Seeking to understand what edge computing is all about and what impact can it have on oil and gas industry, Globuc has asked Ann Sun – Vice President, Market Development & Marketing at Atomiton several questions.
Atomiton is a next generation software company for the IoT age, using unique technology to enable smart systems. Engineers at Atomiton work to empower application developers to “Transcend Device Logic” – write value-driven IoT applications with clean business logic that can scale. The company has been marked as one of the most promising IOT Solution Providers by CIO Review in 2017 and 2018.
What is edge computing?
Edge computing is computing happening as close as possible to the sources of data – sensors, SCADA and other operational systems. Edge computing has been enabled by low cost, small footprint computing devices such as microcontroller units (MCUs) and single board computers or System on a Chip (SoCs). In industrial operations, this new form of distributed computing brings the intelligence close to the field, where industrial machines operate and industrial engineers work. The industrial edge framework includes six essential aspects of how edge computing is comprised across 3 areas – physical (sensors, hardware), digital (compute, communications) and functional (operating system and applications).
What does edge computing mean for the oil & gas industry?
The oil & gas industry operates on the physical industrial edge – wellbores thousands of feet underground, millions of miles of pipelines, extreme temperatures in LNG, complex refining processes, and many configurations in terminals. The distributed environments have traditionally been a reason for information delays, silos, and reactive operations. Edge changes the paradigm.
Edge computing brings three transformations to the oil and gas industry: information transformation, workforce transformation, and commercial transformation.
- Information transformation means companies will compete on how fast they process data, not how fast they collect and store data. Real-time intelligence will reside on the edge.
- Workforce transformation means digital technologies will increasingly occupy the front lines of O&G workers, challenging the workforce to step up their digital skills.
- Commercial transformation means O&G product transactions and supply chain decisions will have to adapt to faster, and availability of more granular information streaming directly from the infrastructure and products themselves.
Across the industry, there is a tremendous amount of data available that can enable operations to run more efficiently. Leveraging artificial intelligence with edge computing in IIoT applications transforms this data, to deliver real-time operational intelligence directly to the people and places where needed. The integration of disparate, discrete data sources across sensors, devices or systems requires this data to be analyzed, filtered and contextualized into digital models or profiles of equipment or processes relevant to operations. These digital models continually learn and then predict operational impact – e.g. mobile or fixed equipment health issues, pipeline vandalism, leaking gas, environmental impact, dynamic truck schedules impacting loading at terminals in order to optimize them. In essence, edge computing in combination with IIoT applications enables operations to run more smoothly and efficiently.
How edge computing will coexist with cloud computing in oil & gas? Is edge the new cloud?
Edge computing and the distributed nature of industrial operations complements cloud computing. An edge to cloud architecture enables operations to take advantage of the operational intelligence needed at the industrial edge, while also enabling augmented big data analytics and broad visualization in the cloud. Building an edge to enterprise reference architecture requires collaboration between operations and IT to ensure requirements are considered all the way from the asset or field operations edge through the enterprise, including security.
What are some real-world use cases of edge computing in oil & gas?
Applications of IIoT edge computing are relevant in many areas. Unconnected environments such as production wells, pipelines, compression stations, underground gas storage facilities or under-connected environments such as offshore rigs, construction yards and vessels can be connected, facilitating local data processing and communications to enable smart rigs or digital yards. Existing legacy equipment – e.g. analog meters or gauges, or standalone processes can be digitized and integrated for more robust, real-time information so intelligent decisions can be made in the field. Real-time tracking of moving assets such as crude and natural gas from wellheads through pipelines to processing facilities to distribution fleets interconnects the complex supply chain for better commercial decisions. Bringing additional detection and monitoring through edge computing in complex or rapidly changing situations can improve worker health and safety, safeguard critical infrastructure, and minimize environmental impact.
What are the main difficulties in edge computing adoption in oil & gas?
One of the challenges with edge computing and IIoT projects is lack of in-house experience. Another challenge is in some companies, IT and OT operate very independently. Extending the network edge to operations means security and management must be considered and designed in. In other companies, the many opportunities for different applications or use cases becomes overwhelming.
As with any innovation or transformation initiative, deployment of new technologies and applications should start with the end goal for the business, before considering what technology to apply. The starting point is evaluating what you want to achieve – where can value be added? What operational issues could have tangible impact if solved – e.g. gain real-time visibility of operations, ensure on-time delivery of construction projects, reduce environmental impact, increase terminal efficiency. Pick one problem to solve, then create a small, nimble team comprised of members from operations/OT and IT that understands the problem in detail and can determine requirements and evaluate solutions that may address the problem. From there, assessing the applications that leverage IIoT and edge computing is straightforward. Run a pilot, keeping the importance of the need to scale in mind, and assess the results from the pilot to determine next steps.