Abstract
Workover vessel operations in offshore oil fields are crucial to recovering productivity and helping ensure safety. Because of economic constraints, the number of workover vessels is often limited, and logistics optimization has significant relevance. This paper provides a methodology for increasing efficiency with regards to the operational management of these vessels.
The method to address logistics optimization for a fleet of vessels should consider fleet and operational constraints to help determine the most economic routes to perform tasks in different geographic locations. This paper presents a solution to a workover operation routing problem performed by a heterogeneous fleet of vessels with different load capacities and limited abilities to perform different operations. Decisions were necessary to determine which boat should perform each task scheduled for a location at a specific time. The model was developed using operational historical data integrated into an optimization workflow, based on genetic algorithms, to determine which itinerary and tasks each vessel should execute.
This work evaluated two fleet scenarios characterized by vessel capacity and potential ability to perform specific tasks. The results reveal the possibility of a significant reduction in the total time necessary for the fleet to perform the same group of tasks. For both configurations evaluated, the total time reduction was 20 and 40%, respectively. This reduction in total time could result in an increase in tasks performed within a given time period, improved operational economics attributed to reducing downtime, and less production loss caused by the absence of these services. Another benefit provided by this methodology is the capability to generate a new optimized schedule, as necessary.
In the current scenario of high economic variation and uncertainties and reduced oil prices, increasing operational efficiency is a primary challenge. The study results illustrate the importance of an improved method to optimize fleet logistics, thus improving key performance indicators (KPIs) of interest to stakeholders, such as reduced navigation time, improved economics, and improved operator satisfaction. Furthermore, the general methodology presented can be adapted to other routing problems encountered within the petroleum industry.
