The increase in costs in the exploration and production of oil and gas in deep waters has led companies in the sector to invest in innovative technologies to detect, locate and correct faults in their production systems.

This research aims to develop a methodology for monitoring and detecting leaks in subsea structures based on deep neural networks, allowing automated, efficient, and less costly monitoring than conventional monitoring methodologies.

A set of monitoring data will be pre-processed for noise elimination, resolution improvement and resizing, to obtain a better performance of the algorithm. The next step consists of extracting relevant characteristics from the dataset to clearly identify the leak.

The results show the metrics used to evaluate the performance of the neural network as the accuracy and efficiency of the algorithm to detect leaks in the underwater structures and equipment.

Images of the Gulf of Mexico oil spill were used to test the methodology and the successful detection of the leak demonstrates the potential of the methodology for underwater leak detection.

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