Monte Carlo-Driven Decision Support for Subsea Pipeline Decommissioning under Uncertainty

Abstract As Brazil's offshore fields age, the need to decommission subsea structures intensifies. While the importance of decommissioning is well understood, the current literature lacks a detailed stochastic approach for the decommissioning of subsea pipelines. This paper fills the gap by presenting a stochastic multi-criteria decision analysis (MCDA) to aid in determining the most appropriate decommissioning option. The method includes a way to handle data variability and incorporates decision-makers' preferences using MCDA techniques. Through a case study of a rigid pipeline in Brazil's Cação field, various decommissioning strategies are evaluated against criteria like safety, social, environmental impact, and cost. The study considers various decommissioning alternatives, including leaving the pipeline in place, rock deposition at the pipeline ends, total removal by cutting and lifting sections, and complete removal by reverse S-lay. The study, using Monte Carlo simulations to account for data uncertainties, concludes that leaving the pipeline in place is the preferable choice. This research provides a comprehensive tool for transparent decommissioning decision-making in the face of uncertainties, demonstrating its effectiveness in practical scenarios.