Sizing Up The Digital Prize

Sizing Up The Digital Prize

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The trope is familiar – digitalization is the way forward, a means for the oil and gas industry to drag itself into the 21st century. Advances in cloud computing, predictive analytics, machine learning and artificial intelligence will enable companies to store, analyse, interpret and use a Big Data set in a way few other industries can.

It’s like Aladdin suddenly coming into possession of the sorcerer’s oil lamp – what riches can the digitalization genie conjure up?

Greig Aitken, research director, who led Wood Mackenzie’s work on digitalization, reckons there are three main prizes for the industry to seize.

First and foremost, health and safety. Automation, robotics and drones are among the technologies that allow people to be kept safely away from the front line. Reduced accident and injury rates for the workforce will be an unequivocal boon for an industry that operates at geographical, geological and engineering frontiers.

Second, a structural reduction in costs. To set the scene, senior management in some companies have high ambitions. Some see digitalization as a means to cut the workforce by half, perhaps more. A measure of success will be growing production as headcount and costs fall.

Our own analysis focuses on identifying potential cost reductions rather than headcount. The value chain analysis suggests U.S.$73 billion of potential annual savings for the industry is achievable in the next five years and based purely on known technologies. That’s about 10% of all-in annual global upstream spend. Development and production account for around 90% of the opportunity.

The wins in conventional development will likely be from improvements in project design and delivery – automated platforms with smaller, simpler topsides; and automated drilling. Savings could total U.S.$20 billion. Another U.S.$24 billion could be found in the production phase by using advanced analytics on production data, smart production systems, remote working, predictive maintenance, 3D printing and drone-based inspections.

In unconventionals, automation is among other factors that can speed up drilling, saving US$5 billion annually. In the production phase, unconventional wells decline rapidly. Smart production management can be effective in reducing lease operating expenses and extending the economic life of mature shale wells, adding another U.S.$5 billion a year.

Third, subsurface. We put cost savings at up to U.S.$7 billion annually from faster drilling and processing of geological and geophysical work and fewer dry holes.

The industry is already starting to deliver new and incremental resources using digitalization. Shell cites GeoSigns, its proprietorial seismic imaging and interpreting software suite, as instrumental in discovering 150 million barrels of oil beneath complex subsurface salt structures in the Deimos field in the U.S. Gulf of Mexico.

Separately, BP based the go-ahead for its Atlantis Phase 3 development, also in the U.S. Gulf of Mexico on “breakthroughs in advanced seismic imaging and reservoir characterization” that revealed an additional 400 million barrels of oil in place. These are high-value barrels. We value Phase 3 at U.S.$$1.1 billion (NPV,15) with an IRR of 62% and breakeven of U.S.$29/bbl based on 110 million barrels of oil equivalent recoverable.

The IEA estimated in 2017 that digitalization might boost technically recoverable resources for conventional oil and tight oil by 3%, with similar forecasts for gas. Our sense is that this might prove conservative. Assuming 5% higher recovery adds 15 billion to 20 billion barrels of oil and condensate to Wood Mackenzie’s global database of commercial and technical reserves. The numbers are substantial, but not game changing, amounting only to about half a year’s global oil consumption or a single year’s yield from conventional exploration. There are also questions around value, given uncertainty of extraction cost and the timing of when the oil might be produced.

The digitalization genie’s real value in subsurface could be in exploration. Machine learning in seismic interpretation, with parallels to technologies such as facial recognition, could reduce risks and help identify new prospects. The prize would be lower finding costs and more yet-to-find resources.

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