Logistics Simulation of Offshore Production Sites

Abstract

Logistics of offshore production sites is rising in importance due to massive increase in exploration and production of oil and gas. In addition to this, the number of production sites and number of the ships that are working in these fields such as crude oil tankers and Platform Supply Vessels (PSV) are increasing significantly with the complexity of logistics operation process. Although there are some researches about fleet efficiency of supply vessel in offshore logistics, there are only few studies simulating the complete supply chain management of the offshore production fields. This thesis covers the investigation of the logistics of supplies and transportation of oil from/to offshore production sites around Brazilian coasts. Besides, it proposes an option to extract relevant information from an AIS (Automatic Identification System) database and use this information to create the inputs of a stochastic simulation about logistics of offshore production sites. A database which contains 6 months of AIS tracking data for 90 ships has been analyzed. An algorithm has been developed to be able to identify the behavior and role of each PSV and each crude oil tanker in the studied offshore supply chain. Offshore logistics activities in Campos Basin, i.e one of important coastal sedimentary basins of Brazil, has been modeled by using Discrete Event Simulation (DES) methodology. It includes 5 ports, 15 crude oil tankers, 23 PSVs and 38 offshore production platforms such as FPSO (Floating Production, Storage and Offloading), fixed platforms and SS (Semi-submersibles). Several distributions have been extracted for each type of ship and operation profilethen used as input variables for the simulation. Following the implementation of stochastic parameters into the model, the simulation is repeated until it reaches to convergence. The preliminary results show a good accordance between simulation outputs and measured data from AIS database. However, future work is required to improve the offshore supply chain simulation and robustness of the simulation. Optimization is also a possible improvement for the next future. We suggest that DES is a useful tool to make precise analyses and decisions in offshore logistics applications.