Arctic field logistics and transarctic shipping Development of new business and commercial models for Arctic transportation

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1 Faculty of Engineering Science and Technology Department of Marine Technology Sustainable Arctic Sea Transport Group SAST ntnu.edu/sast Arctic field logistics and transarctic shipping Development of new business and commercial models for Arctic transportation PhD Candidate Aleksandar-Saša Milaković Project partners ABB, CHNL, DNV-GL, HiÅ, Marintek, Northenergy Main supervisor Prof Sören Ehlers TUHH / NTNU Co-supervisor Peter Schütz DNV-GL

2 Project Description Project divided in two parts Arctic offshore logistics Impact of Arctic challenges on offshore installations supply chain Transarctic shipping Techno-economical comparison of northern vs southern routes

3 Publications Arctic offshore logistics Offshore upstream logistics for operations in Arctic environment Milaković et al. MTEC 2014 Optimization of OSV fleet for an offshore oil and gas field in the Russian Arctic Milaković et al. OMAE 2015 Transarctic shipping A simulation-based decision support tool for Arctic transit transport Schartmüller et al. OMAE 2015 Shipping oil from the Russian Arctic - Past experiences and future prospects Bambulyak et al. POAC 2015

4 Simulation-based decision support tool for Arctic transit transport Work in progress Part of the methodology presented at OMAE 2015 (Schartmüller et al.) Aleksandar-Saša Milaković, IMT, NTNU, Trondheim, Norway Peter Schütz, DNV-GL, Oslo, Norway Sören Ehlers, IMT, NTNU, Trondheim, Norway

5 Motivation? NSR SCR HOW to design your transport system to maximize benefit of shorter Arctic route?

6 Objectives Build a simulation framework both for NSR and SCR Develop a decision support tool to be used by the shipowners for ad-hoc calculations or for long term planning Establish index showing dependency between profitability of use of Arctic route and variables affecting it

7 Simulation framework NSR CAPEX (including investment into ice class) OPEX VOYEX: - Fuel consumption in open water and in ice based on brash ice resistance calculations - Rosatomflot fee - Insurance fee NSR single trip costs SCR single trip costs CAPEX+OPEX+VOYEX RFR = Amount of cargo transported SCR CAPEX OPEX VOYEX: - Fuel consumption in open water - Suez Canal fee - Insurance fee

8 Simulation framework NSR CAPEX (including investment into ice class) OPEX VOYEX: - Fuel consumption in open water and in ice based on brash ice resistance calculations - Rosatomflot fee - Insurance fee NSR single trip costs SCR SCR single trip costs Still water resistance + wave resistance (according to metocean data for the route) CAPEX OPEX VOYEX: - Fuel consumption in open water - Suez Canal fee - Insurance fee

9 Simulation framework NSR CAPEX (including investment into ice class) OPEX VOYEX: - Fuel consumption in open water and in ice based on brash ice resistance calculations - Rosatomflot fee - Insurance fee NSR single trip costs SCR single trip costs SCR CAPEX OPEX VOYEX: - Fuel consumption in open water - Suez Canal fee - Insurance fee

10 Modeling ice conditions Ice thickness Separate paper

11 Case study Container shipping on route Rotterdam Yokohama, slow-steaming the NSR How to build a fleet (wrt ice class) for system to be economically feasible? 2 possible options: 1) Vessels sailing only SCR (no ice class) RFR SCR [$ / TEU] 2) Vessels sailing NSR when ice conditions allow it and SCR otherwise (vessels having ice class) RFR SCR+NSR [$ / TEU]

12 Case study RFR Only SCR Number of operational days along the NSR

13 Profitability of investment index n NSR = Number of operational days along NSR IC = Vessel s ice class DWT = Vessel size CAPEX dist NSR/SCR = Distance between ports IB = Icebreaker fee SC = Suez Canal fee I SCR = Insurance fees for SCR I NSR = Insurance fees for NSR BP = Bunker price i = f ( n NSR, IC, DWT, dist NSR/SCR, IB, SC, I SCR, I NSR, BP ) Verification of results? Data from the shipowners regarding various costs aspects would be very valuable.

14 Optimization of OSV Fleet for an Offshore Oil and Gas Field in the Russian Arctic OMAE 2015 Conference St. John's, Newfoundland, Canada May 31 - June 5, 2015 Aleksandar-Saša Milaković, IMT, NTNU, Trondheim, Norway Mads Ulstein, IMT, NTNU, Trondheim, Norway Alexei Bambulyak, Alvaplan-niva, Tromsø, Norway Sören Ehlers, IMT, NTNU, Trondheim, Norway

15 Motivation? 22% of world s undiscovered petroleum 84% of which located offshore Ref. US Geology Survey

16 Motivation? 22% of world s undiscovered petroleum 84% of which located offshore Increase of production costs Ref. US Geology Survey

17 Objectives? How will the specific Arctic challenges influence offshore upstream logistic operations? - Increased distance between Onshore Supply Base (OSB) and offshore facility - Specific environmental conditions Discrete Event Simulation of Offshore Supply Vessel (OSV) fleet supporting supply chain for offshore field in Arctic? Integration of simulation and optimization to asses an optimal configuration of OSV fleet in order to reduce costs? 18

18 Simulation framework Discrete Event Simulation using SimEvents/MATLAB Model Input variables Output values Offshore installation S Cargo demand (Gaussian) O S V O S V S Wind speed (Gaussian) S Visibility (Bernoulli) S Polar lows (Bernoulli) S Wave height (Log-normal) D Distance OPERATIONAL LIMITATIONS Time loading/ offloading Waiting for weather Fuel consumption Sailing time 19 Onshore supply base D Fleet composition Cargo backlog Waiting time

19 Optimization Optimization model is used to optimize OSV fleet configuration Optimization parameters Heuristic: Genetic Algorithm Objective function: Minimize Fuel Consumption Constraint: Variable: Cargo level at base (backlog) set to a certain maximal value Fleet Composition chosen from a specified pool of vessels 20

20 Hybrid model Hybrid model combines simulation and optimization environments STOCHASTIC INPUT SIMULATION OPTIMIZATION VARYING PARAMETER (Fleet composition) Genetic Algorithm - Crossover - Mutation NO OPTIMAL FLEET COMPOSITION YES Optimal solution SOLUTION (Fuel consumption) 21

21 Case study Case study is conducted on a virtual offshore field in Russian Arctic Oil discovery in Kara Sea made by Rosneft and ExxonMobil in summer 2014 Structure located 135 nm off the coast as a part of Prinovozemelskiy 1 block (EPNZ1) 22 Ref.

22 Case study SIMULATION OPTIMIZATION FLEET COMPOSITION 23

23 Conclusions and limitations Conclusions Model proves to be capable of performing simulation of OSV sailing Integration of simulation and optimization yields realistic results Limitations and further work Additional environmental data should be implemented including sea ice and icebergs Fuel consumption more realistically calculated Optimization procedure is based on fuel consumption only - to be extended to account for capital costs 24