The downturn in the oil price that began in late 2014 prompted companies across the world to cut large numbers of their direct employees as well as contractors. Some estimates put the number of oil and gas jobs lost in 2015 alone as high as a quarter of a million.
Prospects for many people still employed in the industry still look tenuous, with further predictions of job losses for the sector. For example, UK government advisor and former Wood Group chief executive Sir Ian Wood, warned in March that a total of 45,000 jobs in the UK North Sea sector could go during 2016.
Yet, the future holds the promise of employment growth in certain areas of the industry. Largely driven by developments in technology, many of these jobs require new skills – or skills currently used in other industries. Indeed, the continuing challenge presented by the new low oil price environment is encouraging some companies to actively seek these new technologies, and the people who can work with them, to boost profit margins.
Rigzone offers a four-part series to examine employment and in this first article, take a look at how the Internet of Things and Big Data will create job opportunities in the upstream sector.
The Internet of Things
In simple terms, the Internet of Things can be described as the network of physical objects – such as machines, buildings and vehicles – that are embedded with electronics so that data can be collected and shared with other objects. Just as humans have for many years had the ability to connect with each other via the internet, everyday machines are increasingly “talking” to each other.
In the oil and gas sector the “digital oilfield” is already a reality according to BP, which has stated that sensors installed across topsides, downhole and in the subsea arena already producing huge volumes of data that can be used to support decision making. Initiatives such as BP’s Field of the Future, Chevron’s i-field and Shell’s Smart Fields are likely to further boost the prevalence of smart devices in oil and gas fields around the world.
But according to strategic consultancy McKinsey’s June 2015 report, “The Internet of Things: Mapping the Value Beyond the Hype,” today less than 1 percent of the data generated by the 30,000 sensors that exist on a typical offshore oil rig is currently used to make decisions. This shows the data collected right now is used mostly for anomaly detection and control, not optimization and prediction – where it can add the most value.
The Internet of Things promises a scenario in which oilfields become fully automated, so that several fields have their production entirely controlled by a remote central operations site. Of course, wells in these fields would need round-the-clock monitoring for pump failures and other issues. A pump failure can cost $100,000s per day in lost production.
While automation will take many hands-on engineers and technicians out of the field, there will be opportunities for other IT-savvy engineers. Such an engineer will sit in an office, perhaps thousands of miles away, monitoring equipment at several fields and will be able to direct maintenance teams when alerted to any issues at a particular field.
Indeed, equipment supplier GE Oil & Gas – a keen proponent of the ‘Industrial Internet’ – already has three iCenters located in Florence, Kuala Lumpur and Houston, where engineers remotely monitor turbomachinery used by its customers around the world. And Chevron operates a ‘Machinery and Power Support Center’ that monitors thousands of pieces of rotating equipment on six continents. Other firms operate similar remote monitoring stations.
Job Opportunity: Internet of Things Engineer
The industry is likely to need specialist engineers who can not only build smart devices, but who will know how to apply them to equipment used in the upstream sector.
An example of this kind of job was advertised by a major oilfield services firm during the last year. The company was looking for an engineer who had software skills, familiarity with networking technologies, experience of embedded systems prototyping (such as familiarity with the Arduino electronic prototyping platform) and experience with a range of Internet of Things-related message protocols (e.g. MQTT, RabbitMW and CoAP).
Data is not new to the oil and gas industry. Seismic data and data gleaned from wells have long been subject to analysis and visualisation. But major advances in sensor technology and data storage means the industry now has more data than it can handle from many different parts of the upstream business.
Norway’s DNV GL, a consulting firm to the offshore sector, is sharing how Big Data could transform efficiencies.
“We’ve been doing a lot of industry surveys and … what we’re finding out is that a key factor in driving Big Data is technological advancements,” DNV GL Senior Consultant Nada Ahmed told Rigzone in a recent interview.
“The platforms that are coming online now are equipped with thousands more sensors, which are being used to collect a lot more data. There’s also better connectivity, as well, which means you can easily transmit large amounts of data onshore from offshore facilities. So, the Internet of Things is leading to more data for the oil and gas industry to use.”
DNV found that the volume of data available to companies is no longer at the gigabyte or terabyte level, but is now measured in petabytes – that’s a “1” with 15 zeros after it. So some industry players are taking a proactive approach to how they manage data, such as establishing innovation and technology centers that focus on the management and interpretation of data. But not all are engaged, which according to DNV, is indicative not just of a recession in the oil and gas sector, but also a lack of clarity on the cost-saving potential of digital technologies in the industry.
However, Ahmed told said, “We definitely do see a trend in that market growing because of the size of the data, because that’s increasing and the industry is becoming more curious about how to use that data … We are seeing a few early adopters who are hiring people with data analytics skills, developing centers of focus to try to see how to use the data they have and then also referring to consulting companies who have expertise in that and how they can better use the data.
“Companies that have traditionally served the utilities and power industry are now looking on to the oil and gas industry to take advantage of the large amount of data being generated. [They] are ahead of the curve in using large amounts of data coming off their grids more efficiently to manage the electricity supply and demand. Techniques used in such parallel industries can be applied to the oil and gas industry to start reaping the benefits of big data.”
Job Opportunities: Big Data Scientists, Big Data Software Engineers
So what are the employment opportunities that Big Data is generating in the oil and gas sector?
“Everyone is very cautious … Even if they do see value in it, right now they are not making any major changes in their workforce other than downsizing,” Ahmed told Rigzone. “But we see the downturn as an opportunity right now to build up your competence in analytics. This is because once the industry does pick up there will be a big need for data scientists. They will be in high demand and I don’t think there are enough data scientists out there today.”
Although Big Data in the oil and gas sector is still in its early days, there are a few jobs being advertised by oil and gas firms right now. Typically, the industry is looking for graduates, especially those with doctoral degrees with a science, engineering or mathematics background, coupled with an understanding of complex data and the ability to interpret large volumes of data. Software development experience – particularly in Java, Python, C, C++ – appears to be a must, also.
Recruiters are not hung up about oil and gas experience. One stated that one-to-five years’ work experience in the oil and gas industry would be helpful, but not required. Another explicitly pointed out that oil and gas experience was not required, but candidates are expected to understand and apply statistical techniques and analytical processes such as Markov, Hilbert, Bayes, Fourier, Gauss, Kernel Tricks and Bootstrapping.