How Real-Time Analytics on Streaming Data Can Transform the Oil Industry
› White Paper
Managing Data: The Old Approach ... 1
Three Technologies That Are Transforming the Oil Industry ... 1
The Internet of Things ...1
Event Stream Processing ...3
Prescriptive Analytics ...3
Combining People, Processes and Technology for a Winning Approach ... 4
Why SAS
®? ... 5
Learn More ... 5
1
Today’s “fast” is defined by a different measure of speed than in the past. Responding quickly and confidently in the business world no longer happens if you accept the same type and pace of data collection and analytics as before.
With today’s technologies, oil companies can change not just their speed and precision, but also the location where they inject
“rapid” and “responsive” analytics into their processes. And that incredible difference changes the whole nature of the game.
Managing Data: The Old Approach
Traditionally, oil companies have collected masses of data from their equipment, their fields and other operations. They store all of that data, run processes on it, get results and analyze them.
Then they start all over again with new data.
Consider how this works for an oil rig. Despite being connected to the rig, remote operators aren’t usually incorporated into operational decision-making processes about the rig itself. They see rig data on the back end of the operation – after it’s stored – which tends to result in deterministic analytics and passive surveillance.
What you want from your remote operations is an active form of operational engagement that routinely uses your enterprise knowledge as part of the rig site decision-making process. The challenge lies in having an operational process that advises without intruding on the rig site decision maker’s activities.
Three Technologies That Are Transforming the Oil Industry
With three key technologies, you can radically overhaul your capabilities as an oil company. Combining these capabilities can lift the performance of your assets in real time:
• The Internet of Things.
• Event stream processing.
• Prescriptive analytics.
The Internet of Things
There’s a growing network of objects – cars, homes, appliances and equipment, even oil rigs – that can share information and accomplish tasks without human intervention. These intelligent devices are part of the Internet of Things, and they are the next wave in the big data explosion.
Figure 1: Remote operators today usually only see rig data after it has been stored, which results in deterministic analytics and passive surveillance.
Real-Time Control Reactive Surveillance Batch Review
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But the oil and gas industry has had connected rigs for almost 15 years. So what’s the big deal? Well, instead of receiving a stream of values without context, now you can embed self- awareness into the sensor or device. This enriches the
information stream to include the context of what the operation and the individual components and subsystems are trying to achieve.
Contextual data enrichment
Let consider what we mean by contextual data enrichment, and how can we apply it to improve our operational objectives. One of the key elements of using data to optimize a process is the
validation element. For the validation element to have an impact, it requires contextual knowledge in addition to absolute state. For example, when optimizing a drilling process, the context of the data needs to be included so that the output of the analysis will be accurate and appropriate for the task at hand.
But context is not solely discovered by structuring text. Context may also require analysis of related data sources and historical patterns to determine behaviors, procedures, situation and intent. Only after the context has been added to the stream of information can you validate models against your business’
operational needs and requirements.
Figure 2. Contextual data enrichment goes beyond text structures to include behaviors, procedures, situation and intent.
Drilling Activities
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& Sensors
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Pump Ramp Up Tool Face Orientation
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Event Stream Processing
Through an adaptation of complex-event processing called event stream processing, you can collect and analyze huge amounts of data simultaneously and in real time. This capability enables you to place analytic models inside your systems. Your information is used within the context of a machine and a deterministic analytic model to provide you with both predictive (What will happen?) and prescriptive (How I can reach my objective?) control and advice – either directly or indirectly.
Prescriptive Analytics
Prescriptive analytics is a form of advanced, predictive analytics that helps you discover more than just what has already happened, why it happened or what may happen next given current inputs to the system. It can prescribe the actions you should take based on the objectives you’re trying to achieve.
According to the International Institute for Analytics, prescriptive analytics is part of the wave of Analytics 3.0.1 Businesses can compete in this “data economy,” IIA says, if they:
• Seamlessly blend traditional analytics and big data.
• Treat analytics as a strategic asset that’s integral to running the business.
• Deliver insights rapidly and with agility.
• Make analytical tools available at the point of decision.
• Embed analytics into decision-making and operational processes.
UPS is often cited as an example of a company that puts prescriptive analytics in action. Their drivers receive instructions on package delivery that optimize fuel usage and reduce driving times – and their instructions are updated throughout the day to account for changing traffic and weather conditions.
What if prescriptive analytics were applied in real time to managed pressure drilling? A contextually enriched data set could describe current fluid and hydraulic properties through in-stream analytics processing on thousands of events per second. The guided information in the drilling system would adapt to conditions as they occur, highlighting and recording recommended actions to keep the well under control, and simultaneously quantifying the likely outcomes of future events.
This insight – which merges traditional analytics with real-time big data analytics – is a mechanism for optimizing resources and enhancing safety and efficiency.
1sas.com/content/dam/SAS/en_us/doc/event/The-Era-of-Impact-127837.pdf
Managing Information:
A Better Way
The key to improving the efficiency of remote operations lies in enabling contextual and situational analytics within a high-performance
environment. With this approach, you can analyze data while it’s in-stream, then interpret what actions the operator should take within the objectives of the overall program.
Event stream processing in an ultra- high-speed, ultra-low-latency
environment creates a technological inflection point for remote operations.
Why? Because it flips the paradigm from deterministic, after-the-event operational surveillance to an embedded advisory role that’s predictive, prescriptive and nonintrusive. In turn, remote
operations are now actively engaged
in the decision-making process.
Combine People, Processes and Technology for a
Winning Approach
Despite all our technological advances, people are still core to business success. Using technology and re-imagining processes requires strong leadership, a community of advocates and, in some cases, a center of excellence in analytics. It’s ideal to have an executive sponsor who can remove barriers, recognize that you don’t have all the knowledge you need in-house, and make it possible for you to go outside of your organization to acquire special expertise.
Ideally, you need an innovative believer to support your efforts – one who can relate to your executive and to your subject matter experts. You may need to bring in data scientists who are good at predictive analysis and process modeling. You may need information experts to comment on cybersecurity. This collection of data-minded people are well equipped to assess and act on statistical information models.
With this team in place, you can generate a culture that says,
“We can, we will and we must do much better.” Innovation becomes an expectation, along with win-win thinking. You realize you don’t have all the answers, but you know that you can recruit vendors, suppliers and other people to help – indeed, you welcome knowledge and external perspectives that are critical to your success.
By analyzing streaming, event-driven data, you can make timely improvements, mitigate risks and optimize assets. You can become more efficient and better informed. Change the way you think and how you respond to needs. Identify opportunities for improvement sooner. Act faster, and be smarter about how you operate. The results could be incredible.
Figure 3: SAS® Event Stream Processing moves analytics closer to the data, providing a proactive advisory capacity that helps you make decisions faster.
Real-Time Control Proactive Advisory Batch Review
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Why SAS ® ?
SAS can significantly speed up your ability to get insights from massive amounts of data. Our event-processing engine can analyze millions of records per second with a low level of latency at the microsecond and millisecond level. All of this data is cleansed and funneled through a model – calculated as it arrives. Plus, SAS integrates with existing solutions and complies with industry standards – including WITSML, OPC and ETP – so you can use data from any source. And it’s engineered with solutions like SAP, Hadoop and OSIsoft so that it’s easy to use and can deliver outstanding results.
With SAS, you can do descriptive, predictive and prescriptive analytics on a continuous data stream that’s happening right now – no waiting for the data to be refreshed, no searching for a place to store it all. You can take action as events occur, which shifts responsiveness into high gear, generates new
opportunities for the business and potentially reduces risk and cost at the same time.
Learn More
Data management and advanced analytics from SAS provide capabilities that span upstream, midstream and downstream segments. These capabilities convert data into assets that exploit conventional and unconventional resources, reduce nonproductive time, optimize return on asset investment, as well as forecast and manage the impact of supply and demand trends on your business. For more information, visit sas.com/oilgas.
Get more details about how SAS helps you acquire, understand and use real-time, streaming data to make automated, fact- based decisions and respond to new information
instantaneously: bit.ly/1GDrW0F.
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