Wax deposition behaviour and prediction modelling of high‐wax crude oil transport in cold regions

Abstract The safe and efficient transportation of high‐wax crude oil presents a persistent challenge, primarily due to wax deposition within pipelines. This problem is particularly severe in extremely cold environments, where elucidating the characteristics of wax deposition and developing robust predictive models are critical for devising effective transportation strategies and ensuring flow assurance. This study conducts a series of flow‐loop experiments to systematically investigate the effects of key parameters, including oil temperature, pipe wall temperature, temperature difference, flow rate, and deposition time, on wax accumulation. The results help clarify the underlying mechanisms of wax deposition. Leveraging the experimental data, a predictive model for wax deposition rate was constructed by integrating a backpropagation (BP) neural network optimized with the sparrow search algorithm (SSA). The SSA‐BP model exhibits a high degree of correlation with experimental results, demonstrating superior predictive accuracy and reliability. These findings substantially advance pipeline flow assurance technologies and offer an innovative, data‐driven approach to addressing the challenges of transporting high‐wax crude oil in extremely cold regions.