Assessing Maintenance Time, Cost and Uncertainty for Offshore Production Facilities in Arctic Environment

• Main Authors: Homlong, Eirik, Kayrbekova, Dina, Panesar, Sukhvir, Markeset, Tore
• Other Authors: Unknown Labs Norway, University of Stavanger, Apply Sørco, Jan Frick, Bjørge Timenes Laugen, TC 5, WG 5.7
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2011
• Subjects: Operation; Maintenance; Offshore production facility; Arctic conditions; Cost assessment; Monte Carlo Simulation method; [INFO]Computer Science [cs]; Arctic; Norway
• Online Access: https://hal.inria.fr/hal-01524243; https://hal.inria.fr/hal-01524243/document; https://hal.inria.fr/hal-01524243/file/978-3-642-33980-6_26_Chapter.pdf; https://doi.org/10.1007/978-3-642-33980-6_26

Part 1: Production Process International audience Many of the oil and gas fields on the Norwegian Continental Shelf are entering their tail-end phase of the production life cycle, and the production in temperate areas is slowly declining. Thus, the oil and gas industry looks northwards, and this trend can be seen in all countries bordering the Arctic. Arctic conditions in the form of climate, darkness, ice, remoteness from infrastructure, etc. will cause different and bigger strains on the human factor of the working personnel and machinery than can be seen in more temperate areas. Furthermore, the fact that less data exists – in the form of both statistics and experience of the operation and maintenance strategies to be executed in the Arctic areas – poses additional challenges for the design of offshore production facilities to be used in the less familiar environment of the Arctic. This paper introduces and discusses a method for maintenance cost and time assessments and their uncertainty, using the Monte Carlo simulation method. The method is to be employed when designing for operation and maintenance in Arctic conditions of offshore production facilities. The proposed method can enable a decision maker to assess and adjust maintenance time and cost data more realistically.