Risk measurement of international oil and gas projects based on the Value at Risk method
|
摘要
International oil and gas projects feature high capital-intensity, high risks and contract diversity. Therefore, in order to help decision makers make more reasonable decisions under uncertainty, it is necessary to measure the risks of international oil and gas projects. For this purpose, this paper constructs a probabilistic model that is based on the traditional economic evaluation model, and introduces value at risk(VaR) which is a valuable risk measure tool in finance, and applies Va R to measure the risks of royalty contracts, production share contracts and service contracts of an international oil and gas project. Besides, this paper compares the influences of different risk factors on the net present value(NPV) of the project by using the simulation results. The results indicate:(1) risks have great impacts on the project's NPV, therefore, if risks are overlooked, the decision may be wrong.(2) A simulation method is applied to simulate the stochastic distribution of risk factors in the probabilistic model. Therefore, the probability is related to the project's NPV, overcoming the inherent limitation of the traditional economic evaluation method.(3) VaR is a straightforward risk measure tool, and can be applied to evaluate the risks of international oil and gas projects. It is helpful for decision making.
International oil and gas projects feature high capital-intensity, high risks and contract diversity. Therefore, in order to help decision makers make more reasonable decisions under uncertainty, it is necessary to measure the risks of international oil and gas projects. For this purpose, this paper constructs a probabilistic model that is based on the traditional economic evaluation model, and introduces value at risk(VaR) which is a valuable risk measure tool in finance, and applies Va R to measure the risks of royalty contracts, production share contracts and service contracts of an international oil and gas project. Besides, this paper compares the influences of different risk factors on the net present value(NPV) of the project by using the simulation results. The results indicate:(1) risks have great impacts on the project's NPV, therefore, if risks are overlooked, the decision may be wrong.(2) A simulation method is applied to simulate the stochastic distribution of risk factors in the probabilistic model. Therefore, the probability is related to the project's NPV, overcoming the inherent limitation of the traditional economic evaluation method.(3) VaR is a straightforward risk measure tool, and can be applied to evaluate the risks of international oil and gas projects. It is helpful for decision making.
International oil and gas projects feature high capital-intensity, high risks and contract diversity. Therefore, in order to help decision makers make more reasonable decisions under uncertainty, it is necessary to measure the risks of international oil and gas projects. For this purpose, this paper constructs a probabilistic model that is based on the traditional economic evaluation model, and introduces value at risk(VaR) which is a valuable risk measure tool in finance, and applies Va R to measure the risks of royalty contracts, production share contracts and service contracts of an international oil and gas project. Besides, this paper compares the influences of different risk factors on the net present value(NPV) of the project by using the simulation results. The results indicate:(1) risks have great impacts on the project's NPV, therefore, if risks are overlooked, the decision may be wrong.(2) A simulation method is applied to simulate the stochastic distribution of risk factors in the probabilistic model. Therefore, the probability is related to the project's NPV, overcoming the inherent limitation of the traditional economic evaluation method.(3) VaR is a straightforward risk measure tool, and can be applied to evaluate the risks of international oil and gas projects. It is helpful for decision making.
引文
Adams A,Gibson C,Smith RG.Probabilistic well-time estimation revisited.SPE Drill Complet.2010;25(4):472-99.https://doi.org/10.2118/119287-PA.
Arps JJ.Analysis of decline curves.Trans AIME.1945;160(1):228-47.https://doi.org/10.2118/945228-G.
Dalton GJ,Alcorn R,Lewis T.A 10-year installation program for wave energy in Ireland:a case study sensitivity analysis on financial returns.Renew Energy.2012;40(1):80-9.https://doi.org/10.1016/j.renene.2011.09.025.
Dixit AK,Pindyck RS.Investment under uncertainty.Princeton:Princeton University Press;1994.
Dong Z,Holditch S,McVay D.Resource evaluation for shale gas reservoirs.SPE Econ Manag.2013;5(1):5-16.https://doi.org/10.2118/152066-pa.
Falconett I,Nagasaka K.Comparative analysis of support mechanisms for renewable energy technologies using probability distributions.Renew Energy.2010;35(6):1135-44.https://doi.org/10.1016/j.renene.2009.11.019.
Fetkovich MJ.Decline curve analysis using type curves.J Pet Technol.1980;32(6):1065-77.https://doi.org/10.2118/4629-PA.
Gass V,Strauss F,Schmidt J,Schmid E.Assessing the effect of wind power uncertainty on profitability.Renew Sustain Energy Rev.2011;15(6):2677-83.https://doi.org/10.1016/j.rser.2011.01.024.
Goel V,Grossmann IE.A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves.Comput Chem Eng.2004;28(8):1409-29.https://doi.org/10.1016/j.compchemeng.2003.10.005.
Hertz DB.Risk analysis in capital investment.Harv Bus Rev.1964;57(5):169-81.
Ho¨o¨k M,Aleklett K.A decline rate study of Norwegian oil production.Energy Policy.2008;36(11):4262-71.https://doi.org/10.1016/j.enpol.2008.07.039.
Hu XD,Shen HC.Basis for risk management.Nanjing:Southeast University Press;2001(in Chinese).
Jakobsson K,Bentley R,So¨derbergh B,Aleklett K.The end of cheap oil:Bottom-up economic and geologic modeling of aggregate oil production curves.Energy Policy.2012;41:860-70.https://doi.org/10.1016/j.enpol.2011.11.073.
Khadem MMRK,Piya S,Shamsuzzoha A.Quantitative risk management in gas injection project:a case study from Oman oil and gas industry.J Ind Eng Int.2017.https://doi.org/10.1007/s40092-017-0237-3.
Lin J,de Weck O,de Neufville R,Yue HK.Enhancing the value of offshore developments with flexible subsea tiebacks.J Pet Sci Eng.2013;102:73-83.https://doi.org/10.1016/j.petrol.2013.01.003.
Liu M,Wang Z,Zhao L,Pan Y,Xiao F.Production sharing contract:an analysis based on an oil price stochastic process.Pet Sci.2012;9(3):408-15.https://doi.org/10.1007/s12182-012-0225-6.
Mazeel MA.Petroleum fiscal systems and contracts.Hamburg:Diplomica Press;2010.
McIntosh J.Probabilistic modeling for well-construction performance management.J Pet Technol.2004.https://doi.org/10.2118/1104-0036-jpt.
Me′jean A,Hope C.Modelling the costs of non-conventional oil:a case study of Canadian bitumen.Energy Policy.2008;36(11):4205-16.https://doi.org/10.1016/j.enpol.2008.07.023.
Me′jean A,Hope C.Supplying synthetic crude oil from Canadian oil sands:a comparative study of the costs and CO2emissions of mining and in situ recovery.Energy Policy.2013;60:27-40.https://doi.org/10.1016/j.enpol.2013.05.003.
Mohamed S,Mc Cowan AK.Modelling project investment decisions under uncertainty using possibility theory.Int J Proj Manag.2001;19(4):231-41.https://doi.org/10.1016/S0263-7863(99)00077-0.
Montes GM,Martin EP,Bayo JA,Garcia JO.The applicability of computer simulation using Monte Carlo techniques in windfarm profitability analysis.Renew Sustain Energy Rev.2011;15(9):4746-55.https://doi.org/10.1016/j.rser.2011.07.078.
Osterloh WT,Mims DS,Meddaugh WS.Probabilistic forecasting and model validation for the first-eocene large-scale pilot Steamflood,Partitioned Zone,Saudi Arabia and Kuwait.SPE Reserv Eval Eng.2013.https://doi.org/10.2118/150580-pa.
Richardson J,Yu W.Calculation of estimated ultimate recovery and recovery factors of shale-gas wells using a probabilistic model of original gas in place.SPE Reserv Eval Eng.2018.https://doi.org/10.2118/189461-pa.
Rivera N,et al.Static and dynamic uncertainty management for probabilistic production forecast in Chuchupa Field,Colombia.SPE Reserv Eval Eng.2007.https://doi.org/10.2118/100526-pa.
Schwartz ES.The stochastic behavior of commodity prices:implications for valuation and hedging.J Finance.1997;52(3):923-73.https://doi.org/10.1111/j.1540-6261.1997.tb02721.x.
SQW.Economic study for ocean energy development in Ireland.2010.http://www.seai.ie/Renewables/Ocean_Energy/Ocean_Energy_Information_Research/Ocean_Energy_Publications/SQW_Eco nomics_Study.pdf.
Suslick SB,Schiozer DJ.Risk analysis applied to petroleum exploration and production:an overview.J Pet Sci Eng.2004;44(1-2):1-9.https://doi.org/10.1016/j.petrol.2004.02.001.
van der Poel R,Jansen JD.Probabilistic analysis of the value of a smart well for sequential production of a stacked reservoir.J Pet Sci Eng.2004;44(1-2):155-72.https://doi.org/10.1016/j.petrol.2004.02.012.
Wang Z,Li L.Valuation of the flexibility in decision-making for revamping installations-a case from fertilizer plants.Pet Sci.2010;7(3):428-34.https://doi.org/10.1007/s12182-010-0089-6.
Wang Q,Zhang BS.Risk analysis of overseas oil and gas exploration and development-taking the Central Asia as an example.J Tech Econ Manag.2012;01:23-36.https://doi.org/10.3969/j.issn.1004-292X.2012.01.005(in Chinese).
Wang Z,Zhao L,Liu M.Impacts of PSC elements on contracts economics under oil price uncertainty.In:International conference on E-business and E-government,Guangzhou China.2010.
Wang DJ,Li XS,Liu MM,Wang Z.A simulation analysis of international petroleum contracts based on the stochastic process of oil price.Acta Pet Sin.2012;33(3):513-8.https://doi.org/10.7623/syxb201203026(in Chinese).
Weijermars R,Sorek N,Sen D,Ayers WB.Eagle Ford Shale play economics:U.S.versus Mexico.J Nat Gas Sci Eng.2017;38:345-72.https://doi.org/10.1016/j.jngse.2016.12.009.
Welkenhuysen K,Rupert J,Compernolle T,Ramirez A,Swennen R,Piessens K.Considering economic and geological uncertainty in the simulation of realistic investment decisions for CO2-EORprojects in the North Sea.Appl Energy.2017;185:745-61.https://doi.org/10.1016/j.apenergy.2016.10.105.
Yan W,Cheng ZY,Li HD.Risk statistics and decision analysis.Beijing:Economy&Management Publishing House;1999(in Chinese).
Zhang BS,Wang Q,Wang YJ.Model of risk-benefit co-analysis of oversea oil and gas projects and its applications.Syst Eng Theory Pract.2012;32(02):246-56.https://doi.org/10.3969/j.issn.1000-6788.2012.02.003(in Chinese).
Zhao L,Feng LY.Establishment and application of evaluation and investment timing model for undeveloped oilfields.J China Univ Pet.2009;33(6):161-6.https://doi.org/10.3321/j.issn:1673-5005.2009.06.033(in Chinese).
Zhu L,Zhang Z,Fan Y.Overseas oil investment projects under uncertainty:how to make informed decisions?J Policy Model.2015;37(5):742-62.https://doi.org/10.1016/j.jpolmod.2015.08.001.
1The project is owned by a national petroleum company of China.The related information is provided by this company.
2The volatility of the OOIP is assumed to be low in this paper,therefore, its impact on the NPV is relatively weak, and the bubble Footnote 2 continued size of quadrant I is just a little larger than that of quadrant III. Once the volatility of OOIP is raised, OOIP’s impacts on the NPV will obviously be enhanced.
Arps JJ.Analysis of decline curves.Trans AIME.1945;160(1):228-47.https://doi.org/10.2118/945228-G.
Dalton GJ,Alcorn R,Lewis T.A 10-year installation program for wave energy in Ireland:a case study sensitivity analysis on financial returns.Renew Energy.2012;40(1):80-9.https://doi.org/10.1016/j.renene.2011.09.025.
Dixit AK,Pindyck RS.Investment under uncertainty.Princeton:Princeton University Press;1994.
Dong Z,Holditch S,McVay D.Resource evaluation for shale gas reservoirs.SPE Econ Manag.2013;5(1):5-16.https://doi.org/10.2118/152066-pa.
Falconett I,Nagasaka K.Comparative analysis of support mechanisms for renewable energy technologies using probability distributions.Renew Energy.2010;35(6):1135-44.https://doi.org/10.1016/j.renene.2009.11.019.
Fetkovich MJ.Decline curve analysis using type curves.J Pet Technol.1980;32(6):1065-77.https://doi.org/10.2118/4629-PA.
Gass V,Strauss F,Schmidt J,Schmid E.Assessing the effect of wind power uncertainty on profitability.Renew Sustain Energy Rev.2011;15(6):2677-83.https://doi.org/10.1016/j.rser.2011.01.024.
Goel V,Grossmann IE.A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves.Comput Chem Eng.2004;28(8):1409-29.https://doi.org/10.1016/j.compchemeng.2003.10.005.
Hertz DB.Risk analysis in capital investment.Harv Bus Rev.1964;57(5):169-81.
Ho¨o¨k M,Aleklett K.A decline rate study of Norwegian oil production.Energy Policy.2008;36(11):4262-71.https://doi.org/10.1016/j.enpol.2008.07.039.
Hu XD,Shen HC.Basis for risk management.Nanjing:Southeast University Press;2001(in Chinese).
Jakobsson K,Bentley R,So¨derbergh B,Aleklett K.The end of cheap oil:Bottom-up economic and geologic modeling of aggregate oil production curves.Energy Policy.2012;41:860-70.https://doi.org/10.1016/j.enpol.2011.11.073.
Khadem MMRK,Piya S,Shamsuzzoha A.Quantitative risk management in gas injection project:a case study from Oman oil and gas industry.J Ind Eng Int.2017.https://doi.org/10.1007/s40092-017-0237-3.
Lin J,de Weck O,de Neufville R,Yue HK.Enhancing the value of offshore developments with flexible subsea tiebacks.J Pet Sci Eng.2013;102:73-83.https://doi.org/10.1016/j.petrol.2013.01.003.
Liu M,Wang Z,Zhao L,Pan Y,Xiao F.Production sharing contract:an analysis based on an oil price stochastic process.Pet Sci.2012;9(3):408-15.https://doi.org/10.1007/s12182-012-0225-6.
Mazeel MA.Petroleum fiscal systems and contracts.Hamburg:Diplomica Press;2010.
McIntosh J.Probabilistic modeling for well-construction performance management.J Pet Technol.2004.https://doi.org/10.2118/1104-0036-jpt.
Me′jean A,Hope C.Modelling the costs of non-conventional oil:a case study of Canadian bitumen.Energy Policy.2008;36(11):4205-16.https://doi.org/10.1016/j.enpol.2008.07.023.
Me′jean A,Hope C.Supplying synthetic crude oil from Canadian oil sands:a comparative study of the costs and CO2emissions of mining and in situ recovery.Energy Policy.2013;60:27-40.https://doi.org/10.1016/j.enpol.2013.05.003.
Mohamed S,Mc Cowan AK.Modelling project investment decisions under uncertainty using possibility theory.Int J Proj Manag.2001;19(4):231-41.https://doi.org/10.1016/S0263-7863(99)00077-0.
Montes GM,Martin EP,Bayo JA,Garcia JO.The applicability of computer simulation using Monte Carlo techniques in windfarm profitability analysis.Renew Sustain Energy Rev.2011;15(9):4746-55.https://doi.org/10.1016/j.rser.2011.07.078.
Osterloh WT,Mims DS,Meddaugh WS.Probabilistic forecasting and model validation for the first-eocene large-scale pilot Steamflood,Partitioned Zone,Saudi Arabia and Kuwait.SPE Reserv Eval Eng.2013.https://doi.org/10.2118/150580-pa.
Richardson J,Yu W.Calculation of estimated ultimate recovery and recovery factors of shale-gas wells using a probabilistic model of original gas in place.SPE Reserv Eval Eng.2018.https://doi.org/10.2118/189461-pa.
Rivera N,et al.Static and dynamic uncertainty management for probabilistic production forecast in Chuchupa Field,Colombia.SPE Reserv Eval Eng.2007.https://doi.org/10.2118/100526-pa.
Schwartz ES.The stochastic behavior of commodity prices:implications for valuation and hedging.J Finance.1997;52(3):923-73.https://doi.org/10.1111/j.1540-6261.1997.tb02721.x.
SQW.Economic study for ocean energy development in Ireland.2010.http://www.seai.ie/Renewables/Ocean_Energy/Ocean_Energy_Information_Research/Ocean_Energy_Publications/SQW_Eco nomics_Study.pdf.
Suslick SB,Schiozer DJ.Risk analysis applied to petroleum exploration and production:an overview.J Pet Sci Eng.2004;44(1-2):1-9.https://doi.org/10.1016/j.petrol.2004.02.001.
van der Poel R,Jansen JD.Probabilistic analysis of the value of a smart well for sequential production of a stacked reservoir.J Pet Sci Eng.2004;44(1-2):155-72.https://doi.org/10.1016/j.petrol.2004.02.012.
Wang Z,Li L.Valuation of the flexibility in decision-making for revamping installations-a case from fertilizer plants.Pet Sci.2010;7(3):428-34.https://doi.org/10.1007/s12182-010-0089-6.
Wang Q,Zhang BS.Risk analysis of overseas oil and gas exploration and development-taking the Central Asia as an example.J Tech Econ Manag.2012;01:23-36.https://doi.org/10.3969/j.issn.1004-292X.2012.01.005(in Chinese).
Wang Z,Zhao L,Liu M.Impacts of PSC elements on contracts economics under oil price uncertainty.In:International conference on E-business and E-government,Guangzhou China.2010.
Wang DJ,Li XS,Liu MM,Wang Z.A simulation analysis of international petroleum contracts based on the stochastic process of oil price.Acta Pet Sin.2012;33(3):513-8.https://doi.org/10.7623/syxb201203026(in Chinese).
Weijermars R,Sorek N,Sen D,Ayers WB.Eagle Ford Shale play economics:U.S.versus Mexico.J Nat Gas Sci Eng.2017;38:345-72.https://doi.org/10.1016/j.jngse.2016.12.009.
Welkenhuysen K,Rupert J,Compernolle T,Ramirez A,Swennen R,Piessens K.Considering economic and geological uncertainty in the simulation of realistic investment decisions for CO2-EORprojects in the North Sea.Appl Energy.2017;185:745-61.https://doi.org/10.1016/j.apenergy.2016.10.105.
Yan W,Cheng ZY,Li HD.Risk statistics and decision analysis.Beijing:Economy&Management Publishing House;1999(in Chinese).
Zhang BS,Wang Q,Wang YJ.Model of risk-benefit co-analysis of oversea oil and gas projects and its applications.Syst Eng Theory Pract.2012;32(02):246-56.https://doi.org/10.3969/j.issn.1000-6788.2012.02.003(in Chinese).
Zhao L,Feng LY.Establishment and application of evaluation and investment timing model for undeveloped oilfields.J China Univ Pet.2009;33(6):161-6.https://doi.org/10.3321/j.issn:1673-5005.2009.06.033(in Chinese).
Zhu L,Zhang Z,Fan Y.Overseas oil investment projects under uncertainty:how to make informed decisions?J Policy Model.2015;37(5):742-62.https://doi.org/10.1016/j.jpolmod.2015.08.001.
1The project is owned by a national petroleum company of China.The related information is provided by this company.
2The volatility of the OOIP is assumed to be low in this paper,therefore, its impact on the NPV is relatively weak, and the bubble Footnote 2 continued size of quadrant I is just a little larger than that of quadrant III. Once the volatility of OOIP is raised, OOIP’s impacts on the NPV will obviously be enhanced.