Data Science & Analytics

Anadarko, AAET, and Decisions Faced by Shale Operators

Readers of DataShopTalk know we’re firm believers that advanced analytics and data science are, and will continue to be, fundamental to O&G development – especially in unconventionals. Many E&P companies have also embraced this position, some more than others. Anadarko made significant investments through their Advanced Analytics and Emerging Technologies (AAET) group. Founded in 2016, the group grew to over 50 data scientists and software developers.

Buy vs. Build

As a recent JPT article points out, AAET had some major successes that touched a large portion of the business and Anadarko’s strategy could serve as a template for other operators to emulate. Any operators considering going down the path of creating their own AAET will have to evaluate the buy vs. build trade off, a decision I’ve seen play out at many E&P’s.

Anadarko’s approach was to develop internally those “initiatives with the highest potential impact on the bottom line.” Will other operators, especially ones of similar size to Anadarko, follow suit? The decision really comes back to what operators see as their core competency. Building quality software takes a lot of effort but so does maintaining and supporting it year after year.

Time to Value

The key factor facing operators interested in building their internal capabilities is the time it takes to see results. The JPT article cites one use case developed by AAET that took two years to deploy. That’s not uncommon, and I’ve heard from other operators where a two to three-year time span is typical for an internally developed analytics solution. If you’re considering going down this path, then you should also take into consideration that the development time only starts once the team is in place. Just building a team and getting them up to speed can easily take 12-18 months. This means that if you’d don’t have the team yet, you’re looking at a minimum of three years to see value.

If you were to follow the principle that initiatives with the highest potential impact on the bottom line are to be built internally… then you need to be prepared to invest up front and wait multiple years for any results. The potential returns from an impactful analytics solution – something that will affect decisions on capital allocation or recovery factors – can easily dwarf the operating expenses required to build a data science team. The real trade off to the buy vs build question rests on how soon you want to see these results. Develop internally and wait years? Or buy off the shelf solutions (perhaps do light customization) and see an impact in weeks or months?

What Next

I could certainly see how a shale operator keeps a few data scientists and developers on staff to customize or build on top of a commercially available platform but am suspect of the ROI on building platforms and applications internally from the ground up. A team of 50 data scientists and developers will cost much more than most operators can justify.

The Petro.ai team has put in years of development effort to build our flagship petroleum analytics software. Petro.ai gives operators the power and insight of an advanced analytics team at a fraction of the cost. And it can be deployed in weeks, not years. Because it’s an extensible platform, a small team of power users also can build on top of Petro.ai to add customization and build internal workflows that become their differentiated IP. Regardless of where your company currently is on the analytics spectrum, we can help advance your digital strategy. We work with operators that are just getting started in analytics but also help advanced internal teams achieve more, faster.

AAET was an exciting experiment and I commend Anadarko’s leadership for their forward-thinking approach. It will be interesting to see how Occidental integrates Anadarko and the work done by AAET.

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