In this paper it is presented how the application of the Decision Tree technique (DT) with different Utility Functions (UF) and the Certainty Equivalent concept (CE) can reveal the optimal level of financial participation (OLFP) of a given decision maker in risky projects for oil and gas exploration and production.
The decision whether or not to participate in an upstream project may lead to either a one-company contract orand association of several companies, with the aim of distributing the risk to levels tolerated. With this in view, this article will apply the Decision Tree (DT) with five types of Utility Functions (UF) with their respective Certainty Equivalents (CE), and discuss the different results obtained, according to the type of UF used: exponential, hyperbolic tangent, logarithmic, square root and linear, the latter being used for the case of risk indifference and the others for decision makers with risk aversion.
Each company has its particularities in deciding whether or not to participate in an oil and gas exploration and production project, such as the level os risk aversion or its estimate of reserves available for the next years, given the present production. Each utility function has a distinct behavior and each one of them is presented and discussed some utility functions suiting best each decision maker profile. Additionally, the application of different attitudes towards risk in the successive phases of an upstream project is discussed, as well as Multi-Attribute Utility Theory (MAUT), which can take advantage of the five types of utility function, each one capable of representing a different dimension of the same project (e.g. economic, polytical, environmental, technological, financial). Softwares were used to obtain the graphical and numerical results presented. The results obtained are easily replicable.
The novelty is an analysis that compares in detail the use of five utility functions in the study of the optimal level of financial participation in oil and gas exploration projects with risks in a clear and replicable way, applying the results obtained to the profiles of decision makers and explaining certain behaviors in the acquisition and development of oil and gas fields by companies.