Abstract
Oil fields development plans invariably translate into huge investments. Any effort to improve these plans is important and could save expressive sums. One of the main issues in these highly capital-demanding projects is the uncertainty present in most of its key drivers. This paper present a technique to model dependency between variables in view of the construction of bivariate distributions based on that dependency structure. These bivariate distributions can be used in simulation studies to improve uncertainty analysis of our development plans. The technique presented is based on the concept of copulas, in which the copula function links the univariate margins with their full multivariate distribution. Copulas' modeling has been increasingly used in financial risk analysis in recent years.
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