Simulation Modeling of Offshore Offloading System for Arctic Oil and Gas Condensate Field

Problem:




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 issues in planning transportation in an arctic region are the harsh ice environment and difficult sea conditions. Most of the year, the ships are operating in a 500m ice channel in shore fast ice of Ob’ Bay over 2 m in thickness. Some months of the year, the open water area of Kara Sea is almost completely covered with drifting ice.


  • Define a sufficient amount of arctic cargo tanks and the demand for icebreaker assistance. Calculate the expenses for the tanks' fuel and freight of icebreakers in different ice conditions. 
  • Design a temporary scenario for oil shipment during 2016-2017, when tanks of low capacity and low ice reinforcement will be used. Gazprom Neft plans on placing new arctic tanks of bigger capacity in operation gradually, in accordance with the increase of cargo traffic. Consultants needed to define the system capacity during 2016-2017. 
  • Define the capacity of a shore-based storage facility to be sufficient for usage in ice conditions of different severity. Any storage overflow should be eliminated. Consultants needed to calculate the minimum volume of shore-based storage which will meet capacity requirements within all periods of field development. They had to take into account that the more severe the ice conditions, the more difficult it would be for tanks to provide the required rate of transportation and avoid the storage overflow.

Solution:




By the order of Gazprom Neft Novy Port LLC, the experts from Krylov State Research Centre incorporated ship calculation modules, GIS environment, and a logistic simulation model developed using AnyLogic under common interface. 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.


Tanks were modeled as independent agents moving in the bay and guided by the logic blocks of the simulation model: interactions between tanks and icebreakers, choice of tanks’ speed in correlation with storage fill, other tanks’ locations, ice conditions, and other factors.


Using GIS technology inside the simulation model allowed them to do an analysis of a transportation system sensible to geographic factors, including bathymetric conditions, navigating channels, protected waters, and shore line.


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

Tanker Logistics Simulation Model Interface

Model Interface

  • Characteristics and number of tanks in the channel
  • Time after last pass
  • Air temperature
  • Wind and wave conditions
  • Terms for canal laying

Tanker Logistics Simulation Parameters Change

Changing parameters in different ice conditions

Result:




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


Application of simulation modeling technology allowed them to design a transportation system adjusted for dynamics of ice channel freeze-up, tank traffic, and storage fill. No analytical tool can consider these kinds of dynamic factors.


In the course of the modeling project, Krylov Centre experts also developed best practices to eliminate the risk of storage overflow. The model also showed the optimal amount of channels in land-ice for different ice conditions, approximate dates of canal laying, and volume and terms for icebreaker support for tanks. Analytics defined the dynamics of pilotage outwards, expenses for fuel, and icebreaker support in various scenarios during all periods of field development. The model also helped plan operations during the temporary usage period of small tanks of low ice-class.

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