Abstract
Considering the high number of wells in offshore formations such as the Gulf of Mexico having a risk of leaking gas into the surrounding formations, a deep understanding of the fate and transport of gas released from damaged wells is of special relevance for hazard assessment and prevention in offshore petroleum operations. This work explores a robust strategy to reduce the risk and impact of contaminant releases in an offshore formation by analyzing the applicability of machine learning technology as a tool to forecast the information regarding a possible broaching in a loss of containment scenario of an offshore well. Numerical simulations to describe the system behavior in hydrate-bearing media were implemented to generate the data regarding the broaching day and location, and the hydrate mass generated and the total released CH4 in gas phase in the system. Using the data generated from the different scenarios, we trained multiple Artificial Neural Networks for the prediction of the different outputs, which showed excellent correlations between the input and output features. This is the first study to combine machine learning technology for advanced reservoir simulation to reduce the broaching hazard of gas escaping from an offshore production well.
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