Work

Seismic Facies Classification of Salt Structures and Sediments in the Northern Gulf of Mexico Using Self-Organizing Maps

Year: 2025

Type: article

Abstract: Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study aims to leverage seismic multi-attribute analysis and machine learning to improve reservoir characterization, which could inform field development strategies for hydrocarbon exploration and hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, and spectral frequency attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate thicker sediments, gas hydrates, salt structures, associated radial faults, gas-charged sediments, and areas with poor data quality due to the presence of salt bodies than other SOM Models. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information.

Source: Geosciences

DOI: 10.3390/geosciences15050183

Authors: Silas Samuel, Camelia C. Knapp, James H. Knapp

Institution: Oklahoma State University

Language: English

Topic: Seismic Imaging and Inversion Techniques

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,172