Iranian Journal of Oil and Gas Science and Technology

Abstract Electric Submersible Pumps (ESPs) are widely used in oil and gas production, yet their reliability is challenged by diverse failure modes and inconsistent monitoring practices across manufacturers. This study develops a vendor-agnostic Key Performance Indicator (KPI) framework to enable objective comparison of ESP performance across different brands.

A representative dataset comprising 330 ESP runs across three major ESP brands was analyzed using standardized failure classification and statistical techniques, including Weibull reliability modeling. Failure modes were categorized into mechanical, electrical, hydraulic/gas-related, material, and operational classes, and correlated with vendor-independent KPIs derived from thermal, electrical, hydraulic, and operational measurements.

The results reveal distinct brand-specific failure patterns. Brand A is dominated by electrical failures associated with reduced thermal margin, Brand B exhibits longer characteristic life with wear-out-dominated behavior, and Brand C shows higher susceptibility to hydraulic instability driven by gas interference. Several KPIs, including Motor Temperature Margin (MTM), Current Imbalance (CI), and Gas Interference Index (GII), consistently provide early-warning indicators of failure.

The proposed framework enables cross-brand benchmarking, improves interpretability of ESP health monitoring, and supports the development of predictive maintenance strategies independent of proprietary vendor systems.

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.