Outside the Toy Box

Digital tools already in use in other industries could transform oil and gas

Digital transformation, including data management, analytics, machine learning and artificial intelligence are already at work in many industries, including retail and healthcare. And, many of these industries have learned that there is a commonality in the data collection, management, processing, manipulation and reporting they all do. It is a phenomenon the oil and gas sector has been slow to embrace, even though its needs are not all that different from those of these other fields.

Chikoke Ejimuda, principal engineer and scientist at hybriData, said the reasons for that may be found in the DNA of the profession.

“It’s in the work itself. Adapting a new technology may dramatically alter or reduce the efficiency of some processes, as well as lead to a loss of revenue,” he said.

Slowly, though, that reluctance to change is giving way and the digital embrace is picking up significant momentum, he believes, both in individual oil and gas companies and the profession itself.

“Some organizations now have dedicated advanced analytic teams and industry-wide conferences now include hackathons and pitch-style competitions to spur innovation,” Ejimuda said.

For this to be successful, comparisons and contrasts will have to be drawn between data types and workflows, so that new approaches can emerge without negatively affecting existing efficiencies. The goal is to explore how a disruptive technology from one industry – those processes that shake up one industry while fostering another – may be applied to another, like oil and gas, while also stimulating cross-cultural learning.

Ejimuda said it’s in the company’s best interests to pursue such disruption, because no matter how successful a company is, “becoming number one is easier than remaining number one.”

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Digital transformation, including data management, analytics, machine learning and artificial intelligence are already at work in many industries, including retail and healthcare. And, many of these industries have learned that there is a commonality in the data collection, management, processing, manipulation and reporting they all do. It is a phenomenon the oil and gas sector has been slow to embrace, even though its needs are not all that different from those of these other fields.

Chikoke Ejimuda, principal engineer and scientist at hybriData, said the reasons for that may be found in the DNA of the profession.

“It’s in the work itself. Adapting a new technology may dramatically alter or reduce the efficiency of some processes, as well as lead to a loss of revenue,” he said.

Slowly, though, that reluctance to change is giving way and the digital embrace is picking up significant momentum, he believes, both in individual oil and gas companies and the profession itself.

“Some organizations now have dedicated advanced analytic teams and industry-wide conferences now include hackathons and pitch-style competitions to spur innovation,” Ejimuda said.

For this to be successful, comparisons and contrasts will have to be drawn between data types and workflows, so that new approaches can emerge without negatively affecting existing efficiencies. The goal is to explore how a disruptive technology from one industry – those processes that shake up one industry while fostering another – may be applied to another, like oil and gas, while also stimulating cross-cultural learning.

Ejimuda said it’s in the company’s best interests to pursue such disruption, because no matter how successful a company is, “becoming number one is easier than remaining number one.”

The Digital Transformation Movement

And he believes it was that mindset that spurred the new technology in the first place.

Some of the changes were visionary, some reactionary, some necessary.

“Increased competitive pressure and unstable oil prices and growth opportunities in new markets launched the digital transformation movement,” he said.

“It originally started,” he said, “from the low-cost internet, devices, cloud computing and the open source communities (AI, ML, IIoT, Big Data) built around technology. The internet ushered the era where you could easily share information with the world in seconds.”

The cost of commodity hardware devices then dropped, making it possible for companies to use distributed and scalable cloud computing services under a pay-as-you-use model.

It was all coming together.

“Having the software and hardware in place, all it took was the reality of the open source communities to make it possible for smaller/mid-tier companies to compete with the larger companies and even create new markets,” he said.

Additionally, and particularly intriguing to Ejimuda, as it is to many in all industries, not just oil and gas, is the role of artificial intelligence in all this.

But having artificial intelligence, he said, can’t just be about having artificial intelligence for its own sake.

“To maximize the benefits of AI, we need to pay closer attention to infrastructures that actually produce AI solutions,” he said.

Self-driving Drills?

Such matters will be part of the inaugural Energy in Data Conference in Austin, Texas, June 17-19, sponsored by AAPG, the Society of Exploration Geophysicists and the Society of Petroleum Engineers, which will do a deep dive into such digital transformation. Specifically, Ejimuda, along with representatives from Microsoft, Fuld+Company and Maverick Labs, will lead a general session called “Outside the Oil and Gas Toy Box.”

“Comparisons and contrasts will be drawn between data types and workflows,” said Ejimuda, “so that new approaches can emerge and knowledge from one industry to another can be leveraged.”

As one such example, he said, “We can learn from self-driving car technology, to improve drilling, subsea and facility engineering processes.”

And one of the ways this can be done is simply by following directions … literally.

“For instance, in drilling engineering, we simply navigate the drill bit to a location (reservoir) that contains hydrocarbon,” he said, which he likens to taking a self-driving taxi from an address to your destination.

“Similarly, within subsea, you are navigating a (remotely-operated vehicle) to a location where it can retrieve/install wellhead or production related assemblies,” he said.

And that’s when the pipeline inspection gauges, or “pigs” – devices that are also called “scrapers,” which are used for various maintenance operations around the pipeline – come into play.

“As far as facility engineering goes, intelligent pigs are deployed at such locations either clean up that area of the pipe or even repair it,” Ejimuda said.

He said by leveraging autonomous vehicle (robotics) industry techniques used by self-driving cars, we can improve these processes even in a cost-effective way.

Ejimuda said that an explanation of how non-oil and gas industry companies are gaining value from the digital transformation and how the cross-discipline integration in a manpower- and time-constrained manner are and will be needed to compete effectively.

The name “Outside the Oil and Gas Toy Box” just made sense to him.

“Personally, I see it as an analogy to depict how some oil and gas industry processes still utilize old IT processes and infrastructure to advance their digital transformation journey,” he said.

Comments (1)

How relevant are retails sales and self-driving cars to geoscience processing tasks?
Retail sales and computing water saturation. How similar? Or how similar are retail sales and drawing (automatically or by hand) geologic contours? Are we not trying to shoe-horn geoscience into a retail sales box when saying that O&G lags behind adoption of AI and ML and Big Data and Data Analytics and all the rest of those buzzwords, so gee: what's wrong with people in the O&G industry? Maybe we have our own set of problem types and data space domains that truly are NOT similar enough to retail sales and self driving cars to allow a plug-and-play application of previously-fielded and operationalized data analytics products to geoscience problems. After all, it isn't like O&G people don't have the brains to figure out how to apply data analytics (which are truly nothing more than traditional algorithms, methodologies, and mathematical/computerized processes renamed). Expending a lot of effort trying to fit problems into solutions that are very faddish is not very productive, except to allow people to discover that really there is not very much new under the hood of this new set of products that sales people (paid or "altruistic") are pushing.
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6/13/2019 12:00:58 PM