Optimization models and heuristics for effective pipeline decommissioning planning in the oil and gas industry

Abstract

Efficient operation sequencing is crucial in industrial processes to minimize delays and optimize resource utilization. This study focuses on the sequencing of operations for the recovery of decommissioned submarine pipelines, aiming to minimize project completion times. Unlike traditional sequencing problems, our approach incorporates unique constraints such as precedence relationships and the composition of trips for pipeline removal. We propose an optimization framework integrating a mathematical model and a hybrid solution that combines metaheuristic algorithms with exact methods for solving large-scale instances. Computational experiments were conducted on 40 instances of 100 pipelines each, randomly drawn from real-world data. The heuristic generated feasible initial solutions in all cases and enabled the mathematical model to find optimal solutions in 42.5% of the instances. However, in 35% of the cases, no feasible solutions were obtained within the time limit. For cases where the solver reached a solution, the average project completion time was 214.07 days, with a median of 0.0 and a standard deviation of 547.35 days. A real-world case study highlighted the practical applicability of the proposed approach. Using the constructive heuristic as the solver’s initial solution achieved the best result within 5000 s, with an objective function value of 9774 days. This work is particularly relevant in Brazil’s Oil and Gas industry, where deep-water flexible pipelines and strict environmental deadlines demand effective optimization models for decommissioning planning.