An explosion in information volumes and processing power is transforming the energy sector. Even the major players are dragging their feet to catch up.
The business of oil and gas profit-making takes place increasingly in the realm of bits and bytes. The information explosion is everywhere, be it in the geosciences, engineering and management or even on the financial and regulatory sides. The days of easy oil are running out; unconventional plays are becoming the norm. For producers that means operations are getting trickier, more expensive and data-intensive.
âCompanies are spending a lot of money on IT. Suncor alone spends about $500 million per year.â
Thirty years ago geoscientists could get their work done by scribbling on paper; today they are watching well data flow, in real time and by the petabyte, across their screens. Despite what many think, the challenge for them doesnât lie in storing the mountains of data. Thatâs the easy part. The challenge is more about building robust IT infrastructures that Âholistically integrate operations data and enable Âdifferent systems and sensors to talk to each other. With greater transparency over the data, operators can better analyze it and draw actionable insights that bring real competitive value.
Even the big guys arenât progressive in this area,â says Nicole Jardin, CEO of Emerald Associates, a Calgary-based firm that provides project management solutions from Oracle. âThey often make decisions without real big data analytics and collaborative tools. But people arenât always ready for the level of transparency thatâs now possible.â Asked why a company would not automatically buy into a solution that would massively help decision-makers, her answer is terse: âFirefighters want glory.â
The suggestion is, of course, that many big data management tools are so powerful that they can dramatically de-risk oil and gas projects. Many problems end up much more predictable and avoidable. As a result, people whose jobs depend on solving those problems and putting out fires see their livelihoods threatened by this IT trend. Resistance and suspicion, always a dark side of any corporate culture, rears its ugly face.
On the other hand, more progressive companies have already embraced the opportunities of big data. They donât need convincing and have long since moved from resistance to enthusiastic adoption. They have grown shrewder and savvier and base their IT investments very objectively according to cost-benefit metrics. The central question for vendors: âSo whatâs the ROI?â
There is big confusion about big data, and there are different views about where the oil and gas industry is lagging in terms of adopting cutting-edge tools. Scott Fawcett, director at Alberta Innovates â Technology Futures in Calgary and a former executive at global technology companies like Apptio, SAP SE and Cisco Systems, points out that this is not small potato stuff. âThere has been an explosion of data. How are you to deal with all the data coming in in terms of storage, processing, analytics? Companies are spending a lot of money on IT. Suncor alone spends about $500 million per year.â He then adds, âAnd thatâs even at a time when memory costs have plummeted.â
The big data story had its modest beginnings in the 1980s, with the introduction of the first systems that allowed the energy industry to put data in a digital format. Very suddenly, the traditional characteristics of oil and gas and other resource industries â often unfairly snubbed as a field of âhewers of word and carriers of waterâ â changed fundamentally. The shift was from an analog to a digital business template; operations went high-tech.
It was also the beginning of what The Atlantic writer Jonathan Rauch has called the ânew old economy.â With the advent of digitization, innovation accelerated and these innovations cross-fertilized each other in an ever-accelerating positive feedback loop. âMeasurement-while-drilling, directional drilling and 3-D seismic imaging not only developed simultaneously but also developed one another,â wrote Rauch. âHigher resolution seismic imaging increased the payoff for accurate drilling, and so companies scrambled to invest in high-tech downhole sensors; power sensors, in turn, increased yields and hence the payoff for expensive directional drilling; and faster, cheaper directional drilling increased the payoff for still higher resolution from 3-D seismic imaging.â
One of the biggest issues in those early days was storage, but when that problem was more or less solved, the industry turned to the next challenge of improving the processing and analysis of the enormous and complex data sets it collects daily. Traditional data applications such as Microsoft Excel were hopelessly inadequate for the task.
In fact, the more data and analytical capacities the industry got, the more it wanted. It wasnât long ago that E&P companies would evaluate an area and then drill a well. Today, companies still evaluate then drill, but the data collected in real time from the drilling is entered into the system to guide planning for the next well. Learnings are captured and their value compounded immediately. In the process, the volume of collected data mushrooms.
The label âbig dataâ creates confusion, just as does the term Big Oil. The âbigâ part of big data is widely misunderstood. It is, therefore, helpful to define big data with the three vâs of volume, velocity and variety. With regard to the first âv,â technology analysts International Data Corp. estimated that there were 2.7 zettabytes of data worldwide as of March 2012. A zettabyte equals 1.1 trillion gigabytes. The amount of data in the world doubles each year, and the data in the oil and gas industry, which makes up a non-trivial part of the data universe, keeps flooding in from every juncture along the exploration, production and processing value chain.
Velocity, the second âv,â refers to the speed by which the volume data is accumulating. This is caused by the fact that, in accordance with Mooreâs famous law, computational power keeps increasing exponentially, storage costs keep falling and communication and ubiquitous smart technology keep generating more and more information.
âIn the old days, people were driving around in trucks, measuring things. Now there are sensors that do that work.â
On the velocity side, Scott Fawcett says, âIn the old days people were driving around in trucks, measuring things. Now there are sensors doing that work.â Sensors are everywhere in operations now. Just in their downhole deployment, there are flowmeters and pressure, temperature, vibrations gauges as well as acoustic and electromagnetic sensors.
Big data analytics is the ability to asses and draw rich insights from data sets so decision-makers can better de-risk projects. There is a common big data focus of oil and gas companies on logistics and optimization, according to Dale Sperrazza, general manager Europe and sub-Saharan Africa at Halliburton Landmark. If this focus is too one-sided, companies may end up just optimizing a well drilled in a suboptimal location.
âSo while there is great value in big data and advanced analytics for oilfield operations and equipment, no matter if the sand truck shows up on time, drilling times are reduced and logistical delays are absolutely minimized, a poorly chosen well is a poorly performing well,â writes Luther Birdzell in the blog OAG Analytics.
Birdzell goes on to explain that the lack of predictive analytics results in about 25 per cent of the wells in large U.S. resource plays underperforming, at a cost of roughly $10 million per well. After all, if a company fails to have enough trucks to haul away production from a site before a storage facility fills up, then the facility shuts down. Simply put, when a facility is shut down, production is deferred, deferred production is deferred revenue, and deferred revenue can be the kiss of death for companies in fragile financial health.
The application of directional drilling and hydraulic multi-stage fracturing to hydrocarbon-rich source rocks has made the petroleum business vastly more complex, according to the Deloitte white paper The Challenge of Renaissance, and this complexity can only be managed by companies with a real mastery of big data and its analytical tools. The age of easy oil continues to fade out while the new data- and technology-driven age of âhard oilâ is taking center stage. The capital costs of unconventional oil and gas plays are now so high and the technical requirements so convoluted, the margins for error have grown very small. Decision-makers canât afford to make too many bad calls.
Despite the investments companies are putting into data-generating tools like sensors, much of the data is simply discarded, because the right infrastructure is missing. âIT infrastructure should not be confused with just storage; it is rather the capacity to warehouse and model data,â according to Nicole Jardin at Emerald Associates. If the right infrastructure is in place, the sensor-generated data could be deeply analyzed and opportunities Âidentified for production, safety or environmental improvements.
Today, operators are even introducing automated controls that register data anomalies and point to the possible imminent occurrence of dangerous events. Behind these automated controls are predictive models which monitor operational processes in real time. They are usually coupled with systems that not only alert companies to issues but also make recommendations to deal with them. Pipelines are obviously investing heavily in these systems, but automated controls are part of a much larger development now sweeping across all industries and broadly called âthe Internet of thingsâÂ or âthe industrial Internet.â
âIn the â80s, when data was being stored digitally, it was fragmented with systems that werenât capable of communicating with each other,â Fawcett says. The next wave in big data is toward the holistic view of data system de-fragmentation and integration. âUltimately,â Jardin says, âin order to analyze data, you need to federate it. Getting all the parts to speak to each other should now be high priority for competitively minded energy companies.â
Originally posted via “How oil and gas firms are failing to grasp the necessity of Big Data analytics”
Source: How oil and gas firms are failing to grasp the necessity of Big Data analytics by analyticsweekpick