Meet Jason, A Production Engineer


Jason graduated 6 years ago with a petroleum engineering degree.
After 3 years as a junior production engineer he was promoted quickly to a Senior Production Engineer.


He needs to make sure the oil field is performing at optimal production efficiency at any given time.


He needs to avoid blind-spots to solve production shortfalls and have the right data,
at the right time
in order to make the best decisions.
He must take high-confidence actions quickly.




7:00 AM, Jason Inherited a Hidden Problem

Jason receives an alarm that production is down 20% for Well A-22 and he notices the steam to oil ratio is higher than modeled. He begins troubleshooting the problem before his team meeting at 8:00 am.





Dashboard showing steam map

Jason zooms into the problem area. The steam map model indicated a break through shouldn’t happen for another two years. Jason needs insights!





Natural Language Collaboration

Jason begins investigating by formulating a hypothesis and asking Watson questions. Watson has read data back to the beginning of the oil field.




Watson correlates similar scenarios and presents to Jason for investigation.




Jason selected F-3 as the target well and Watson visualizes correlating events.


 Comparing Wells


Jason hides the collaboration panel, then uses the timeline to compare the scenarios.



Watson ‘understands’ similar topics from different types of sources.






Watson reveals a similar problem occurred in 2010.




The Moment of Insight!


The root problem is likely a fracture, induced in Well A-22, 9 months ago. Further investigation with Watson (not shown) reveals the appropriate action to save $$$ is to begin a shut-in.








Without decades of experience, Jason was able to form a hypothesis and understand the root cause in about an hour, using Watson. Jason also avoided spending days locating, waiting, and researching files, ultimately saving millions of dollars for his company.

Cognitive Computing – Empowering Jason