Abstract

Marginal oil fields play a critical role in sustaining global hydrocarbon output, particularly as larger assets mature. However, these fields often suffer from aging infrastructure, declining productivity, and fragmented operational data, making production optimization increasingly challenging. This paper presents a conceptual model for integrated wells and reservoir surveillance tailored specifically for the realities of marginal field operations. By unifying diagnostic inputs from both well-level and reservoir-level sources, the model enables proactive monitoring, performance assessment, and intervention planning. The model architecture is composed of three interconnected layers: data acquisition, analytics, and decision support. It incorporates static, dynamic, and real-time data streams to generate actionable insights through threshold-based alerts and deviation detection. Emphasis is placed on integrating core performance indicators such as flow rates, pressure, water cut, and gas–oil ratios to build a comprehensive view of asset health. Key strategic implications include improved production reliability, extended economic field life, and enhanced cross-functional coordination. The paper concludes by outlining future directions such as machine learning integration, cloud-based deployment, and the expansion of surveillance capabilities to include artificial lift and completions diagnostics. Collectively, this model provides a robust foundation for transforming marginal fields into actively managed, performance-driven assets.