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Abstract

Summary

Recently, there has been increased interest in deep sedimentary systems such as along-slope bottom currents (contourite systems) and their interaction with gravitational sedimentary processes (mixed systems) due to the high reservoir potential of the resulting deposits. In particular, the Contourite Depositional System (CDS) of the Gulf of Cádiz includes examples of Late Miocene contourites and mixed systems deposited at depths that place them as potential reservoirs for CO2 storage. The distinction between different types of deep-marine deposits (pure turbidites, contourites or mixed deposits) is of paramount importance for the delineation and characterization of these systems as geological storage. However, the criteria used to distinguish between these deposits especially in the Miocene record are still under investigation. The objective of this work is to test several seismic facies analysis workflows based on machine learning (ML) to discriminate the different types of Late Miocene deep marine deposits. Four seismic attributes have been selected as candidate inputs for ML workflows. Among the ML algorithms, PNN has a high potential to combine these attributes and provide good diagnostic criteria for discriminate deepwater sedimentary systems. This will ultimately help to assess the socio-economic impact of these deposits on climate change mitigation and Energy Transition.

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2023-11-14
2025-11-16

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References

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/content/papers/10.3997/2214-4609.202321041
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Discrimination of Deep Water Sediments Using Machine Learning Techniques for CO2 Storage Characterization
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202321041
/content/papers/10.3997/2214-4609.202321041
/content/papers/10.3997/2214-4609.202321041
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