Using Cognite Data Fusion®, a major national oil company was able to predict oil quality and optimize the crude oil separation process at one of its facilities—improvements worth an estimated $11.5 million.
$11.5 million in estimated value
Development time reduced by 10 months
Real-time monitoring and prediction
Many oil and gas companies face the challenge of their crude oil fields struggling to meet oil quality export requirements due to too-high water content.
As a part of its efforts to continuously optimize production across its assets, a national oil company wanted to tackle this challenge.
Machine learning could help the operator predict oil quality and optimize the separation process, but the development process was costly and time-consuming.
The challenges included:
Cognite delivered a live, physics-guided machine learning model to help the national oil company identify factors causing poor oil separation with recommendations for how to improve separation.
To build the solution for one oil train, more than 350 sensors were evaluated. More than 100 sensors were used in the live solution.
The model runs on top of the Industrial DataOps platform Cognite Data Fusion®, which solves the data challenges that the company faced.
Cognite Data Fusion® features:
By taking the foundational approach to solution deployment with Cognite Data Fusion®, the national oil company now has the ability to quickly scale solutions across its entire fleet of assets.
The initial deployment of Cognite Data Fusion® for just one oil train at a large separation facility delivered a time reduction of 70%, resulting in gains of more than $11.5 million by improving the quality of separated oil.
The second phase—scaling to four additional oil trains—brings potential gains of more than $75 million.
Finally, the third phase will scale Cognite Data Fusion® to the full fleet of separation facilities. This could lead to additional potential gains of more than $500 million.