AI-Driven Prescriptive Analytics for Hydrate Mitigation in Offshore Petroleum Production

In offshore petroleum systems, one of the main challenges in production management and flow assurance is preventing blockages caused by natural gas hydrates. These crystalline structures form when water and natural gas combine under specific temperature and pressure conditions, leading to well unavailability and requiring significant resources for restoration. In this study, we evaluate hydrate mitigation strategies and their associated economic aspects using an AI-driven prescriptive analytics approach. We propose a framework based on the probabilistic modeling of influencing events, such as unscheduled shutdowns and the probability of hydrate formation based on the duration of fluid stagnation in subsea pipelines. This probabilistic analysis is combined with deterministic data, including production forecast curves, the duration and priority order of mitigation actions, and economic data related to revenues and expenditures. Using this data, we deploy Monte Carlo simulations alongside a rule-based system to assess differences in cumulative production, blockage probabilities, and net present value (NPV) under various scenarios involving production strategies and improvements in hydrate prevention systems. We demonstrate the practical applicability of this framework through three real-world case studies involving an offshore production platform operating in the Brazilian pre-salt, where the results provide valuable insights for decision-making aimed at maximizing value.