dc.creatorMarques J.B.D.
dc.creatorTrevisan O.V.
dc.creatorMarques L.M.
dc.creatorRavagnani A.T.G.
dc.date2014
dc.date2015-06-25T17:57:27Z
dc.date2015-11-26T14:51:28Z
dc.date2015-06-25T17:57:27Z
dc.date2015-11-26T14:51:28Z
dc.date.accessioned2018-03-28T22:03:02Z
dc.date.available2018-03-28T22:03:02Z
dc.identifier9781632665904
dc.identifierSpe Hydrocarbon Economics And Evaluation Symposium. Society Of Petroleum Engineers (spe), v. , n. , p. 379 - 399, 2014.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84906569736&partnerID=40&md5=174bc2be9abb7eb1be4eea99e716de78
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87250
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87250
dc.identifier2-s2.0-84906569736
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1254472
dc.descriptionOver the last two decades, the stochastic processes and copula theories have been widely used in financial analysis, especially in the conditional variance models. Nevertheless, there are few applications cases addressing to the economic evaluation of E&P projects. In such context, the present paper proposes a comprehensive methodology for integration of these theories to the economic evaluation based on discounted cash flow under uncertainty of E&P projects. A cash flow simulator was specially built for the purpose, enabling the applications these theories on the relevant econometric variables. Some simulations are performed with production curves modeled by a set of analytic functions, including: linear function for the start of production until the plateau, exponential function for production decline curve and sigmoid functions for water curves (injection and production). All models were validated against curves derived from reservoir flow simulators. Four models are shown in this paper: two models for forecasting oil price based on GARCH(1,1) and EGARCH(1,1), one model for the attractiveness minimum rate based on ARMA(1,1) and the last one refers to a model based on Gumbel copula for the CAPEX and OPEX values. The models are applied to predict NPV by cash flow simulator and were performed using the current fiscal regimes valid in Brazil, considering the tax system and production sharing contracts. In the both cases, this paper including the legal details related to the government take. The aim of these models is to determine the breakeven oil price for projects on the Brazilian pre-salt fields. The main contribution of the present work is to provide analysts with a tool and a methodology to anticipate risk analysis of Brazilian oil fields projects based on VaR and A VaR measures. The work is divided into five sections: introduction, stochastic processes and copula theories, integration methodology, applications, results and conclusions. Copyright 2014, Society of Petroleum Engineers.
dc.description
dc.description
dc.description379
dc.description399
dc.description3esi,Palantir Solutions,Society of Petroleum Evaluation Engineers (SPEE)
dc.descriptionAl-Harthy, M., Steve Begg, S., Bratvold, R.B., Copulas: A new technique to model dependence in petroleum decision making (2005) Journal of Petroleum Science and Engineering, 57 (2007), pp. 195-208
dc.descriptionAli, M.M., Mikhail, N.N., Haq, M.S., A class of bivariate distributions including the bivariate logistic (1978) J. Multivariate Anal., 8, pp. 405-412
dc.descriptionArmstrong, M., Galli, A., Bailey, W., Coutet, B., Incorporating technical uncertainty in real option valuation of oil projects (2004) Journal of Petroleum Science and Engineering, 44, pp. 67-82
dc.descriptionAccioly, R., Chiyshi, F., Modelling dependence with copulas: A useful tool for field development decision process (2004) J. Pet. Sci. Eng., 44, pp. 83-91
dc.descriptionAné, T., Kharoubi, C., Dependence structure and risk mearure (2003) Journal of Business, 76 (3), pp. 411-438
dc.descriptionBollerslev, T., Generalized autoregressive conditional heterocedasticity (1986) Journal of Econometrics, 31, pp. 307-327
dc.descriptionBouyé, E., Durrleman, V., Nikeghbali, A., Riboulet, G., Roncalli, T., (2000) Copulas for Finance - A Reading Guide and Some Applications, , http://ssrn.com/abstract=1032533
dc.descriptionBox, G.E.P., Jenkins, G.M., Reinsel, G.C., (1994) Time Series Analysis: Forecasting and Control, , 3rd edition Englewood Cliffs, NJ Prentice-Hall 1994
dc.descriptionBreymann, W., Dias, A., Embrechts, P., Dependence structures for multivariate high-frequency data in finance (2003) Quant. Finance, 3, pp. 1-14
dc.descriptionCherubini, U., Luciano, E., Vecchiato, W., (2004) Copula Methods in Finance, , New York John Wiley & Sons
dc.descriptionClayton, D.G., A model for association in bivariate life tables and its applications in epidemiological studies of familial tendency in chronic disease incidence (1978) Biometrika, 65, pp. 141-151
dc.descriptionCook, R.D., Johnson, M.E., A family of distributions for modelling non-elliptically symmetric multivariate data (1981) Journal of Royal Statistical Society B, 43 (2), pp. 210-218
dc.descriptionDemirmen, F., Reliability and uncertainty in reserves: How the industry fails, and a vision for improvement (2005) SPE Hydrocarbon Economics and Evaluation Symposium Held in Dallas, pp. Texas+April3-April5. , SPE 94680
dc.descriptionDurbin, J., Stuart, A.S., Inversions and rank correlations (1951) Journal of Royal Statistical Society Series B, 2, pp. 303-309
dc.descriptionEmbrechts, P., McNeil, A., Straumann, D., Correlation and dependency in risk management: Properties and pitfalls (2002) Risk Management: Value at Risk and Beyond, pp. 176-223. , M.A.H. Dempster Cambridge University Press
dc.descriptionEngle, R.F., Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation (1982) Econometrica., 50, pp. 987-1007
dc.descriptionFisher, R.A., Tippett, L.H.C., Limiting forms of the frequency distributions of the largest or smallest member of a sample (1928) Proc. Camb. Philos. Soc., 24, pp. 180-190. , New York
dc.descriptionFrank, M.J., On the simultaneous associativity of F(x,y) and x+y-F(x,y) (1979) Aequationes Math, 19, pp. 194-226
dc.descriptionFréchet, M., Les ProbabilitésAssociées à unSystè med'Evénements Compatibles et Dépendants (1940) Hermann &Cie
dc.descriptionGalambos, J., (1987) The Asymptotic Theory of Extreme Order Statistics, , Kreiger Publishing Melbourne
dc.descriptionGlosten, L.R., Jagannathan, R., Runkle, D., On the relation between the expected value and the volatility of the nominal excess return on stocks (1993) Journal of Finance, 48, pp. 1779-1801
dc.descriptionGumbel, E.J., Distributions des Valeurs Extremes en Plusieurs Dimensions (1960) Publications de L' Institute de Statist' 1 Que de L' Universit' e de Paris, 9, pp. 171-173
dc.descriptionHamilton, D.J., Oil and the macroeconomy since World War II (1983) The Journal of Political Economy, 9, pp. 228-248
dc.descriptionHamilton, J.D., (1994) Time Series Analysis, , Princeton, NJ Princeton University Press
dc.descriptionHernández-Maldonado, D́az, M.V., Erdely, A., A joint stochastic simulation method using the bernstein copula as a flexible tool for modeling nonlinear dependence structures between petrophysical properties (2012) Journal of Petroleum Science and Engineering, 90-91, pp. 112-123. , DOI: 10.1016/ j.petrol.2012.04.018
dc.descriptionHoeffding, W., Scale-invariant correlation theory (1940) The Collected Works of Wassily Hoeffding, pp. 57-107. , N. I. Fisher and P. K. Sen New York Springer-Verlag
dc.descriptionHougaard, P., A class of multivariate failure time distributions (1986) Biometrika, 73, pp. 671-678
dc.descriptionJoe, H., (1997) Multivariate Models and Dependence Concepts, , London Chapman & Hall
dc.descriptionKilian, L., Not all oil price shocks are alike: Disentangling demand and supply shocks in the cmde oil market (2009) The American Economic Review, 99 (3), pp. 1053-1069
dc.descriptionKim, I.M., Loungani, P., The role of energy in real business cycle models (1992) Journal of Monetary Economics, 29, pp. 173-189
dc.descriptionKimeldorf, G., Sampson, A.R., Uniform representations of bivariate distributions (1975) Communications in Statistics, 4, pp. 617-627
dc.descriptionKlugman, S.A., Parsa, R., Fitting bivariate loss distributions with copulas (1999) Insurance: Mathematics and Economics, 24, pp. 139-148
dc.descriptionKurowicka, D., (2010) Dependence Modeling: Vine Copula Handbook, , World Scientific Publishing Company
dc.descriptionLee, L., Generalized econometric models with selectivity (1983) Econometrica, 51, pp. 507-512
dc.descriptionLintner, J., The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets (1965) Review of Economics and Statistics, (47), pp. 13-37
dc.descriptionMashal, R., Zeevi, A., (2002) Beyond Correlation: Extreme Co-movements between Financial Assets, , Unpublished, Columbia University
dc.descriptionMcNeil, A.J., Frey, R., Embrechts, P., (2005) Quantitative Risk Management: Concepts, Techniques and Tools, , Princeton University Press Princeton, NJ
dc.descriptionMikosch, T., Copulas: Tales and facts (2006) Extremes, 9, pp. 3-20
dc.descriptionMork, A.K., Olsen, O., Mysen, H.T., Macroeconomic responses to oil price increases and decreases in seven OECD countries (1994) Energy Journal, 15 (4), pp. 19-35
dc.descriptionMossin, J., Equilibrium in a capital asset market (1986) Econometrica, 34, pp. 768-783. , Chicago
dc.descriptionNelson, R.B., Conditional heteroskedasticity in asset returns: A new approach (1991) Econometrica., 59, pp. 347-370
dc.descriptionRachev, S.T., Stoyanov, S.V., Fabozzy, F.J., (2008) Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization, p. 382. , New Jersey John Wiley & Sons
dc.descriptionRose, P., (2001) Risk Analysis and Management of Petroleum Exploration Ventures, , AAPG Tulsa, Oklahoma, USA
dc.descriptionSchweizer, B., Wolff, E.F., On nonparametric measures of dependence for random variables (1981) Ann Statist, 9, pp. 879-885
dc.descriptionSharpe, W.F., Capital asset prices: A theory of market equilibrium under conditions of risk (1964) Journal of Finance, (19), pp. 425-442
dc.descriptionSklar, A., Fonctions de répartition à n dimensionsetleursmarges (1959) Publications de L'Institut de Statistique de L'Université de Paris, 8, pp. 229-231
dc.descriptionA global framework for insurer solvency assessment (2004) Research Report, , www.actuaries.org, The International Actuarial Association
dc.descriptionTreynor, J.L., Toward a Theory of Market Value of Risky Assets, pp. 15-22. , Unpublished Manuscript. Rough draft dated by Mr. Treynor to the fall of 1962. A final version was published in 1999, in Asset Pricing and Portfolio Performance Robert A. Korajczyk London: Risk Books
dc.descriptionWei, W.W.S., (2006) Time Series Analysis - Univariate and Multivariate Methods, p. 614. , 2nd ed. Pearson Education
dc.descriptionZakoïan, J.M., Threshold heteroskedastic models (1994) Journal of Economic Dynamics and Control, 18, pp. 931-955
dc.languageen
dc.publisherSociety of Petroleum Engineers (SPE)
dc.relationSPE Hydrocarbon Economics and Evaluation Symposium
dc.rightsfechado
dc.sourceScopus
dc.titleStochastic Processes And Copula Model Applied In The Economic Evaluation For Brazilian Oil Fields Projects
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución