Seismic facies analysis using machine learning techniques: a review and case study

Seismic facies analysis which is aimed at identifying subsurface geological features from seismic data, has evolved due to the time-consuming and labor-intensive nature of its traditional approach. To address these challenges, numerical frameworks such as machine learning have been applied, yet attribute selection still comes with some challenges, particularly for inexperienced interpreters. Additionally, validating results in regions with limited well data poses significant challenges. This paper addresses these challenges through a comprehensive review of seismic facies workflows and a proposed workflow for a case study in the Gulf of Guinea. In this case study, seismic attribute selection is significantly based on the contribution (weights) of the individual attributes in a larger set of attributes. Also, we have introduced spectral decomposition for interpretation and initial validation of the workflow due to its independence on well data. Here, we applied an unsupervised vector quantizer to seismic attribute selection and facies analysis. Using a backward feature selection (BFS) approach for attribute selection based on computed weights assigned by our unsupervised vector quantizer (UVQ) network, we selected six seismic attributes for our facies analysis and tested five different attribute combinations of the attributes for facies analysis. This was followed by spectral decomposition colorblend of 5 Hz, 10 Hz, and 15 Hz frequencies. The facies generated using our seismic attributes varied with each combination due to the variations in the individual attributes. Correlating our seismic attributes and spectral decomposition to our facies, it was possible to identify lithological variations without solely relying on well data. Insights from this paper show the suitability of the automatic approach to seismic facies analysis in aiding the identification of new reserves which can bolster the economies of developing countries.