Selection of Alternatives of Investment in Information for Oilfield Development Using Evolutionary Real Options Approach

Consider an undeveloped oilfield with uncertainty about the size and quality of its reserves. There are some alternatives to invest in information to reduce the risk and to reveal some characteristics of the reserve. This paper presents an evolutionary real options model of optimization under uncertainty with genetic algorithms and Monte Carlo simulation, to select the best alternative of investment in information. There is a legal time to expiration of the option to start the investment for the oilfield development. The model considers both the technical uncertainties revealed by the information and the market uncertainty using two different stochastic processes for the oil prices, which are simulated. Monte Carlo simulations evaluate the decision rule curves generated in the evolutionary process. The process evolves toward a near optimum solution, giving the real option value and the optimal decision rule. The evolutionary programming under uncertainty was performed in C++ environment with good results and a description of the programming procedure is provided.