Hybridizing genetic algorithms and real options to approach of the optimal decision rule for oilfield development under uncertainties

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 Monte Carlo simulation, to find an optimal decision rule for alternatives of investment regarding the development of an oil field under market uncertainty. The objective of this new model is to 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 Mean-Reversion Process is assumed as the stochastic process for the oil price. A technique of variance reduction was also used to improve the computational efficiency of the Monte Carlo simulation. Copyright 2007, Society of Petroleum Engineers.