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Oil Supply Chain Planning under Uncertainty and Risk Evaluation

Oil Supply Chain Planning under Uncertainty and Risk Evaluation. Marcelo Maia F. de Oliveira. October 15th, 2014. Introduction and Problem Definition. Wide variety of comercial, industrial and logistics operations occur over the midstream segment.

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Oil Supply Chain Planning under Uncertainty and Risk Evaluation

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  1. OilSupply Chain Planning underUncertaintyandRiskEvaluation Marcelo Maia F. de Oliveira October 15th, 2014

  2. IntroductionandProblemDefinition Widevarietyof comercial, industrial andlogisticsoperationsoccur over themidstreamsegment Strong dependenceamongtheoperationsand some gainscanonlybeestimatedbyconsideringthewholesupplychain Planning thissegmentis crucial toachievethesuccessofalloperations.

  3. 2. OilSupply Chain in Brazil • CurdeOil: • Imports (1) • Exports (3) • Production (5) • OilProducts: • Imports (2) • Exports (4) • Market Selling (6) • ~ 200 differentcrudeoils • ~ 50 differentoilproducts • 10 basinsofoilproduction • 43 terminals (marine andinsidethe country) • 12 refineries 3 4 2 5 6 1

  4. 3. RefineryOperations There are twomainoperationsexecuted in therefineries: ProcessUnits Blending Unitstypes: PhysicalSeparation – Disttilation (firststepofoilprocessing) Conversion – CrackingandDelayedCoker (residual oiland heavy fractionstogasolineand diesel) Treating – HDT Nafand HDT Diesel (remove impurities – sulfur – andspecifyotherqualities) Blendintypes: 1 – Oilblendingaimingtogeneratedintermediateswithdesiredyieldsandqualities; 2– Intermediateblendingtospecify charge ofprocessunities; 3 – Intermediateblendingtoproduce final productswithspecifiedqualities.

  5. Solutionwellposicioned in respectwiththepossiblerealizationsofuncertaintyω. • Secondstagedecisions in a posicionto explore advantageablevaluesofuncertaintyω. 4. BibliographicReview: StochasticProgramming The two-stagestochasticproblem (Higle, 2005) canbedescribed as presentedbytheequations: e x ≥ 0 e y ≥ 0 & Where

  6. 4. BibliographicReview: ScenarioGenerationMethods MomentMatching– StatiscalMethodproposedbyHoyland e Wallace (2001) whichapplies a non-linear modeltogenerate a limitednumberofdicretescenariosthatsatisfyspecificstatisticproperties. • Time Agregation– samplingmethodthatuses Markov Chain concepts (ErgodicChains, Return Time andMeanPath Lenght) andthe Time AgregationmethodofCaoet al., 2002.

  7. 4. BibliographicReview: Cvar - RiskMeasure The CVaRof a probabilitydistributionwithconfidencelevel α: MeanvalueofthosescenarioswhichhavetheprofitslowerthanVaRandwhosecumulativeprobabilitysumsup to 1- α (Pineda e Conejo, 2009)

  8. 5. Uncertainties ofOilSupply Chain in Brazil OilProduction Brent Quotation OilProductsNational Market

  9. 6. MathematicalModelandProblem Approach SecondStage FirstStage Oilimportandexport Month 1 Month 2 Decisions: Oilallocation, Spot marketoilimportandexport, unitutilization, oilproductsimportand export.

  10. 6. MathematicalModelandProblem Approach FirstStageConstraints: • Global Oil Balance (import+ production = export + allocated); • OilImportandExportLimits; SecondStageConstraints – scenariodependent: • Global Oil Balance

  11. 6. MathematicalModelandProblemApproach – Recourseactions Spot Market Export Allocation in refineries ProductionSurplus PredictedProduction – ActualProduction ExportCancellation AllocationCancellation ProductionDeficit Besidestheseoptions, it’salsopossibletoimportoil in the Spot Market. SecondStage Global Oil Balance:

  12. 6. MathematicalModelandProblem Approach FirstStageConstraints: • Global Oil Balance (import+ production = export + allocated); • OilImportandExportLimits; SecondStageConstraints – scenariodependent: • Global OilBalance • OilProductBalance (import+ production = export + processed); • OilandProduct Balance onTerminals; • Products Market Selling; • Flowofoilandproductlimitedtothedeciosionsofimportation, exportation, productionandselling.

  13. 6. MathematicalModelandProblem Approach SecondStageConstraints – scenariodependent- RefineryOperations: • Intermediates balance (producedbyprocessunit= productblending + charge blending); • Charge Balance (charge blending = consumedbyprocessunits); • OilProductsQualitySpecification; • Charge QualitySpecification; • Process Unit CapacityLimits; • StorageLimits; • OilProduct Balance (producedbyblending + initialstorage + received = delivered + final storage). Intermediatequalitiesindexedtoavoid non-linearity

  14. 6. MathematicalModelandProblem Approach ObjectiveFunction:

  15. 7. ModelParameters: Products CrudeOilList OilProductList

  16. 7. ModelParameters: Refineries

  17. 7. ModelParameters: OilLogisticsOperations IM R1 MN T1 R2 R3 R4 PN Nodes: International Market (IM), NationalProduction (PN), Terminal (Tn) andRefineries (4 Rn) 7 arcs: 1 for Import, 1 for Export, 1 for NationalProduction e 4 for OilSupplytoRefineries

  18. 7. ModelParameters: OilProductsLogisticsOperations IM R1 MN T1 R2 R3 R4 PN Nodes: InternationalMarket (IM), Terminal (Tn), Refineries (4 Rn) e NationalMarket (MI) 14 arcs: 1 for Import, 1 for Export, 16 for flowsbetweenrefineriesand terminal e 4 for marketdelivery

  19. 7. ModelParameters: Refineries

  20. 7. How does theuncertaintyaffectthescenarioprofit? Linear influenceofOilProduction – thehighertheproduction, more oil is exported. SmootherinfluenceofQuotation, as bothimportcostsandexportrevenue varie. Thehigherthedemand, thebiggerthesupplycost, as internalprices are lowerthaninternationalones.

  21. 8. Results – ScenarioGenerationMethods CumulativeProbability Curve for oilproductionuncertainty MomentMatchingMethod Time AgregationMethod MomentMatchingMethodhighconcentrationofProbabilityin narrow range ofthepossiblevalues. Time AgregationMethod uniformityand curves overlapped.

  22. 8. Results – ScenarioGenerationParameterSelection 1- Gainonobjectivefunctionstability as thescenariotreegrows, mainly for theMomentMatchingMethod.

  23. 8. Results – ObjectiveFunctionandScenarioProfit(thousandu.m.) MomentMatching Time Agregation

  24. 8. Results – Revenueandcost MomentMatching Time Agregation 1- Similartityofeachcostandrevenue quotas 2- Time AgregationMethod: higherproductexchange– highercostandrevenue

  25. 8. Results – FirstStageVariables MomentMatching Time Agregation Import(thousandm³) Import(thousandm³) Export(thousandm³) Export(thousandm³) 1- Highresemblance in firststageindications: 2- OilA1 Import(lowpriceandgoodyield) untilthelimit; 3- OilD2 e E2 Exportuntilthelimitrespectingthesecondstageuncertainties.

  26. 8. Results – SecondStageRecourseActions MomentMatching Agregação temporal 1- Strongindicationofallocationadjustment as oilproductiondeficithappens. 2- Theoilexportdecision is generallykeptonthesecondstage.

  27. 8. Results – Disttilationutilization MomentMatching Time Agregation 1 – Maximumutilizationofdisttilationon R3 and R4 - robustindication; 2 – Maximumutilizationofdistillationon R2 doesn’tseem to beinterestingonthesecondmonth; 3 – Lowerutilizationon R1 for solutionsusing Time AgregationMethod.

  28. 8. Results – ProductQualities

  29. 8. Results – RiskMeasureImpact(CVaR) MomentMatching Time Agregation 1- CleardifferenceoftheriskmeasureontheObjectiveFunction for eachgenerationmethod; 2- Scenariotreesbuiltbythemomentmatchingmethod are lesssusceptible to riskandthe Objetive Function varies onlywhenchangingtheriskaversion (β).

  30. 8. Results – RiskMeasureImpactonScenarioProfit MomentMatching Replication 2 Time Agregation Replication 2 1- ↑ RiskAversion (β) e ↑ ConfidenceLevel (α) : toavoidlowvaluesofprofits, the curve moves totheleft, sohigherprofits are alsoavoided. 2- ConfidenceLevel (α) effectonthesolutionsobtainedby Time AgregationMethod: desirablefor decisiontaking in anuncertainenvironment .

  31. 9. Conclusions • The instancebuilt for thisproblem, eventhoughsimplified, reflectthecomplexityofoilsupplychainplanning; • The resultsobtained are suitable for real operationsandgive some important insightsofhowtodealwiththeuncertainfactorsconsidered; • Compairingtheresultsobtainedbybothscenariogenerationmethodsallowstoidentifyqualitiesanddisadvantagesofeachone; • It wasclearthattheTime AgregationMethodhas a more desirablebehaviourin functionofriskmeasuresparameters; • The problemherepresentcouldbecloser do real operations, consideringtheintegernatureofoilandproductimportandexportoperationsorbytakingintoaccountothersourcesofuncertainty, such as assetavailability; • Othermathematicaltechniquescouldbeapplied, for exampleLagragianDecomposition, whichwould drive to a smallercomputationaleffort.

  32. Contact marcellomaia@gmail.com maiamarcelo@petrobras.com.br

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