Real Options and Genetic Algorithms to Approach of the Optimal Decision Rule for Oil Field Development Under Uncertainties

  • Juan G. Lazo Lazo
  • Marco Aurélio C. Pacheco
  • Marley M. B. R. Vellasco
Part of the Advances in Soft Computing book series (AINSC, volume 42)


A decision to invest in the development of an oil reserve requires an in-depth analysis of several uncertainty factors. Such uncertainties may involve either technical uncertainties related to the size and economic quality of the reserve, or market uncertainties. When a great number of alternatives or options of investment are involved, the task of selecting the best alternative or a decision rule is very important and complex due to the considerable number of possibilities and parameters that must be taken into account. This paper proposes a new model, based on Real Option Theory, Genetic Algorithms and on Monte Carlo simulation to find an optimal decision rule for alternatives of investment regarding the development of an oil field under market uncertainty that may help decision-making in the following situation: immediate development of a field or wait until market conditions are more favorable. This optimal decision rule is formed by three mutually exclusive alternatives, which describe three exercise regions through time, up to the expiration of the concession of the field. The Monte Carlo simulation is employed within the genetic algorithm to simulate the possible paths of oil prices up to the expiration date. The Geometric Brownian Motion is assumed as stochastic process for represents the oil price. A technique of variance reduction was also used to improve the computational efficiency of the Monte Carlo simulation.


Real Options Genetic Algorithms Monte Carlo Simulation Latin Hypercube Sampling 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Juan G. Lazo Lazo
    • 1
  • Marco Aurélio C. Pacheco
    • 1
  • Marley M. B. R. Vellasco
    • 1
  1. 1.ICA : Applied Computational Intelligence Laboratory, Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Rua Marquês de S. Vicente 225, Gávea, Rio de Janeiro, CEP 22453-900, RJBrazil

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