This work presents the conception, modeling and development of an E&P projects economical analysis system under uncertainty that integrates the following modules:
an optimization hybrid system for oilfields development based in evolutionary algorithms with distributed evaluation, proxies and use of quality maps to optimize the place and quantity of wells in a delimited petroleum field to maximizing the NPV of the alternative. Also, this system considers some technical constrains as the minimum wells distance and maximum wells trajectory.
a model based on Genetic Algorithms and Monte Carlo simulation designed to find an optimal decision rule for some oil field development alternatives obtained from previous module, considering market uncertainty (oil price), that may help decision-making with regard to: developing a field immediately or waiting until more favorable market conditions.
In the economic analysis also is considered, for each alternative under evaluating, the option of investment in information taking in account interactions of different uncertainties types. This analysis considers the option of future production expansion by installing an additional well under reserve volume uncertainties in the in the area to be drained by the additional well.
Some computational intelligence techniques were applied in this system as: evolutionary algorithms, neural networks and fuzzy numbers; also, the system uses other techniques as Real Options and Monte Carlo Simulation to treat uncertainties.
The obtained outcomes show the benefits to have an integrated decision support system to the decision-making in the economic analysis of oilfields development.