Quantitative Methods for Strategic and Investment Planning in the Oil-Refining

Transcription

1 Quantitative Methods for Strategic and Investment Planning in the Oil-Refining Brenno C. Menezes, Lincoln F. Moro Refining Op*miza*on PETROBRAS Petroleo S.A. Rio de Janeiro, RJ Ignacio E. Grossmann Department of Chemical Engineering Carnegie Mellon University PiFsburgh, PA industrialgorithms Fernando Pellegrini, Ricardo Medronho Department of Chemical Engineering Federal University of Rio de Janeiro Rio de Janeiro, RJ Jeffrey D. Kelly IndustrIALgorithms Toronto, ON March 13 th,

2 Goal: develop quantitative methods to predict structural modifications in refining and logistics assets through time What, Where, When to Invest? Atmospheric Dis*lla*on Delayed Coker Terminal/Pipeline PETROBRAS Current Tool for Strategic Planning (PLANINV) LP No Scenario Synthesis Quan*ta*ve Methods Only op*mize streams transfers (fuel and petroleum import/export, market supply) PLANINV Scenario Opt (MILP) + NLP Processing Blending 2

3 The need to develop strategic level supply chain planning models in order to address issues in a quan*ta*ve manner rather than the qualita*ve approaches used *ll now is acknowledged by the industry and s*ll remains an ac*ve area of research. (Shapiro, 2004; Papageorgiou, 2009) Current Investment Planning Methodology in PETROBRAS Strategy - Increase the supply by one refinery Max NPV NPV=Sales- Cost Invest. Costs: - *QF+ *Y NPV Net Present Value Refinery Opera*onal Planning - Simulate Find the the Best Refining Scenarios Scenarios (MILP) Supply Chain Investments Planning - Test the refinery best scenarios in the home- grown NLP in Blending global investments and Processing tool (LP) Financial Strategy - Find the best set of compe*ng investments regarding the total capital flow Strategy Decision: - Invest or not in the supply increase MINLP MILP + NLP 3

4 Refining Modeling Q u,s,u Q u,s,u Mixer QF u PF u,p Unit QS u,s PS u,s,p QS u,s PS u,s,p SpliFer SpliFer Q u,s,u Q u,s,u Q u,s,u Q u,s,u QF u PF u,p QS u,s PS u,s,p Feed Flow Feed Property Product Flow Product Property Q u,s,u Q u,s,u Q u,s,u Transfer Stream Flow Mixer: QS u,s PS u,s,p SpliFer Q u,s,u Q u,s,u u units (tanks, pipelines) s streams p proper*es Feed Proper*es: Unit Products Proper*es: Unit Products: SpliFer: (Moro, Zanin & Pinto, 1998) 4

5 Blending Equa*ons Property Group PF or P base Property Name PF or IPF (Property Index) ACID Mass Acidity Concentra*on GRAV Vol Gravity SULF Mass Sulfur Content CCR Mass Conradson Carbon Residue DIST Vol Dis*lla*on Vola*lity RVP Vol (IP) Reid Vapor Pressure FLASH Vol (IP) Flash Point MON Formula Motor Octane Number Combus*on RON Formula Research Octane Number CETAN Formula Cetane Number Stability GUM Vol Gum VISC Vol (IP) Viscosity POUR Vol (IP) Pour Point Fluidity GRAV, SULF, RVP, T10, T50, T85, T90, T95, AROM, GUM, OLEF, FLASH, CETAN, ANI, VISCO, POUR, CLOUD CLOUD, Vol (IP) PPFC, Cloud RCR, Point ACID, RON, MON PPFC Vol (IP) Plug- Flow Filter 5

6 Crude Choice and Processing Equa*ons Crude Assay: Proposed Swing- Cut Yields Gravity Acidity All Bilinear Terms (Moro, Zanin & Pinto, 1998) 6

7 Gravity QSWL=Light Swing-Cut Flow QSWH=Heavy Swing-Cut Flow Swing- Cuts Swing- Cuts (Improved) N FN=0.719 FN=0.717 N Q GRAV GRAV SW1L K N SW1H SW1L N K SW1H SW1H P=Property K SW2L K FK=0.800 SW1L Q=Flow FK= LN SW1L SW1H K SW2L SW2H LD SW3L SW3H HD 7

8 Mixed- Integer Problem (R,U,N,T) QC INVEST R=Refinery U=Unit Type N=Number T1 of the T2 Unit Type T=Time 3 years 4 years T3 5 years (R,U,N,T) QF QC Crude Processing Take an investment decision (binary) Count with the addi*onal produc*on R=Refinery U=Unit type N=Number of an unit type T=Time Blending SW ON Project execu*on 8

9 Type Unit REVAP Name CDU Crude Dis*lla*on Unit DIST VDU Vacuum Dis*lla*on Unit ATR Cracking RFCC Residue FCC FCC Fluid Cataly*c Cracking GO Cracking MHC Medium Hydrocracking HCC Hydrocracking PDA Propane Desasphal*ng VR Cracking DC Delayed Coker TC Thermal Coking LCNHT Light Cracked Naphtha HT CLNHT Coked Light Naphtha HT HT KHT Kerosene HT DHT Diesel HT REF Reforming ON ISO Isomeriza*on ALK Alkyla*on Existent CDU.1 VDU.1 PDA.1 FCC.1 DC.1 LCNHT.1 CLNHT.1 KHT.(1,2) DHT.(1,2) REF.1 12 units Expansions CDU.1 VDU.1 PDA.1 FCC.1 DC.1 LCNHT.1 CLNHT.1 KHT.(1,2) DHT.(1,2) REF.1 12 binaries binaries per *me period U=16 types N=3 per type Installa*ons CDU.(2,3) VDU.(2,3) PDA.(2,3) FCC.(2,3) DC.(2,3) LCNHT.(2,3) CLNHT.(2,3) KHT.3 DHT.3 REF.(2,3) RFCC.(1,2,3) RFCC.1 MHC.(1,2,3) MHC.1 HCC.(1,2,3) HCC.1 TC.(1,2,3) TC.1 ISO.(1,2,3) ISO.1 ALK.(1,2,3) ALK binaries 9

10 Capital Cost Constraint Logic Constraints for Expansions and Installa*ons 10

11 Logic Constraints for Unit Type Investments Unit Groups (U G ): ATRC = Atmospheric Residue Cracking GOC = Gasoil Cracking VRC = Vacuum Residue Cracking ON= Octane Number Booster These types are known as: sequence- dependency or structural precedence constraints 11

12 REPLAN U.N.T REVAP U.N.T RPBC U.N.T RECAP U.N.T CDU.(1,2) VDU.(1,2) FCC.(1,2) DC.(1,2) LCNHT.(1,2) CLNHT.(1,2) DHT.(1,2) REF.1 CDU.1.1 FCC.1.1 CDU.3.2 RFCC.1.1 KHT.1.2 DHT.3.1 LCNHT.3.1 CDU.1 VDU.1 FCC.1 PDA.1 DC.1 LCNHT.1 CLNHT.1 KHT.(1,2) DHT.(1,2) REF.1 KHT.1.2 CDU.(1,2,3) VDU.(1,2) FCC.1 DC.(1,2) LCNHT.(1,2) CLNHT.1 DHT.(1,2) REF.1 ALK.1 CDU.1.2 FCC.1.1 LCNHT.1.1 DHT.2.2 RFCC.1.2 LCNHT.2.2 CDU.1 RFCC.1 LCNHT.1 DHT.1 CDU.1.2 RFCC.1.2 LCNHT.1.1 (10 expans/7 install) (R,U,N,T) R.U.(N).T Refinery Unit Type Number of the Unit type Time 15 units 2 expans 5 install EWO Intel Mee(ng i7-3820qm March 2.7GHz GB 12 units bi U$ T1 T2 T3 NPV years Incom Expans Install Capital expans GAMS (Solver DICOPT) ; CPU(s): 1754 (29 min) 15 units 4 expans 2 install 4 units 3 expans Discrete Var. Eq. Var. Non- zero Non- Linear ,039 20,592 98,863 58, k now m 3 /d Kerosene 188k 12 y GLN 37k 63k JFUEL 9k 27k DSL 35k 45k C3 +C3= 12

13 Next Steps Structural, temporal and phenomenological decomposi*on strategies Deal with opera*onal modes to include investments in logis*cs Opera*onal Purchasing & Procurement Crude Strategic Tac*cal Processing Distribu*on Marke*ng & Sales Refinery Terminals Fuels Thank You Ques*ons? 13

14 References Moro, L.F.L., Zanin, A.C. e Pinto, J.M. (1998). A planning model for refinery diesel produc(on. Computers and Chemical Engineering. 22 (1): Papageorgiou, L. G. (2009). Supply Chain op(miza(on for the process industries: Advances and opportuni(es. Computers and Chemical Engineering, 33, Sahinidis, N.V., Grossmann, I.E., Fornari, R.E., Chathrathi, M. (1989). Op(miza(on model for long range planning in the chemical industry, Computers & Chemical Engineering, 13, Shapiro, J. F. (2004). Challenges of strategic supply chain planning and modeling. Computers & Chemical Engineering, 28,

15 Current Methodology Proposed Methodology Tac*cal X Opera*onal Planning Throughputs/Units Capacity Crude Diet/ Recipe 80% Transfers 25,526 Gross Margin K$/ 27.3 CPU (s) 70% 25,527 day 28.2 Tac*cal (All) Variable 60% Fixed 25,678 25, REPLAN 40% 25,685 11, REVAP 30% 25,671 Infeasible (7,670) Fixed Fixed RPBC 20% 25,678 4, RECAP 10% 25,677 2, Opera*onal (All) Ini*al Point Gross Margin (K$/day) CPU (s) 100% 25,672 Freed the Ultra 27.4 Low 90% 25,677 Sulfur Diesel 24.9 Demand 50% 25, % 25,678 25, RPBC 1% - Changed 25, Fixed the 0.1% Variable Crude Recipe 25,686 25, Variable Variable 25,

16 T1 QC=EXCAP INVEST QF QC REFPLAN Crude Processing Blending T2 QC t =QC t- 1 +QNEW t- 1 INVEST QF QC REFPLAN Crude Processing Blending SW ON SW ON 16

17 Which NL Surface? Blending variable recipes fixed recipes It is iden*cal to SLP (Zhang et. al., 1985) except that at each major Binary variables itera*on of the nonlinear fixed yields program a QP is called instead of the LP if there are quadra*c Swing- cuts terms (fixed in the proper*es) objec*ve func*on which is found in industrial planning, scheduling, control & reconcilia*on problems. Refining Processes IMPRESS- SLPQPE Sequen*al Linear Programming and Quadra*c Programming 17 Engine

18 Decision- Making Types in PETROBRAS Planning Strategic Tac*cal Opera*onal Scheduling Offline Online (< 1h) Opera*onal RTO Variable: Crude Groups Recipes Fixed: Variable: Crude Recipes Fixed: Fuels Recipes Integra*on Flows Variable: Fuels Recipes Fixed: Purchasing & Procurement Crude Binary Variables: Refining & Logis*cs Scenario Fuels Recipes No Integra*on Crude Recipes Integra*on Flows Strategic Model: LP x NLP MILP x MINLP Period: single x mul* Recipes: fixed x variable Variables: Integra*on Units Opera*on Tac*cal Processing Distribu*on Marke*ng & Sales Refinery Terminals Fuels 18

19 Decision- Making Types in PETROBRAS Planning Strategic Binary Variables: Refining & Logis*cs Scenario Supply Chain Investments: - New Units or Revamps Con*nuous Variables: Tac*cal Opera*onal Supply Chain Opera*on: - Pipeline Sequence Supply Chain Opera*on: - Units Opera*on Modes - Tanks Opera*on Modes Recipes Integra*on Units Opera*on Offline Online (< 1h) Scheduling RTO 19