Iranian Journal of Oil and Gas Science and Technology

Abstract Process pipelines are essential for operations, but their failures can have severe consequences. This study investigates and analyzes the consequence modeling of the pipeline corridor located in southern Iran. It also determines the restricted and impacted areas around the pipelines using PHAST software. The consequence modeling of the main scenarios, including flammable and toxic gas dispersion, fire, and explosion, is analyzed. According to the results, the restricted and impacted areas around the pipelines are 426.9 meters and 454.3 meters for section 1, and 648 meters and 802 meters for section 2, respectively.

Following the results, preventive and mitigative measures are recommended to lower risks. Multiple strategies should be employed to reduce the risks associated with the pipelines. Some strategies aim to decrease the frequency of incidents, while others focus on reducing the impacts and consequences of leakage, rupture, or any other type of containment failure.

Abstract Accurate detection of marine oil spills in Synthetic Aperture Radar (SAR) imagery remains a major challenge. Mineral oil and naturally occurring biogenic slicks often produce nearly identical signatures in SAR imagery, despite their fundamentally different environmental implications. This review critically evaluates the physical processes governing SAR-based oil spill detection and asks whether secondary dielectric and polarimetric effects can overcome the inherent limitations of single-channel intensity data.

The analysis reveals two key findings. First, while wave damping ensures high contrast under optimal wind conditions (3–10 m/s), secondary dielectric effects only emerge in thick emulsions (>0.4 mm). These conditions are rarely encountered in operational scenarios. Second, instrument noise floors (NESZ) severely compromise polarimetric retrievals, limiting their reliability.

Together, these findings provide a practical framework for interpreting SAR imagery. They also guide the integration of AI-driven classification with multi-sensor fusion (X-, C-, and L-band) to reduce false alarm rates in operational monitoring systems.