Planning and scheduling a fleet of rigs using simulation–optimization

• Published in: Computers & industrial engineering 2012-12, Vol.63 (4), p.1074-1088
• Main Authors: Bassi, Hugo Vinícius, Ferreira Filho, Virgílio Jose Martins, Bahiense, Laura
• Format: Article
• Language: English
• Subjects: Offshore oil wells; Oil fields; Operations research; Optimization; Planning; Rigs; Scheduling; Scheduling algorithms; Simulation; Simulation–optimization; Studies
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2012.08.001

► We study a rig-scheduling-and-planning problem with stochastic service time. ► The model deals with rig operations in an offshore environment. ► Model used to schedule an existing fleet of rigs or to scale the size of the fleet. ► Results include measures of performance for each rig and expected delay for a well. ► Additional results are the distribution of well servicing order. Some of the most important and expensive activities in the oil field development and production phases relate to using rigs. These can be used for drilling wells, or for maintenance activities. As rigs are usually scarce compared to the number of wells requiring service, a schedule of wells to be drilled or repaired must be devised. The objective is to minimize opportunity costs within certain operating constraints. This paper present the first stochastic approach to deals with the problem of planning and scheduling a fleet of offshore oil rigs, where the service time is assumed being uncertain. A simulation–optimization method is used to generate “expected solutions” and performance measures for rigs, as well as statistics about well allocation to rigs. The methodology can be used in two different ways – to schedule an existing fleet of rigs or to scale the size of the fleet – both contemplating the uncertain nature of the problem. The method’s expected results include performance measures for each rig, expected delay for a well to be served, the expected schedule of rigs, and a distribution of the well servicing order. The experiments based on real situations demonstrate the effectiveness of the simulation–optimization approach.