The oil and gas industry is huge and highly diversified in
terms of its performance, encompassing the upstream, mid-tier and downstream
sectors. Even within each sector there are distinct differences; Offshore oil
and gas rigs present a completely different set of requirements for surface
rigs in the fracturing industry. However, all industries are subject to the
boom and bust cycles that traditionally characterize the oil and gas industry.
All this makes the oil and gas industry ideal for deploying IoT technologies to
address a wide range of problems and risks, as well as to smooth the ups and
downs of the business cycle.
Where are the oil and gas companies today with the
transition to edge computing?
Stratus recently attended the Oil and Gas IoT conference in
Houston, Texas and provided an interesting insight into where oil and gas are
in the current hype around the IoT in general. If there is a general view, then
implementing the IoT and analytics is a journey, not a project. This applies to
technology, but, above all, people and processes. This is excellently illustrated
by Marathon Oil, describing its three-year journey towards digital oilfield
automation.
The role of the cloud and the border
Moving data from source to cloud has been a subject of great
interest. Everyone agreed that the cloud is a place for deep analytics,
especially where machine learning and artificial intelligence technologies can
best be deployed. However, moving data from the edge to the cloud has its own
challenges. Nearly 75% of end-user submissions indicated that they are
deploying, testing, or evaluating the use of advanced computing to optimize
their cloud analytics. They turned to Edge Computing to help with oil and gas
challenges such as collecting data from a single location (such as an offshore
platform, well platform, compressor station, or processing plant) to limit the
number of connections to a single cloud. This is especially important in the
oil and gas industries, where there are many remote locations.
LEARN MORE ABOUT CLOUD AND EDGE HERE
Leveraging advanced computing for real-time analytics, where
latency and two-way latency would make a cloud-based approach impossible, was
also seen as an important application. They also discussed using modern
computing to filter and normalize data before sending it to the cloud. This can
significantly reduce bandwidth usage and greatly reduce the cost of cloud
computing.
The panel discussion I attended was not so much about
advances in the cloud, but more about the relationship between the cloud and
the periphery. We also explored the idea of taking machine learning
algorithms to the limit. "Learning" will take place in the cloud, and
the algorithmic implementation will be local for scalability and constant
updates as new and improved algorithms are learned.
All agreed that advanced computing will play a key role in
driving IoT deployment in the oil and gas industry. As data becomes more
important for making business decisions, its value will grow exponentially.
Ultimately, it will be critical to be able to collect, store and process data locally
with simple, secure and self-contained devices.
Edge roadmap
In short, it is clear that we are in the early stages of an
IoT rollout, and five years later, at least in terms of conferencing, are still
in a steep learning curve. In the recent Stratus Edge Computing Trends report,
the main obstacle to Edge adoption was the lack of information on when and how
to use advanced technologies and applications. So if you're in the early stages
and just starting to think about traveling the Internet of Things and whether
advanced computing can help, you are not alone.
In addition to reporting on edge computing trends, Stratus
has resources to help you know where you are, where you need to go, and how to
get there. We have a quick self-assessment to tell you where you are now and a
maturity model to help you think about the various aspects you need to consider
and what you need to be successful.
