This document summarizes two case studies of companies using the PI System to optimize digital oilfield operations. The first case study describes how Occidental Petroleum used PI System data and analytics to transform their operations from reactive to predictive. The second case study outlines how Eni implemented real-time online modeling of upstream assets using PI System data fed into simulation and optimization algorithms. Both companies leveraged PI System's integrated edge-to-cloud architecture to improve efficiency and reduce costs through predictive analytics.

1. Case Studies Enabled by the PI System
Best Practices for Digital
Oilfield Optimization
Cynthia Bourne (Sr. Systems Engineer)
MAY 18, 2022
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2. AVEVA PI System’s edge to plant to cloud data management
© 2022 AVEVA Group plc and its subsidiaries. All rights reserved.
An integrated, edge-plant-cloud architecture supports OT, IT and IIoT use cases
At the edge
Pervasive, real-time data
collection from sensors,
IIoT devices and remote assets
In the cloud
Scalable data services available
for a wider array of users, tools
and applications
On-premises
Enriched industrial data available
24/7 for critical operations

3. Proven components accelerate time-to-value
© 2022 AVEVA Group plc and its subsidiaries. All rights reserved.
Control systems
& historians
Remote assets,
sensors, & IIoT
devices
Edge Data
Store (EDS)
Industrial applications
and analysis tools
Cloud data silos
AVEVA Data Hub
(formerly OSIsoft
Cloud Services)
AVEVA PI Server
on-premises
Data sharing with trusted
ecosystem partners
Advanced analysis
via AI/ML platforms
PI Vision: no-code, self-
service visualization
PI DataLink: Microsoft Excel
add-in
Via ready-to-use
edge adapters
Via open APIs
and pretested
integrations
Via ready-to-use
system interfaces

4. From Digital Oilfield to Digital
Transformation to Full Field Analytics
CASE STUDY #1
4

5. Business Challenge
5
• Turning 4.5 MM PI tags
into useful information
Digital Oilfield
• Using the data to
improve processes
Digital
Transformation • Using analytics to drive
change
Full Field
Analytics

6. Digital Oilfield
6

7. Digital Transformation
Using data to
drive change
7

8. From Digital Transformation to Full Field Analytics
8
1 - Routine Inspection
2 - Alarm Notification
3 - PI Analytics
4 - Automatic Adjustments
5 - PI Future Data
6 - Model Predictive Control
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
Analytics toAct on Predictions
Analytics to Act on Exceptions
TargetState

9. 1 – Manual Exception Identification
9
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
AnalyticstoAct onPredictions
Analytics to Act on Exceptions
1
2
3
4
5
6
Manual high tank level
identified on daily route

10. 2 – Management by Exception
10
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
AnalyticstoAct onPredictions
Analytics toAct on Exceptions
1
2
3
4
5
6
Operator receives a high
tank level alarm and goes to
this facility first

11. 3 – Analytics to Identify Exceptions
11
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
AnalyticstoAct onPredictions
Analytics toAct on Exceptions
1
2
3
4
5
6
PI System Asset Analytics
identifies an exception - High
water tank level & disposal
wells not 100% open

12. 4 – Analytics to Act on Exceptions
12
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
AnalyticstoAct onPredictions
Analytics to Act on Exceptions
1
2
3
4
5
6
Automatic adjustment of
disposal wells to reduce high
tank level

13. 5 – Analytics to Predict Optimization
13
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
AnalyticstoAct onPredictions
Analytics to Act on Exceptions
1
2
3
4
5
6
PI System future data
predicts a high level,
providing more time to
respond

14. 6 – Analytics to Act on Predictions
14
Manual Exception Identification
Management by Exception
Analytics to Identify Exceptions
Analytics to Predict Optimization
AnalyticstoAct onPredictions
Analytics to Act on Exceptions
1
2
3
4
5
6
Automatic adjustment of
disposal and producing wells
to maintain optimal tank level

15. Field Mobility – Virtual Control Room & Ops Dashboard
15

16. Occidental Petroleum
Levering the PI System to Drive Change
16
Transform ways of working
• Take advantage of the massive
amounts of data in the multiple PI
System deployments to change work
processes
Operate fields more like a
manufacturing plant using full
field analytics
• Use PI System data to predict and
prevent upset conditions
Increasing operational efficiency
• Still in early stages, but making
progress in transforming operations
from reactive to predictive and
preventative
Challenge Solution Results

17. Digital Implementation Extended
to Full Field Optimization
CASE STUDY #2
17
Real-time Online Modeling of Upstream O&G Assets

18. • Primary objectives:
• Maximize asset value defining best configuration and optimizing operating
parameters
• Identify residual potential and bottlenecks
• Minimize intervention time and costs
• Minimize/avoid shutdowns
• Minimize structural interventions
• Focus on Production Facilities: from wells to sales point:
• Reservoir
• Gathering Network
• Wells
• Treatment Plant
Integrated Production Optimization
18

19. Project Scope – Main Continuous and Real-time Features
19
1. Monitoring 2. Optimization 3. Updated Models

20. PI System Connection – Standard eDOF Infrastructure

21. PI System – eDOF
21

22. Simulation
e-rabbit
Optimization Algorithm – Solver
eDOF
PI
Data feeding online
and in real-time
22
21
PI System Feeding Integrated Production Optimization

23. PI System - KPIs

24. Operating Workflow – Results Examples
24

25. Eni
Real-Time Online Modeling of
Upstream Oil &Gas Assets
25
Simulation models rely on
manual inputs and get out of
date quickly, leading to:
• Poor diagnosis/monitoring of
production system
• Bottlenecking and production
inefficiencies using field operating
conditions/constraints
Real-time online digital twin based
on:
• Digital oilfield solution (eDOF) fed by
PI System
• Advanced algorithms (e-rabbit)
• Simulators (Olga, Eclipse, HYSYS, etc.)
Real-time online monitoring of
assets via digital twin and a
continuously updated asset
optimization model
• Early and continuous support for
detecting issues, bottlenecks, and
optimization opportunities
• Significant time reduction
updating models
Challenge Solution Results

26. Case Studies Enabled by the PI System
Best Practices for Digital Oilfield Optimization
Oxy - From Digital Oilfield to Digital Transformation to Full Field Analytics
Link to PI World 2019 presentation
Eni - Real-Time on-line modeling of an oil & gas upstream asset
Link to PI World 2019 presentation

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are or may be construed as being forward-looking. While these forward-looking statements
represent our current judgment on what the future holds, they are subject to risks and uncertainties
that could result in actual outcomes differing materially from those projected in these statements.
No statement contained herein constitutes a commitment by AVEVA to perform any particular action
or to deliver any particular product or product features. Readers are cautioned not to place undue
reliance on these forward-looking statements, which reflect our opinions only as of the date of this
presentation.
The Company shall not be obliged to disclose any revision to these forward-looking statements to
reflect events or circumstances occurring after the date on which they are made or to reflect the
occurrence of future events.
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28. linkedin.com/company/aveva
@avevagroup
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