STRATEGIC PLANNING OF ARCTIC SHELF DEVELOPMENT USING FRACTAL THEORY TOOLS

• Published in: Journal of Mining Institute
• Main Authors: V. S. Vasiltsov, V. M. Vasiltsova
• Format: Article in Journal/Newspaper
• Language: English; Russian
• Published: Saint-Petersburg Mining University 2018
• Subjects: Mining engineering. Metallurgy; TN1-997; Arctic
• Online Access: https://doi.org/10.31897/pmi.2018.6.663; https://doaj.org/article/5588412713b442a4a41cdd991a260072

The paper justifies the necessity to utilize new methods of strategic planning in oil and gas field exploitation in the Arctic shelf during the implementation of high-technology diversified model of development for oil and gas companies (OGC) based on principles and tools of fractal theory. It has been proved that despite its challenging conditions the Arctic represents not only resource potential of the country and a guarantee of national safety, but also a key driver of market self-identification and self-organization of OGCs. Identified and analyzed problems in institutional procurement of shelf development and utilized methods of strategic planning and project management, both on the levels of state and corporate governance, demonstrate that reductive approach of the fractal theory allows to take into account diversification of heterogeneous multicomponent project models, which can be reduced to a single management decision with inverse iterations of neural network modelling. Suggested approach is relevant for strategic planning not only on the stage of investment portfolio justification, but also for identification and assessment of project risks; ranking of projects according to the order of their implementation; back-and-forth management (monitoring and supervision) and project completion. It has been detected that such basic properties of the fractal as self-similarity, recurrence, fragmentation and correlation between all fractal dimensions allow to systematize chaotically changing values of market parameters in the Arctic shelf development project, which provides an opportunity to forecast market development with minimal prediction errors.