Oil and gas organizations need to be more efficient than ever. In one of the world’s most competitive industries, companies are increasingly facing stricter regulations, working to safeguard themselves against price fluctuations while simultaneously fostering growth. It is quickly becoming a game of disrupt or be disrupted. To survive, organizations need to be able to leverage technology and drive innovative solutions to address problems as they arise and have a complete understanding of their operational environments.
On any given oil rig, there are approximately 30,000 sensors, each monitoring a different piece of equipment and ensuring consistent operation. This generates an enormous amount of data, and properly utilizing that data can be the difference between success and failure. While the cloud can be a great resource for storing and sending data, it takes time to upload and send those data points via satellite and that can curb the ability to make decisions in real time. To make sense of all the noise generated by so many data points, companies are turning to edge computing solutions whereby data that is collected on site is processed on site. Enabling edge computing can solve the problem of time lag in data uploading. Processing data on site means that rather than having to prioritize what data is sent to the cloud and commonly sending data points that are irrelevant to business operations as a whole, companies can deploy applications and machine learning to process on site, eliminating the lag created by sending data off site.
Oil and gas companies will only continue to ramp up the need for monitoring and automation as time advances, generating more and more massive amounts of data with each upgrade. The industry is unique in its need to be mobile and remote regardless of sector — upstream, midstream or downstream. Sensors sending data can be found on tankers crossing the ocean, on remote rigs or on mobile fracking fleets. Wherever there is oil, the ability to monitor and process data on the go will be necessary. To better understand their operations and innovate new solutions, oil and gas companies need to become more agile in their approach to handling data. This does not replace the cloud — rather it enhances it. By gaining the ability to process data on site through machine learning, companies can send only the business-relevant data to the cloud, speeding up the time it takes to upload, while still providing data scientists with the information they need to make informed decisions.
So, how can companies leverage all this data to better improve their operations? First, through automation. Automation requires that machines can make real-time decisions based on data from other points of the operation. If a leak is detected, or another piece of equipment stops working, that needs to be instantly communicated across the system so that it can adapt and prevent a costly or dangerous incident. Increased automation can improve the efficiency of a rig, well site and asset, as well as cutting costs and increasing safety by reducing the headcount of employees in the field.
And it is not just the safety of the employees that improves. While oil and gas companies want efficient, safe operations, they are also stewards for the environment. Organizations are facing increasing pressure to not only keep up with stricter regulations, but to go beyond the bare minimum to ensure their name doesn’t end up in the news for the wrong reasons. Maintaining a strong reputation as a responsible and safe operator is becoming just as important to businesses as their ability to produce. By processing data on site, operators are able to see in real time if there are any issues that could cause leaks, spills or other catastrophic events around them.
Finally, all of this monitoring, when processed correctly, contextualizes data points and brings out useful information for increasing efficiency. Greater automation means less downtime and more innovative solutions for avoiding or repurposing waste. Processing data on site and sending only business-relevant data to the cloud empowers data scientists to find new options for how to best utilize resources and get the most from their equipment.
The future of oil and gas operations is fully automated rigs, shipping and fracking fleets. This level of automation requires unprecedented amounts of data being generated each minute of each day, and real-time monitoring to ensure that things do not go wrong on the minute level like the operation of a single valve or actuator. Oil companies need to find ways to continue to link legacy equipment into more modern systems, and while the cost of setting up an edge computing system can be prohibitive for legacy equipment, its potential to save money and time more than makes up for the cost. Companies that are able to invest in innovative technologies and increase their learning and understanding of their own operational environments will be best situated to survive price fluctuations and keep up with increasing regulatory environments.
Edge computing has already been implemented in many aspects of the oil and gas industry. Its proliferation will allow companies to continue to find innovative solutions to problems as they arise and to implement ideas that have not been possible in the past to further disrupt the space.
John Bledsoe focuses on the energy industry to help Hivecell clients produce more product at lower costs, operate safely and be good stewards of our environment. The convergence of IT and OT to solve previously unsolvable problems helps organizations mitigate risk, increase revenues and further insulate against commodity price volatility. John has partnered with numerous energy companies in his career and has delivered business value for his customers such as implementing a management of change program for downstream operations to increase safety and compliance, reducing well completion time, greenhouse gas emissions reporting and compliance, reducing field headcount and decreasing downtime with exceptions based operations and maintenance programs, and optimizing supply chain to increase customer satisfaction while reducing costs and decreasing customer churn.
