CASE STUDY

Industrial DataOps
Lays Foundation for
Predicting Oil Quality
at a Major National
Oil Company
Cognite Data Fusion solved the following challenges toimprove the quality of separated oil:
Access to and sufficient analysis of data
Building an analytical model that incorporates both
the governing physical phenomena of the separation
process and a data model of the separation facility
Operationalizing the solution into production
Real-time monitoring and governance of theproduction modelCOGNITE.COM

SECTION ONE

Industrial DataOperations:
Built for Scale
By leveraging Cognite DataFusion, initially for one oiltrain at a large separation
facility, a major nationaloil company (‘the NationalOil Company’) was able
to improve the quality ofseparated oil, resulting in over
$11.5 million gains.

Time to deploy one solution, with and without
Cognite Data Fusion
Without Cognite
Data Fusion

14 months of man-hours

With Cognite
Data Fusion

4 months of man-hours

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

2

COGNITE.COM

Quality of Oil
Separation Solution
As a part of their continuousproduction optimization
efforts across their fleetof assets, the National Oil
Company focused on thechallenge of crude oil fields
struggling to meetthe export specifications foroil quality due to

To build the solution for
one oil train, more than 350
sensors were evaluated, withover 100 sensors being usedin the final solution.

too-high water content.
To solve this challenge,
Cognite delivered a live,
physics-guided machinelearning model to identify
bad actors causing
poor oil separation withrecommendations for how toimprove separation.

70%
Time to value wasdecreased by 70% for theinitial asset, making an
immediate impact on thebusiness.

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

3

COGNITE.COMDeployingfor proof,
buildingfor scale
In line with their broader efforts
of digital transformation andapproaching solution creationfrom the foundational data
layer up, the National Oil
Company was able to realize
immediate value with the firstsolution deployment, achieving
a 70% reduction in time tovalue. It will continue to realize
the exponential impact of its
investment as it solves theproblem of scale.

By laying the foundational data
model for scale in Cognite Data
Fusion, the National Oil Company
will quickly deploy the quality ofoil separation solution—first to 4
additional oil trains, and then to
over 35 separation facilities.
Building AI and ML solutions for
production optimization on topof Cognite Data Fusion allowed
the National Oil Company tooperate as efficiently as possible,
maximizing production andreporting transparently on
their sustainability progress toregulators, the financial sector,consumers, and potentialemployees.
The efficiency gains can beseen at each step of theprocess from raw data tocreation of the live, physicsguided machine-learning model
deployed into production.

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

4COGNITE.COM

CDF value story

Speed
Depth
Quantity

Set up infrastructure
Security approval
AccessIngestn

Evaluate and explore data
Preprocessing and prepare dataAnalyze
DeployontextualizeTag and repair
Contextualize
Visualize

Data governance
Data lineage

erov
dG
r an
Monitoand
Understand

alec
S

ess
cc,A
te

Collect, In
tegraCDeve
lop and Deploy:

IN-DEPTH USE CASE: OPERATIONAL EFFICIENCY AT SCALE

5COGNITE.COM

Without CDF

With CDF

Set up infrastructure:

Set up infrastructure:

Security approval:

Security approval:

COLLECT, INTEGRATE, ACCESS

Solution architects set up onpremises compute service(openshift), a time series
database (influx db) andvisualization service (Grafana)

Data owners needed tobe convinced that thedata will be handled
securely and motivatedby a valuable use case

Solution architects set up
Cognite Data Fusion with allof its resources

Cognite Data Fusion
documentation wasprovided to the National
Oil Company forsecurity approval

1,200
man-hours
saved

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY6COLLECT, INTEGRATE, ACCESSCOGNITE.COM

Without CDF

With CDF

Access:Access:The National Oil Company’s
security department
worked with multiple
presentations about security
and authentication to find asolution that the Customer’s
stakeholders approves

Established methods forsecurity make for trust inauthentication

Set up oauth for the onpremise infrastructure

Ingest:created custom OSI PI data
extractor for time series
database
Metadata was hard-coded

Cognite supports multiple
forms of authentication
easily integrated withCognite Data Fusion

Ingest:Cognite already has
extractors available formany data sources, e.g.
a OSI PI extractor that isrobust and easy to setup

Documents were notingested; they werehard-coded

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

7

CONTEXTUALIZE AND UNDERSTAND

COGNITE.COM

Without CDF

With CDF

Tag and repair:

Tag and repair:Contextualize:

Contextualize:

Visualize:

Visualize:

No options existed fortagging and repairing.
Done manually in the usecase phase

Negligible contextualizationin the custom solution

Grafana was used tovisualize and verify that datawas available

Utilized metadata, assethierarchy, relationships
nd templates, data quality
monitoring in Cognite
Data Fusion

Cognite contextualization
connects data sources
together for easy
exploration and use

Visualization is available outof the box

16+hours timesavings

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

8

DEVELOP AND DEPLOY

COGNITE.COM

Without CDF

With CDF

Evaluate/data
exploration:

Evaluate/data
exploration:

Stacks of hard-copied
papers of P&IDs andP&FDs were used to identify
tags, and a dashboard wasused to verify data together
with SMEs
There was no good place tostore the contextualization,
which was all done inconfigurations specific tothe use case. similar to alarge excel spreadsheetwith mappings

Data scientists andcustomer SME work
together to find therequired data for theuse case using features
including P&ID parser toexplore data and set up
templates to keep track ofdata

Siloed data and Outputof data exploration donein an Excel spreadsheet,including configuration files
inside the source code of
the use case

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

360

man hours
saved9DEVELOP AND DEPLOY

COGNITE.COM

Without CDF

With CDF

Preprocessing andpreparing of data:

Preprocessing andpreparing of data:

Needed to establish
methods for preprocessing
specific to theinfrastructure available

Raw data is transformed toa dataset to be used for theuse caseCognite Data Fusion
functions are utilized, witheasy data-fetching andestablished methods making
this process much faster

Analyze:

Analyze:

Iterative process of tuning
the model and determining
feature engineering

Iterative process of tuning
the model and determining
feature engineering

Feature engineering basedon governing physicsFeature engineering basedon governing physics

Model created using
Python code and open
source libraries

Model created using
Python code and open
source libraries

Collaboration between
Cognite SMEs and National
Oil Company SMEs
IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

10COGNITE.COM

Without CDF

With CDF

Deploy:Deploy:DEVELOP AND DEPLOYDocker containers andrunning the containers on
openshift (server)More labour-intensive
because it required
involvement from solutionarchitects from Cognite andthe National Oil Company

Cognite Functions (based onopen functions as a service)
No need for solutionarchitects to be involved incloud hostingSpecial case: deployment
had to be scheduled
because it was on-premises
and needed assistance fromthe National Oil Company IT
Cognite SAs spent time
setting up test environment
in Norway

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

11

COGNITE.COM

With CDF

Not previously possible

Alerted when themodel does not run orproduces errors

MONITOR AND GOVERN

Without CDF

Alerted when the data
source is down
Alerted when front end
is down

SCALE

Monitoring
nowpossible

Not previously possible

Oil quality separationsolution scaled across thefleet of assets

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY

12COGNITE.COM

SECTION THREE

Solutionand value
realization
Separated oil quality use cases
deployed at the National
Oil Company needed tobe architected to run live inproduction, at scale, before thefirst line of code was written.
By taking the foundational
approach to solution
deployment with Cognite Data
Fusion, the National Oil Company
now has the ability to quickly
scale this solution across its
entire fleet of assets, and quickly.
Industrial DataOps delivers
exponential value in production
optimization

The initial deployment of Cognite
Data Fusion for just one oil train
at a large separation facility
delivered a 70% time reduction,
resulted in gains of over $11.5
million for the National Oil
Company, by improving thequality of separated oil.
The second phase—scaling tofour additional oil trains —brings
potential gains of over $75
million. Finally, the ultimate value
is set to be realised after the final
third-phase scaling of Cognite
Data Fusion to the full fleet ofseparation facilities.

This will lead toadditional potential
gains of over$500 million.

IN-DEPTH USE CASE: MAJOR NATIONAL OIL COMPANY13COGNITE.COM