The oil and gas industry already live on the edge when it comes to the remote and often inhospitable geographic locations that it operates in, but now it is moving its computing to the edge to gain valuable business insights that can increase operational efficiency and profitability.
For any industry, downtime is an anathema, a situation that all process companies strive to avoid. It can be costly and disruptive in any industry, but for oil and gas companies, it can be particularly expensive. According to an MIT Sloan study, a single day of downtime for a liquefied natural gas (LNG) facility can cost $25 million. And a typical midsize LNG facility goes down about five times a year.
It is well recorded that oil facilities, both upstream and downstream, generate vast volumes of data. A report from Cisco estimates that a typical oil platform generates up to 2TB of data every day. This creates enormous challenges when it comes to communications, storage, and analysis. One solution would be to collect less data, but with the growth of sensors, there is no sign of any reduction in the flow of data, quite the opposite.
“While technologies such as cloud computing and hybrid storage have been touted as solutions, these still rely on data being transmitted, and with many offshore facilities working on satellite communications at a speed of around 2Mbps that is still not practical,” Jane Ren, CEO, and founder of Atomiton explains. “The obvious solution would be to deal with that data on site as close as possible to where it is generated. Not just handled but analyzed and used to deliver actionable business information. That is why edge computing is rapidly becoming a crucial tool in the industrial internet of things (IIoT) toolbox.”
At this year’s World Mobile Congress, Jonathan Carpenter, Petrofac’s head of strategy, spoke about the struggle for uptime that his company had in its North Sea operations. He quoted average uptime in the North Sea as 73%, and he compared this to the aviation sector that had an uptime of 99.9%. He asserted that this low figure had traditionally been acceptable to the oil and gas industry because of the high oil prices. His answer was what he called Petroltytics, that utilized predictive analytics from data collected and processed by edge devices.
As Ren explains, edge computing is not a new phenomenon, but the maturation of several key technologies has made it much more viable in the current climate. “Firstly, there is the diminishing cost of computing power and sensors that reduce the cost of most IIOT applications,” she says. “Then there is the increasing amount of data both from within the process and externally, such as weather or commodity/energy pricing. Then there is the smaller footprint of computing devices such as microcontroller units (MCUs) and single board computers or System on a Chip (SoCs).
“But possibly the most significant driver is the growth of advanced machine learning and analytics capabilities that makes computing on the edge a very valuable process. MCUs are very low-cost tiny computational devices. They are often found in the heart of IoT edge devices. With 15 billion MCUs shipped a year, these chips are everywhere. Their low-energy consumption means they can run for months on coin-cell batteries and require no heatsinks. Their simplicity helps to reduce the overall cost of the system.”
What is edge computing?
As of today, there is no standard definition for what is the edge, different organizations or sources have varying descriptions. For example, Wikipedia describes it as pushing the frontiers of computing applications, data, and services away from centralized nodes to the logical extremes of a network. It enables analytics and data gathering to occur at the source of the data. This approach requires leveraging resources such as laptops and smartphones that may not be continuously connected to a network.
“We like to say that edge is computing happening as close as possible to the sources of data—sensors, SCADA, and other operational systems,” Ren says. “In industrial operations, this new form of distributed computing brings the intelligence close to the field, where industrial machines operate, and people work. It offers the promise of getting the right device data in real-time to drive better decisions and even control industrial processes for improved efficiency.”
There are four primary reasons why computing at the edge is needed in industrial operations—privacy, bandwidth, latency, and reliability. An edge solution achieves privacy by avoiding the need to send all raw data to be stored and processed on cloud servers. Bandwidth and the associated costs are reduced as all raw data is not sent to the cloud. There is no issue of latency when computing occurs at the edge and does not rely on a cloud connection. Finally, reliability is improved because it is possible to operate even when the cloud connection is interrupted.
Edge computing for oil and gas
Although edge computing can be an effective strategy for many industries, there are many facets of the oil and gas sector that make the business case even more compelling. “While edge computing moves the IT and analysis to the edge, the oil and gas sector is already working on the edge in the physical world,” Ren explains. “You just need to look at the operating environment to realize that. It has 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 of information, and reactive operations. Edge changes the paradigm.”
Edge computing is heralding a revolution in the way that the oil and gas industry operates with a triumvirate of transformations for information, the workforce, and commercial operations. The 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. The workforce transformation means digital technologies will increasingly occupy the front lines for oil and gas workers, challenging the workforce to step up their digital skills. The commercial transformation means oil and gas product transactions and supply chain decisions will have to be more flexible with the ability to act on the availability of more granular information that streams directly from the infrastructure and products themselves.
“As we have seen, the oil and gas industry generates a massive amount of data, and with the proliferation of sensors, this is only going to increase,” Ren continues. “The challenge for the industry is how they can extract actionable business insights from this data that can enable their 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 different, 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—such as mobile or fixed equipment health issues, pipeline vandalism, leaking gas, environmental implications, and dynamic truck schedules impacting loading at terminals to optimize them. In essence, edge computing, in combination with IIoT applications, enables operations to run more smoothly and efficiently, in a predictive, rather than reactive manner.”
Edge computing and the distributed nature of industrial operations complement cloud computing. An edge to cloud architecture enables enterprises to take advantage of the operational intelligence needed at the industrial edge, while also allowing 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.
Delivering results from the edge
There are numerous and varied applications for IIoT edge computing within the oil and gas sector. “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 platforms or digital yards,” Ren says.
Another stumbling block in digitalization that the oil and gas industry faces is a large amount of legacy equipment that is out in the field and still performing. Edge computing offers the possibility of connecting this existing legacy equipment such as analog meters or gauges, as well as standalone processes so they can be digitalized and integrated for a more robust network that can deliver real-time information allowing intelligent decisions to be made in the field.
“When it comes to monitoring, the availability of real-time tracking information facilitated by edge computing helps operators make better business decisions on moving assets,” Ren adds. “Tracking the flow of crude and natural gas within these complex supply chains and their interconnections, from wellheads through the pipelines and on to processing facilities and distribution fleets, the access to real-time information allows for better outcomes.
“There are also safety considerations when it comes to knowing in real-time the performance of your assets. Edge computing can bring additional prediction, detection, and monitoring through edge computing in complex or rapidly changing situations that can improve worker health and safety, safeguard critical infrastructure, and minimize environmental impact.”
Edge computing transforms the oil and gas industry by delivering predictive operations, and its use will grow over the coming years. It does not come without challenges, such as lack of skills and a necessary change in company culture, but the technology has been developed, proven, and is already reaping the rewards for the early adopters.
