The Fuse

Automation, Big Data Improve Shale Efficiency, Alter Jobs Outlook in Oil & Gas Sector

by Matt Piotrowski | February 06, 2018

U.S. production is continuing to surge, in large part because of greater efficiencies in the shale sector.

Companies have implemented new technologies to automate simple tasks, and Big Data and machine learning have improved productivity. These changes have occurred rapidly over the past few years, spurred by the price downturn, which forced companies to become more resourceful and control costs. But what’s been good for overall oil production has been bad for shale sector jobs. The trend is likely to continue—with more innovation in shale, increased automation will lower the number of people needed to work on each rig. “We’re running our fields with less people so that’s a reality as well,” ConocoPhillips CEO Ryan Lance said last year.

What’s been good for overall oil production has been bad for shale sector jobs.

In September 2014, there were more than 535,000 people employed in U.S. oil and gas. By early 2017, the sector lost about 185,000 jobs as both prices and production declined. The number employed has rebounded, but by only 40,000. At the end of 2017, there were 390,000 workers in oil and gas, about 145,000 below the high in 2014, despite production accelerating.

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Employment among drilling operators—such as Exxon and other oil companies—has remained mostly stable, but in the oilfield services industry—which includes Schlumberger or Halliburton—the jobs outlook has been more volatile (see below).

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“Companies are able to produce more oil per well and drill wells faster with existing capital,” Kunal Patel, Senior Research Analyst with the Federal Reserve Bank of Dallas, told The Fuse. “Automation is done by digital oil fields with greater use of technology and Big Data.”

“Companies are able to produce more oil per well and drill wells faster with existing capital.”

Since the price downturn, companies have prioritized digital technologies, artificial intelligence, automation, robots and drones, connectivity, and data analytics in their budgets. Although U.S. crude production has risen past 10 million barrels per day (Mbd), the number of rigs deployed are half of the peak in October 2014. Analysts say that the average number of workers on a rig will fall by 40 percent in the next few years to 15.

Here are some activities across the industry that have streamlined activity:

  • Drilling multiple wells on the same pad so rigs can move around easily
  • Using automated robotics to handle pieces of pipe and minimize disruptions
  • Running steel pipe down wells through remote monitoring
  • Measuring pressure and flow of oil virtually
  • Utilizing automated weather sensors to determine shifts in seismic activity
  • Using unmanned aerial vehicles to discover leaks and attacks on pipelines

Big Data in oil

According to McKinsey: “Rapid advances in automation technologies such as artificial intelligence, robotics, analytics, and the Internet of Things are beginning to transform the way resources are produced…including underwater robots that repair pipelines, drones that conduct preventive maintenance … and the use of data analytics to identify new fields.”

This dynamic has led industry to look for workers with different skill sets. Companies have shifted their hiring priorities to data scientists instead of engineers, geologists, and field workers.

Companies have shifted their hiring priorities to data scientists instead of engineers, geologists, and field workers.

Data analytics is crucial to the industry because it can more accurately define a project’s costs and well productivity. Data analytics has improved accuracy, hastened lead times for new projects, and helped companies determine where to drill. Just as crucially, they do not have to resort to trial and error for new wells by drilling in a variety of areas. As a result, they avoid dry holes, which are costly. Real-time geological and mapping data also reduces the amount of time between planning and first production, further reducing costs.

McKinsey even speculated that within “…ten years, oil and gas companies could employ more PhD-level data scientists than geologists.”

Automation, Big Data to continue

One big question for shale firms is how much of the apparent cost reductions during the price downturn were the result of structural efficiency gains and how much were simply the result of a cyclical decline. ConocoPhillips said that two-thirds of cost drops stemmed from efficiency, and others have estimated that about half of cost savings are structural. As the market for oilfield services has increased in the past year, costs have predictably rebounded, too, leading some to downplay recent efficiency gains and the positive effects of automation and Big Data. “Efficiency will be lost as activity continues to increase,” one investor told The Fuse. “Quantifying the effects of efficiency is complicated.”

There is a new sense of determination among oilfield services, and the industry at large, to continue to improve efficiency to keep costs under control.

Nonetheless, there is a new sense of determination among oilfield services, and the industry at large, to continue to improve efficiency to keep costs under control. And they believe they can do that through technology. Thus, automation and the use of proprietary datasets will continue to be big in the industry in coming years. For instance, with “next generation” drilling rigs that have “closed loop automation,” companies will use systems that can communicate between the surface and the equipment below ground, and perform a number of tasks automatically at the same time.

The price downturn of 2014-16 hurt the industry, as seen in the large number of job losses and the decline in capital spending, but it also spurred companies to exploit new technology and data analytics. Digitization, automation, and other structural operational gains will continue for the foreseeable future now that companies know the rewards. This should reduce costs—but the industry’s workforce may forever change, and likely shrink as a result.

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