YPF iUp intelligent Upstream...CONFIG TEMPLATES PRODUCTION OBJECT MODELS OPC DA DLL (developed with...
Transcript of YPF iUp intelligent Upstream...CONFIG TEMPLATES PRODUCTION OBJECT MODELS OPC DA DLL (developed with...
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YPF – iUp intelligent Upstream
Máximo Frias – Lucas Llano
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Outline
• YPF intro
• Business challenge
• iUp’s Big Picture • iUp SCADA
• iUp Web
• Conclusion
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A 95-year-old
company
Publicly
traded
corporation
since 1993 on
the NY and BA
Exchanges
The largest
O&G producer
in Argentina
World-class
shale producer
The leading downstream player
in Argentina
YPF Luz fifth-
largest power
generator in
Argentina:
542 Kboe/d (LTM)
41% Market
Share
98.4 K boe/d
894 productive
wells
The biggest
outside the US
• 3 refineries: 50% of Argentina´s capacity.
Over 320 kbbl/day
• +1,500 gas stations. +36% Market Share
• +55% Market Share of diesel and gasoline
• 120 branches covering the agro sector
• #1 petrochemical manufacturer: output of
over 2.2 mm tons/year
The Company
that invests
most in
Argentina
1.8 GW
USD 3,400 (LTM)
YPF TODAY
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UPSTREAM: SIGNIFICANT POTENTIAL WITH LEADING MARKET POSITION
4 Source: Company data 2017.
(1) As of December 2017.
YPF has 112 concessions in the most productive Argentine
basins (total reserves 1P: 929 mm boe) and 23 exploration
blocks in the country
Proved reserves: 29 mm boe
% liquids: 10%
% gas: 90%
Production: 6.3 mm boe
Noroeste
Proved reserves: 243 mm boe
% liquids: 85%
% gas: 15%
Production: 39.5 mm boe
Proved reserves: 37 mm boe
% liquids: 12%
% gas: 88%
Production: 8.2 mm boe
Austral
2017
Proved reserves 1 Production share
Liquids
52%
Gas
48%
Total: 929 mm boe Total: 359.3 mm boe
Pan American
17%
Pampa
3%
Others
20%
Pluspetrol
3%
Chevron
2%
Wintershall
5%
Total Austral
6%
YPF
41%
Sinopec
3%
Source: IAPG, as of September 2018.
Golfo San Jorge
Proved reserves: 29 mm boe
% liquids: 99%
% gas: 1%
Production: 7.1 mm boe
Cuyana
Proved reserves: 590 mm boe
% liquids: 40%
% gas: 60%
Production: 141.5 mm boe
Neuquina
Headquarters
Buenos Aires
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iUp’s Big Picture
17K 220 14
We have scattered in supervised by
oil wells oil fields different production and
artificial lift methods
controllers
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Before: Well Grids
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Before: Dyna Cards
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iUp’s Big Picture
Oil Wells
Oil Field Data Center Corporate Network
Communication
Driver
SCADA
PI Interfaces PI Suite
Corporate DB
OLEDB
iUp
SCADA (powered by AF)
SDK
PI ProcessBook
PI Vision
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iUp SCADA
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SCADA
DATABASE
COMM
DRIVER
ENGINEERING
STATION
PI OLEDB
CONFIG
TEMPLATES
PRODUCTION
OBJECT
MODELS OPC DA DLL
(developed with SDK)
OPERATOR
STATION PI WEB API
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iUp’s Big Picture
Oil Wells
OLEDB
Oil Field Data Center Corporate Network
Communication
Driver
SCADA
PI Interfaces PI Suite
Corporate DB
iUp
SCADA (powered by AF)
SDK
PI ProcessBook
PI Vision
iUp
Web
ESRI
WebAPI
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iUp Web
Widgets
• Torque
• Goodman
• Pump
Curves
• Dyna Cards
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iUp Web
Well overview (PI VISION)
Well Torque (PI WebAPI) ESP Curves (PI WebAPI) Goodman (PI WebAPI)
ESRI Maps (PI WebAPI)
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iUp Web - Beam Pump Torque
• Minimum Net
Torque
• Minimum
CLF
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iUp Web – Beam Pump Goodman
• Metallurgical
stress
• Service Factor
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iUp Web – ESP pump curves
• Real-time data
• Operating
window
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iUp Web: Dyna card diagnostic
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Conclusion
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• 700 new users (72% of total) in 1 year
March 2018 May 2018 July 2018 September 2018 November 2018 January 2019
1000
500
January 2019
712 users
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RESULTS CHALLENGE SOLUTION
Business Challenge: standardize data countrywide
17K wells in 220 oil fields
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Collect, transform and display data across the country in a uniform way.
Provide SCADA with template capability and give users a website with precise information
Everybody gets the same information, the same way and performs the same analysis.
• PI AF
• PI Web API
• SQL and OLEDB
• 700+ users
• Engineers more productive
• Increase uptime
• Field data collection
• Asset modelling
• Presentation layer
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Next steps
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• Mobile
• Notifications
• KPIs
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Presenters
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• Lucas Llano
• Data Management Specialist
• YPF
• Máximo Frías
• SCADA Specialist
• YPF
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Questions?
Please wait for
the microphone
State your
name & company
Please remember
DOWNLOAD THE MOBILE APP
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Improving Data Quality at Marathon Oil using PI AF and EQM
Presented by: Scott Williams
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Agenda
• About Marathon Oil
• What is EQM?
• Problem Background
• Business Challenge
• Solution
• Results Obtained and Business Impact
• Conclusion
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About Marathon Oil • Became an Independent E&P company in 2011.
• Based in Houston, Texas. With international operations in Europe and Africa.
• Our strategy is focused on the lower cost, higher margin U.S resource plays
Accelerating operations through digital technology.
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About Marathon Oil
PI Growth over the past 5 years
• Increased tag count from 150k to 800k
• Increased use of AF Models
• Over 100 AF Templates
• Use of Event Frames to capture events leading to water and oil haul notification
• Substantial use of PI Analysis and Notifications
• CoreSight and now PI Vision used for dashboards
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What is EQM?
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Planning Drilling Completing Producing
Equipment Maintenance Application
• In house developed application
• Designed to solve data quality issues and increase efficiency
• Focus on completion and production stages
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Problem Background • Data Quality
• Updating each system involved manually entering data.
• Data Consistency
• Inconsistent data across systems
• Efficiency
• Timing of when each system was updated was inconsistent.
• Taking days to weeks.
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Problem Background
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Plant Maintenance
SCADA
Measurement
Hydrocarbon Accounting
PI
Time
System update delays
Cross system dependencies
Similar data entered
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Problem Background
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How can we ensure data quality and data consistency across systems when the process looks like this?
UserInput
ProCount
SAP PM
OPC Server
UserInput
UserInput
UserInput
UserInput
UserInput
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Business Challenge
In a lower priced oil market you must reduce costs and increase efficiency:
• Better tools for Operations by combining data from multiple systems
• More complete view of the well
• Prioritize operator tasks
• Marathon initiatives resulted in the PI System being the central hub for
providing data not only to users but to enterprise systems as well. • Increase in AF Models • Increase in Tag maintenance
This shift without adequate change in processes caused significant data quality issues.
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Business Challenge
Connecting groups and changing processes
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New Process
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Solution
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Reduce - Reuse
Types of Data
• Well Header
• Tank
• Meter
• Equipment
• Financial (Cost Centers)
EQMWell and Equipment Maintenance
Processes Name Generator Assign IP Assign Meter ID Exception Generator Intelligent Defaults
UserInput
ProCount
SAP PM
OPC Server
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Solution
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Before
UserInput
ProCount
SAP PM
OPC Server
UserInput
UserInput
UserInput
UserInput
UserInput
Well Header Data
Financial Data
Meter DataWell DataTank Data
Equipment Data
Meter Data
Meter DataWell DataTank Data
Equipment Data
Meter DataWell DataTank Data
Equipment Data
Meter DataRTU Data
EQMWell and Equipment Maintenance
Processes Name Generator Assign IP Assign Meter ID Exception Generator Intelligent Defaults
Equipment Data
UserInput
ProCount
SAP PM
OPC Server
After
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PI System – Data HUB
LOWIStm
ProCount
Users
ProCount• Using PI to push data to Enterprise systems
• Use of Integrators to push data to HANA
• Connecting PI to more systems to aggregate
data for users
• Custom AF SDK solutions
• PI Integrator for SAP HANA
• Spotfire Reports
• PI Vision Dashboards
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PI System Solution
PI Components
• PI AF SDK
• PI Templates
• PI Analytics
• PI Notifications
• PI Vision
• Event Frames
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PI Archive
EQMWell and Equipment Maintenance
PI AFProCount
Model
PI AFOperations
Model
PI AFHANA Model
UserInput
Fully Automate the maintenance of PI Tags and AF Models
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AF Template + AF SDK = PI Asset Creation
AF Template
AF Attribute
Configuration - Tag Creation Config PI Assets Created
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PI Analytics + PI Notifications = Action
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Use PI AF Analytics and Notifications to check and alert on data consistency
• Data Quality Reports check critical data points across systems
• KPIs measure performance
• Notify groups on lower priority issues
• Generate trackable incidents on higher priority issues
ProCount
Email Alerts
Service Now
Incident
PI Vision Data
Quality Reports
Data QualityAnalysis
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PI Vision – Data Quality Dashboard
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Focus Changed from Maintenance to Data Quality
PI Vision – KPI Dashboard
Cross System Data Exception Report
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DEMO
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EQM
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EQM Dashboard
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EQM New Request
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EQM New Request
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EQM New Request
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EQM Data Entry
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EQM – Error Checking
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EQM Results
200+
5 Applications
Integrated
4066 Pieces of Equipment
Created Across the 5 Applications
152,932 Data Points Created Across the 5 Applications
200+ ~90% Manual Data Entry
Reduction
Impact
= Higher Efficiency
Increased Data Quality
Improved Data Consistency
>20% Reduction in Data
Quality issues
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RESULTS CHALLENGE SOLUTION
Marathon Oil Company
Increase process efficiency, improve data quality and reduce time for system updates
EQM increased efficiency and data quality by reducing data entry and improved the speed by which systems were updated by reusing data
Time for all systems to be updated was reduced from days to hours while data quality improved due to reduction in manual data entry and the addition of cross system analysis
• PI Templates were used to maintain consistency in Tag creation
• PI AF SDK allowed the complete automation of multiple AF models
• PI Analysis provided the ability to perform Data Quality checks across systems
• 5 Systems integrated
• Overall manual data entry reduced
by 90%
• >20% Reduction in Data Quality
Issues
• Poor data quality
• Inconsistent timing of data entry across systems
• No Data quality checking
Improving Data Quality using PI AF and EQM
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Contact Information
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• Scott Williams
• Consulting SCADA Specialist
• Marathon Oil Company
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Questions?
Please wait for
the microphone
State your
name & company
Please remember
DOWNLOAD THE MOBILE APP
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