Design and Analysis of Marine System for Oil Transportation in the Arctic by Means of Simulation


The Novoportovskoye oil and gas condensate field is located in the Yamal peninsula and owned by Gazprom Neft, the fourth largest oil producer in Russia. Oil from the field is transferred via 100km pipeline to the sea terminal at Cape Kamenny, where it is loaded into arctic cargo tanks for further transportation. The full-size field development will start in 2016 and continue for several decades.

The main obstacle in arctic marine transportation is the harsh ice environment that makes vessel movement difficult. Most of the year, ships operate in an ice channel in the fast ice of Ob’ Bay, which is over 2 m in thickness and 500 km in length. For some months of the year, the open water area of Kara Sea is almost completely covered with drifting ice. To create the robust system of arctic oil transportation, Gazprom Neft had to solve the following tasks:

  • Define a sufficient amount of arctic oil tankers and the demand for icebreaker assistance. Calculate the expenses for the tankers’ fuel and freight of icebreakers in different ice conditions.
  • Design a temporary logistic scheme for oil transportation during 2016-2017, when tankers of low capacity and low ice class would be used. Gazprom Neft plans to put into operation new shuttle tankers with bigger capacity, in accordance with the step-by-step increase of cargo traffic. Consultants needed to define the performance of a temporary transport system during the 2016-2017 period.
  • Define the capacity of a shore-based storage facility that would be sufficient for usage in ice conditions of different severity. Storage overflow should be eliminated. Consultants needed to calculate the minimum volume of shore-based storage to meet the capacity requirements within all periods of field development. They also had to take into account, that the more severe the ice conditions, the more difficult it would be for tankers to provide the required rate of transportation and avoid the storage overflow.


By the order of Gazprom Neft Novy Port LLC, the experts from Krylov State Research Centre incorporated, under common interface, ship calculation modules, GIS environment, and a logistic simulation model developed using AnyLogic. The simulation model reproduced the dynamics of shore-based storage loading, logics of ships’ motion, and interaction in probabilistic weather conditions, taking into account ice channel freezing.

Tankers were modeled as independent agents moving in the space and guided by the logic blocks of the simulation model. Simulation considered interaction between tankers and icebreakers, adjustment of tanker speed according to storage filling level, location of other tankers, ice conditions, and other factors.

GIS technology in the simulation model allowed for the analysis of a transportation system that considered geographic factors including sea bathymetry, navigating channels, areas of restricted navigation at sea, and shore line.

Since ice channel conditions significantly influence ship traffic, Krylov Centre experts added the following parameters to the model:

  • Characteristics and number of ice channels
  • Time after last pass of the vessel in channel
  • Air temperature
  • Wind and wave conditions
  • Criteria of new canal laying

Tanker Logistics Simulation Model Interface

Model Interface

Tanker Logistics Simulation Parameters Change

Changing parameters in different ice conditions


The application of simulation modeling technology allowed the experts to design a transportation system that considered the dynamics of ice channel freezing, tanker traffic, and storage filling. No analytical tool can consider these kinds of dynamic factors.

Based on multiple model runs, Krylov Centre experts defined the optimal storage volume to be sufficient in ice conditions of different severity.

Experts also recommended the best strategy to eliminate the risk of storage overflow. The model also showed the optimal number of channels in fast ice in different ice conditions, approximate dates of canal laying, and volume and terms for icebreaker support of tankers. Analytics defined the dynamics of putting tankers into operation, expenses for fuel, and icebreaker support in various scenarios during all periods of field development. The model also helped to plan system operation during the temporary period.

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