Optimizing the management of production losses in mature fields is essential to maximizing operational efficiency, particularly in large- scale reservoirs such as Cerro Dragón. One of the most critical challenges is the accurate and timely identification of well downtime events and the determination of their root causes, which are key aspects for the effective allocation of resources and strategic decision- making.
This paper describes the development of an advanced automation tool designed for detecting and managing downtime events in production wells. The tool not only enables the automatic identification of downtime events but also provides intelligent suggestions regarding the probable causes of these events, logging this information while seamlessly integrating with existing management and control systems.
The methodological approach comprised several key stages: the design and development of the detection algorithm, its validation through rigorous testing, and its final implementation in a real operational environment. The results demonstrate a significant reduction in the time required for manual data entry, along with substantial improvements in the accuracy and consistency of event detection. These advancements not only optimize operational efficiency but also provide a robust framework for real-time, data-driven decision- making.