Modeling and Optimization of a Maritime Transport System for an Offshore Oil Platform


Arctic offshore oilfield

The offshore oilfield in the Pechora Sea is a project of a major Russian company for oil extraction on the Arctic shelf. Since it was designed for the Arctic region, the field can be used in extreme weather conditions and withstand maximum ice loads, which makes it a unique and technically complex project.

The first loading of oil from the field to an arctic tanker was carried out in April 2014, while by 2021 the field operator is planning to increase the rate of oil production up to five million tons. However, gained experience revealed that a marine transport system for oil export and delivery of supply cargoes to and from the field should be improved. Taking into account the variability of the Arctic sea meteorological and ice conditions, it was necessary to increase the effectiveness, survivability, and safety of the maritime transport system.

To meet this challenge, in 2016, the experts from a state research center studied a transport system for the offshore oilfield, for the period up to the year 2038, using a detailed simulation model developed in the AnyLogic environment.


The marine transport system includes the field itself, two shuttle tankers, and several offshore supply vessels. Despite the apparent simplicity, the transport system is quite specific due to several important features:

By using agent-based and discrete event modeling in the AnyLogic environment, the state research center experts created a simulation model of the transport system. The model allowed them to consider all of the important technical characteristics, along with physical and logistic processes of the real system. The vessel operations between the offshore oilfield and the Murmansk port were modeled in the GIS environment, taking into account natural conditions on the route.

Marine Transportation Network Model
Marine Transportation Network Model

Several interacting computational processes were represented as separate simulation algorithms in the general simulation model:

Simulation Model of the Transport System
Simulation Model of the Transport System

To describe simultaneous accessibility of the four cargo terminals while considering their limitations, the experts created the stochastic generator of natural conditions at the area of field location. The generator enabled them to model a time series of 15 environmental parameters, such as speed and direction of wind and current, height of waves, cloudiness, air temperature, visibility, and others, taking into account the interrelation of different parameters. For example, wind and current have an impact on speed and direction of an ice drift.

The model also described the complicated logic of multiple ship approaches to the field, that are affected by varied weather conditions. The experts added to the model the ability to conduct consecutive operations of various types of supply cargoes, as well as unscheduled interruption of operations due to weather window termination, transition of the vessel to another terminal, or departure of the vessel beyond the three-mile border zone of the field. Also, the model included an algorithm for a local decrease of oil production in case of a risk of a full filling of oil storage.

The following data served as input parameters: planned cargo flows of crude oil and supplies for the period until 2038, tactical voyage plan of vessel operations generated using the optimization algorithm, and average duration of ship operations.


The state research center experts analyzed eleven improving measures to increase the efficiency of the marine transport system of the field. The list of measures included: putting into operation an additional shuttle tanker, increasing the speed of oil offloading, using an additional icebreaker, and other methods. The practical aim of the analysis was to increase the efficiency of the transport system in terms of the ratio of the cost and the achieved reduction in volumes of underproduced oil.

The volume of underproduced oil for the period from 2016 to 2038 served as the main efficiency criterion. This volume was calculated based on the number and duration of cases of reduction in volumes of oil production, which occur when storages are fully loaded and tankers cannot offload oil fast enough.

The obtained data helped the experts discover that the construction of a terminal or an additional oil storage shows an absolute effect, i.e. the absence of underproduced oil. However, the practical implementation of these measures proved to be costly and technologically complicated, which is why they were excluded from further consideration.

The simulation modeling allowed the experts to reveal that the expansion of accessibility of oil terminals has the highest positive effect on system efficiency of all the other measures. This can be achieved by implementing a range of technical measures, and by using the short weather windows for tanker cargo operations that allows for increasing the total duration of weather windows by 10-15%. However, the crucial point for system efficiency was not the total duration of accessibility periods, but the availability of a tanker to approach the field at a desired moment of time.

The model enabled the experts to discover that putting an additional shuttle tanker into operation has no significant impact on system efficiency, because the bottleneck of the system is oil offloading under weather change, not the shortage of tankers.

The results of the study collected with the AnyLogic model helped the experts determine technical and operational characteristics for each improving measure and evaluate statistical distribution laws of all key parameters. The obtained data formed a basis for making managerial decisions at the top level of one of the largest oil production companies in Russia.

Another project carried out by the state research center experts for oil production company, with the help of simulation modeling, was the design of a maritime oil transportation system for the harsh ice environment.

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