<![CDATA[Crude oil with high pour point oil (HPPO) characteristics exhibits elevated viscosity at low temperatures, promoting the deposition of wax and asphaltenes along pipeline walls. These deposits restrict flow and can lead to partial or complete blockages, posing serious challenges to flow assurance, operational safety, and environmental sustainability. Despite extensive studies on deposition mechanisms, blockage detection methods remain fragmented, with no universally reliable or integrated solution. This study presents a systematic review combined with a bibliometric analysis of Scopus-indexed publications to map research evolution, trends, and technological advancements in blockage detection methods for crude oil pipelines. Bibliometric network mapping is applied to identify dominant research clusters and emerging themes. The review evaluates key detection techniques, including pressure wave analysis, gamma-ray inspection, computational fluid dynamics (CFD), and machine learning approaches. Reported results indicate that pressure wave methods achieve detection accuracies of around 5% with prediction errors up to 35%, while gamma-ray techniques show errors of approximately 5% with maximum deviations of 0.59 cm. CFD models effectively represent flow and deposition behavior but are limited in quantifying blockage size. Machine learning approaches demonstrate improved performance, with accuracies ranging from 80.67% to 89.96%, although their robustness depends on data quality and generalizability. The analysis highlights critical gaps, including limited accuracy under varying conditions, lack of methodological integration, and reliance on single-technique approaches that fail to capture the multi-physics nature of blockage formation. The novelty of this study lies in its integrated bibliometric–systematic synthesis linking detection performance, methodological limitations, and research trends, while proposing a hybrid framework combining physical sensing, numerical modeling, and artificial intelligence. This work provides a comprehensive reference and strategic direction for developing next-generation pipeline blockage detection systems.]]>
