & dynamic data, benefits, related workflow, data exchange architecture is relatively newer in the industry. Comprehensive discussion of these aspects in this paper will enable industries to realize faster outcomes.