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Improving Efficiency of the Oil and Gas Sector and Other Extractive Industries by Applying Methods of Artificial Intelligence

Listed author(s):
  • Peter F. Kaznacheev

    ()

    (Centre for Resource Economics Director of the Centre for Resource Economics)

  • Regina V. Samoilova

    ()

    (Centre for Resource Economics)

  • Nikola V. Kjurchiski

    ()

    (Russian Presidential Academy of National Economy and Public Administration)

A considerable decline in commodity prices in recent years, primarily oil prices, is the result of a new equilibrium in the market which, in turn, is a direct consequence of technological innovations. In such circumstances, those producers which can adapt to lower prices by reducing costs and increasing efficiency will gain a strong competitive advantage. Until recently, the main driving force of innovative development of the energy sector had been the “shale revolution”. The situation is changing rapidly — the oil and gas industry is in need of new technological solutions that would allow it to weather the storm of lower prices. Currently, one of the areas where innovation is fastest is artificial intelligence. The article provides a brief overview of the most widespread method within artificial intelligence — artificial neural networks and describes their main applications within the oil and gas sector. In their work the authors distinguish highlight three main applications — interpretation of geological data, hydrocarbon production (smart fields) and price forecasting. The use of artificial intelligence can increase efficiency of both geological exploration and production — it allows to achieve more at a lower cost. Under the new market conditions formed in the energy and mining sectors it is crucially important to utilise all available mechanisms to increase efficiency. Following the drop in commodity prices, it has become of vital for companies to acquire more accurate forecasting methods which would allow to analyze market developments and improve strategic planning.

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File URL: ftp://w82.ranepa.ru/rnp/ecopol/ep1659.pdf
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Article provided by Russian Presidential Academy of National Economy and Public Administration in its journal Economic Policy.

Volume (Year): 5 (2016)
Issue (Month): (October)
Pages: 188-197

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Handle: RePEc:rnp:ecopol:ep1659
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