Oil and Gas Construction Logistic Optimization through the Choice of Supply Chain Model

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MAGAZINE №1(84) February 2018

AUTHOR Korneeva O.N. - Marketing director, LLC PayOnline (Moscow, Russia)

CATEGORY Transportation in logistics Analytics and reviews Industrial companies’ corporate logistics


One of the significant conditions of oil and gas industry development is the steady sale of production including its cheap and reliable transportation to the consumer. Taking into account the fact that oil and gas are used primarily in the energy sector, the reliability of supplies is in priority. This determines construction and development of the pipeline network as the most efficient way to deliver hydrocarbons to customers. But due to remoteness of the construction site from suppliers such activities are connected with a number of difficulties. In addition, this process includes development of new territories characterized by unique natural, climatic, geographical, demographic, environmental and other factors that impose additional requirements and complicate the project logistics, contributing uncertainties affecting the project time frame and budget.

This article discusses the methodology of sequential optimization of construction logistics of the main gas pipeline. Considerable attention is paid to mathematical modeling and selection of a supply chain management model. A substantial part is devoted to practical modeling of transport logistics supplying a large-diameter pipe to the head section of the "Siberia Power".

The work shows that according to the results of the analysis and supply chain modeling, it is possible to optimize the logistics budget of the construction site by 10% of the total logistics costs of the project. Also, the article highlights the importance of the risk component caused by the peculiarities of each individual construction site and uncertainty factors that can vary at different stages of the project in different ways, which makes it impossible to use linear dependencies for their forecasting and leads to a spasmodic increase in risks. So, one of the few effective methods for predicting deviations is a scenario analysis based on a multifactor model. 

 Electronic version


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