Theoretical and applied aspects of scenario analysis of investment projects of enterprises in the mineral resource sector of the economy are considered, its advantages and disadvantages are analyzed. Taking into account the organizational and economic features of mineral resources management, a number of new modifications of the scenario analysis method, aimed at solving an urgent problem - reducing the information uncertainty in assessing the expected efficiency and risk of investment projects, are proposed. The peculiarity of the proposed new modifications is the use of the interval-probabilistic approach in the implementation of the scenario analysis procedure. This approach is based on a moderately pessimistic system of preferences in obtaining point values of the investment project initial parameters. Fishburn estimates and the hierarchy analysis method were used to reduce subjective uncertainty. The maximum likelihood values in the sense of the maximum a priori probability are used as expected estimates. An additional indicator of risk assessment, which characterizes the probability of the event that the net present value of the project will take a value less than the specified one, is proposed. When analyzing one project, this indicator is more informative than the standard deviation. A statistical hypothesis was tested on the improvement of the validity of investment decisions developed using the modified scenario analysis method compared to the standard method.
Effective management in the oil and gas industry is associated with an increased degree of automation, the further improvement of the information systems and technologies in the frame-work of the concept of «intelligent field». Complexity of construction of automated intelligent information systems associated with the working out of models of representation and processing of incomplete knowledge (data). We consider an approach to the construction of a knowledge base using category theory, axiomatic formal theories and the topological Boolean algebra. The possibility to handle fuzzy information and implement the logical conclusion in the framework of the formal deductive systems.