PROJECT: WellTender Field Services + Assets

CLIENT: Chesapeake Energy

AUDIENCE: Employees (Foreman, Well Site  Engineers)

COMPANY: Hypergiant

Dates: July 2018-December 2019

$17K/Day Savings Across 5 Regions

“I met Ross as part of my initial introduction to Hypergiant. Ross’ command of design thinking and facility with artificial intelligence applications was among the reasons that we engaged Hypergiant to collaborate on the WellTender application. He was intimately involved with every aspect of the project and was creative and open-minded throughout. Even when development activities hit a snag, Ross was quick to take ownership of the challenge and we were swiftly put back on track. He is a solid communicator and an excellent relationship-builder, although his approach is non-standard. We have maintained a friendly relationship since the conclusion of the project and I can recommend Ross without equivocation.”

– Kentaro Kawamori, CEO/Co-Founder (Persefoni); 

previously Chief Digital Officer (Chesapeake Energy)

The Client Challenge

Calendar-based maintenance of resource sites is extremely inefficient and requires service personnel to travel to and from work sites irrespective of actual need.  To address this problem, internal teams at Chesapeake Energy created WellTender to manage work tickets at sites on an exception basis. When the time came to evolve the application, expand functionality, and introduce artificial intelligence into the solution, Chesapeake Energy partnered with Hypergiant. Providing visibility into team-based work and providing performance transparency were two unexpected benefits for Chesapeake and each was a component of Hypergiant's Intelligent Incentives program.

The Approach

Chesapeake Energy developed the original iteration of WellTender from a user-centric perspective, so partnership with Hypergiant was natural and the relationship extremely productive. Collaboration across more than a year was intense and fruitful allowing for creation of a truly future-forward manifestation of the original promise of WellTender. 

Across vastly expanded capabilities, WellTender 2.0 incorporates multiple forms of artificial intelligence. Unsupervised learning is employed in analyzing maintenance and historical production. Computer vision is utilized for well information gathering and transmission, eliminating the need for every site employee to spend hours each week traveling to a central office location, scanning, and submitting documents to a central repository. Natural Language Processing (NLP) and sentiment analysis are leveraged to review user comments for efficacy which is coupled with user-driven supervised learning. Algorithmic work item matching ensures that the most appropriate employee addresses site challenges according to their own aptitudes, location, and past performance. 

Most significantly, AI-driven routing and telemetry ensures that site issues are addressed via the most efficient travel pathway. Finally, the Intelligent Incentives program tied together all of the disparate capabilities into a format that was intuitive, graceful, transparent and user-focused.


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