“Drill, baby, drill” makes for an easy mantra when it comes to energy exploration, but the oil and gas (O&G) industry moved past simply drilling long ago with the introduction of digital information processing. For example, integrated production modeling was introduced in the 1970s. With the recent turmoil in the energy industry, the stakes are even higher for O&G companies to work smarter and more efficiently. Forward-looking businesses are making the transition to true digital transformation, which requires the adoption of the Internet of Everything (IoE)—the networked connection of people, process, data, and things—throughout the entire O&G value chain. According to a recent Cisco study, of these four IoE elements, essential “data” is the component most in demand—and the element that needs the most improvement.
However, in many cases it’s not data that’s lacking; O&G firms are awash in data generated by sensors and machines spread throughout their far-flung operations. The struggle comes in capturing real-time operating data closest to the point it’s created, analyzing it in real-time and applying the results to improve functional and business capabilities. To capitalize on the wide range of data IoE generates, O&G firms must overcome three key challenges:
- Automating the collection of data
- Integrating data from multiple—and often far-flung—sources
- Analyzing data to effectively identify actionable insights
Automating data collection: By extending cloud computing and services to the edge of the network—a paradigm sometimes referred to as “fog computing”—companies can take advantage of real-time data analytics and emerging IoE applications that demand low and predictable latency. For example, a typical offshore oil platform generates between 1TB and 2TB of data each day. Most of this data is time-sensitive, pertaining to platform production and safety. Using a satellite connection—the most common communication link for offshore oil platforms—it would typically take 12 days to move one day’s worth of oil platform data to a central repository. Using fog computing capabilities, companies can now assess this data locally to determine whether it needs to be moved to the cloud/data center, or analyzed where it is, at the “edge” of the network.
Integrating data: Integrating data is especially challenging when the data is from diverse and distributed sources, such as embedded sensors, video cameras, and third-party data sources. Many organizations are now using data virtualization to make a heterogeneous set of data sources look like one logical database—regardless of format or location. And because the data is integrated live, there is no need to physically store all the data centrally. It is only when users request data from several different sources that it is integrated. This kind of “integration on demand” provides instant access to all the data users want, the way they want it. Traditionally, O&G firms have used manual processes to gather and integrate data across the value chain. Data virtualization can provide a single view of an oil well and all of its subcomponents, allowing for quicker action to improve efficiencies—and leading to increased profitability.
Analyzing data: Whether it is in the cloud or at the edge, IoE data must be analyzed to identify actionable insights that can be used to create better outcomes. These insights then need to be embedded into efforts such as process reengineering and broader business transformations. To make the most of their data, oil and gas organizations will need both tools to deal with the exploding size, speed, variety, and distribution of data, and employees whose knowledge intersects data science, design, and enterprise architecture.
To deliver true value, data insights must link to specific business processes and outcomes. Respondents to Cisco’s oil and gas survey agree on the potential of data analytics to drive critical business outcomes. They named “data analytics for faster/better decision-making,” “improved operational efficiencies,” and “increased productivity” as their top three drivers for investment in connected technologies such as IoE.
Digital transformation demands that O&G companies leverage IoE to become more hyper-aware, predictive, and agile—and data analytics and data management capabilities are essential components. They play a pivotal role in driving value for O&G—in the oilfield, pipeline, and refinery. The new industry mantra has become “Drill, data, drill.”
How do you see digital transformation changing your business? Do you agree with the survey results that place data analytics at the top of business requirements for better, faster decision-making? How are you gathering and analyzing critical business and performance data at the edge of your network today?