solutions for downstream problemsmath.bme.hu/~diffe/szeminarium/opt_2013_02/kelemen...mathematics in...
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Mathematics in oil industry Solutions for downstream problems Béla Kelemen MOL Group SCM VP Tamás Kenesei MOL Group SCM Modelling&Support Advisor
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MOL Group - Integrated company
Refinery Petchem unit
Up
stre
am
Do
wn
stre
am
Gas
Mid
stre
am
REGION EBITDA 2012 DATA 2012
► 647 MMboe SPE 2P reserves ► Over 100% organic reserve replacement ratio in 3 years average
► 115mboepd production* ► Production in 7, exploration in 11 countries
► 5 refineries, 470 thbpd
► 19 Mtpa sales
► 1.700+ filling stations
► 2 petrochemical plants
Previous pipeline developments
MMBF UGS
► Gas Storage capacity: 1.9 bcm
► Gas Transmission:
5.560 km pipeline in
Hungary
* Excluding Syria
Total Revenue 2012:
24.6 USD bn
Market Capitalization:
7.5 USD bn
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MOL group snapshot
Danube Refinery 162 kbbl/d
10,6 NCI
Crude pipeline 848 km
26 Depots 19 own, 7 rented
Product pipeline 1684 km
5038 RTCs
Bratislava Refinery 122 kbbl/d
11,5 NCI
Rijeka Refinery 90 kbbl/d
9,1 NCI
Mantova Refinery 52 kbbl/d
8,4 NCI
Sisak Refinery 44 kbbl/d
6,1 NCI
TVK 1,4 Mt/y
SPC 0,7 Mt/y
Road tank cars 152
More than 1700 stations
Wholesale activity in 13 countries
1991
2007
2009
2005
2003
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CEE Arena
Rijeka Mantua
Danube
Bratislava
Sisak
Group refinery
Competing refinery
Swechat
Litvinov Kralupy Ingolstadt
Bayernoil
Gdansk
Plock
Burghausen
Bosanski Brod
Novi Sad Pancevo
Porto Marghera
S.M. Trecate
Sannazaro
Cremona
Busalla
Arpechim
Onesti
Petrotel
Petrobrazi
Rompetrol
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The size matters……
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Petroleum supply chain
Refining
Crude Supply
Primary Distribution
Product Depots
Secondary Distribution Market
Crude Depots
►Huge number of data is available at every step
►What is relevant?
►What is important?
►How to get it?
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Enterprise solutions information flow
Co
mm
on
Data
& V
isu
alisati
on
Schedule Adherence
Plan vs. Actual
Operating Envelope
Operator Efficiency
Co
mm
on
Lim
its &
Bo
un
darie
s
Planning
Scheduling
Business Results
Controls
Procedures
Production
Business
Operating Instructions Operations Monitoring Operations
Procedure Analysis Procedure Execution
Execution Decisions Reviews, Reports
weeks ago days ago hours ago now hours ahead days ahead months ahead
* This slide is based on Paul Brice (Honeywell) Holistic Approach presentation,
Process Control
Yield Accounting
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Model Building - challenges
Special tasks require special models
PLANNING,
OPTIMIZATION
SCHEDULING
UNIT
OPTIMISATION
PROCESS
CONTROL
REPORTING
corporate
level
plant
level
unit
level
process
level
orders
setpoints
performance
data
►This our scope, the ‚virtual’ world
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How far do we look?
►week ►Year
►Optimization space
►Uncertainty
Periods Inventory Data
More vs Less Connecting points Realibility
Curse of complexity How to map physical possibilities
Consitency
Business
Plan Forecasts Rolling Plan
► Quarterly practice
► Overlook till the end of the actual year for 7 sites
► Group performance evaluation
►Monthly practice
► 4 months 7 sites plan
► Feedstock selection
► Plant optimization
► Transfer optimization
► Product blending
► Inventory management
►Market allocation
► Basis of real serious business decisions
► Yearly practice
► 12 months 7 sites plan
► Feedstock selection
► Annual budget planning
► Target setting for 3 years
Long Term
Plan
► From 3 to 10 years outlook
► Strategic investment planning
► Supply/demand balance
Rolling Plan
weekly update
► Support execution
► 1 months 7 sites plan
► Fixed closing inventory
► Compared to base RP calculation
How far do we look?
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What is the objective function?
►Profit maximum ►Energy minimum
►Wide range in product yield
►Wide sales demand range
►Wide crude slate
►Sophisticated energy modell is needed
►Product yields
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Basic financial driver of refinery
GPW (Gross Product Worth) is the
value of products obtained from the
particular crude processed in your
refinery
* Fix and variable costs, losses not included 12
13
Basic financial driver of refinery
Urals 108 USD/bbl
1 tons 788 USD
Crude
Mogas 22% 209
Naphtha 8% 75
Diesel 38% 378
0.1 gasoil 2% 19
HFO 19% 111
Kerosene 2% 20
* Fix and variable costs, losses not included
GPW
Crude price: 788 USD/t GPW: 812 USD/t
Refinery Margin: 24 USD/t
Mogas Kerosene Naphtha Diesel 0.1 gasoil HFO
USD/t 948 1010 938 994 935 584
13
812
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Shutdown in diesel desulphurisation plant
Urals 108 USD/bbl
1 tons 788 USD
Crude
Mogas 22% 209
Naphtha 8% 75
Diesel 30% 298
0.1 gasoil 10% 93,5
HFO 19% 111
Kerosene 2% 20
* Fix and variable costs, losses not included
GPW
Crude price: 788 USD/t GPW: 806 USD/t
Refinery Margin: 18 USD/t
Mogas Kerosene Naphtha Diesel 0.1 gasoil HFO
USD/t 948 1010 938 994 935 584
14
806
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No demand on mogas market – sales droop
Urals 108 USD/bbl
1 tons 788 USD
Crude
Mogas 17% 161
Naphtha 8% 75
Diesel 30% 378
0.1 gasoil 10% 19
HFO 19% 111
Kerosene 2% 20
* Fix and variable costs, losses not included
GPW
Crude price: 788 USD/t GPW: 764 USD/t
Refinery Margin: -24 USD/t
Mogas Kerosene Naphtha Diesel 0.1 gasoil HFO
USD/t 948 1010 938 994 935 584
15
764
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Succesful mogas sales, but lower price
Urals 108 USD/bbl
1 tons 788 USD
Crude
Mogas 17% 161
Naphtha 8% 75
Diesel 38% 378
0.1 gasoil 2% 19
HFO 19% 111
Kerosene 2% 20
* Fix and variable costs, losses not included
GPW
Crude price: 788 USD/t GPW: 809 USD/t
Refinery Margin: 21 USD/t
Mogas Kerosene Naphtha Diesel 0.1 gasoil HFO
USD/t 948 1010 938 994 935 584
900
16
809
Mogas 5% 45
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Decision points in the refinery
How to operate the
units?
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Decision points in the refinery
Inland
refinery Multiplant
Seaside
Refinery
►Stable product price (same price of every tons)
►Different crudes are available
►Number of crude is limited
►product prices change by increasing logistic cost
►One market point supplied from more Refinery
►Different Refinery gate prices
Determine the last tons crude which can be processed with profit
Must be focused on
Logistic system
Accuracy of processing
Logistic cost
Harmonization of refineries
Crude
selection
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Decision points on the market
►Harmonization among different product lines
►Represented transform possibilities between different product lines
►Wide sales range
►Harmonized prices (based on same crude price)
►Price prediction
-300
-200
-100
0
100
200
300
400
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11
.01
.04
20
11
.02
.10
20
11
.03
.21
20
11
.05
.03
20
11
.06
.10
20
11
.07
.19
20
11
.08
.25
20
11
.10
.04
20
11
.11
.10
20
11
.12
.19
20
12
.01
.30
20
12
.03
.07
20
12
.04
.17
20
12
.05
.25
20
12
.07
.05
20
12
.08
.13
20
12
.09
.20
20
12
.10
.29
20
12
.12
.05
20
13
.01
.16
20
13
.02
.22
20
13
.04
.04
20
13
.05
.14
20
13
.06
.21
20
13
.07
.30
PREM UNL 10PPM FOB ROTT DIESEL 10PPM FOB ROTT
FUEL OIL 1.0 PCT FOB ROTT
How to handle
uncertainty
Vágási pontok
Pannon Egyetem
How deeply detailed is a good model?
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Dilemmas for model building...
Solvability
Maintenance
Complexity
Reliability
1 100 1
100
1
► Fast
► Complex
► Reliable
► Maintainable
► Rewarding
► Specifiable
► Measurable
Trade-offs to decide Target setting Issues and problems
Market size
Model size
Business Process
Data consistency
Workflow
Calculation Time
End user’s needs
Modelling directives
Should be…
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From reality to a good modell
Crude assay
expert
Technologist
Modeller
Scheduler Operational
expert
Blending
expert
Who is needed to put it together? Harmonization/standardization
► Set the model granularity
► Set the real flexibilities
► Harmonization of sites
► Group level transfers
► Joint portfolio
► Cross country supplies
"as the complexity of a system increases our ability to make precise and yet significant statements about its behavior diminishes until a threshold is reached beyond which precision and significance (or relevance) become almost mutually exclusive characteristics.„
L. A. Zadeh 1973
Input data (buy, sell, caps, pinv, market structure)
Model Core (refineries)
Demand driven
Structural issues
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Translating reality into the language of Math
Actual data ORION data
PIMS data
ORION data:
Operative Rolling Plan
PIMS Data:
Actual data:
► Monthly average data
► Annual Refreshed by PE
► Can be Fine tuned temporarily before calculation
► Feasibility check of RP calculation
► Calculated with real yields
► PIMS result checked by ORION data (can be modified!)
► Solution between PIMS and ORION calculation
► Must be executed
► Fine tuned every month
► Can be modified during month
► More operational mode
► Measured every day
► Continuously controlled
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How does PIMS work?
VALIDATION &
MATRIX GENERATION
DATA
MANAGER MENUS
SOLUTION
REPORTS RECURSION OPTIMIZATION
PIMS LOTUS/EXCEL
Non-Converged
PIMS
Xpress.mp PIMS PIMS
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Conclusions
Math.. Business processes
To deliver such a modell which can be a bridge between science and business. Main aim is the decision support and the tools
should serve this aim.
Backup
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SCM Philosophy and Organizational model
Production Downstream Supply and
Sales Retail
Downstream Development
Supply Chain Management
Downstream Downstream
support
Planning & Optimization
Distribution & Scheduling ►How to integrate vertical structures
(and mentalities) to achieve truly lateral SCM behavior to maximize results?
Planning Modelling Performance monitoring
Supply chain Integration
Product Line
Process
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The magic triangle - from idea to execution
People
Process
Tools
Process ► Horizontal processes, vertical organization
► Planning – Distribution & Scheduling - Execution
Tools ► Technology should serve people – not the other
way around!
► Aspen is the backbone
People ►Well informed , highly knowledgeable and
experienced
► Training professional knowledge, attitude & behavior
Three pillars must be in balance while customers are in focus
3 pillars