UNCERTAINTY ASSESSMENT IN UNSUPERVISED MACHINE LEARNING METHODS FOR DEEPWATER CHANNEL SEISMIC FACIES USING OUTCROP-DERIVED 3D MODELS AND SYNTHETIC SEISMIC DATA

Unsupervised machine learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often relies on geoscientists’ expertise, necessitating a robustness assessment of these methods. To evaluate their reliability, synthetic data generated from an actual outcrop model were employed to demonstrate how two unsupervised methods, Self-Organizing Maps (SOM) and Generative Topographic Maps (GTM), cluster deepwater channel-related seismic facies and then measure the associated error. Six seismic attributes, comprising RMS amplitude, instantaneous envelope, peak magnitude, and spectral decomposition frequencies at 20, 40, and 55 Hz, served as input variables. Geobodies were assigned to each cluster formed, and error in facies clustering was quantified by comparing the actual 3D model with the facies grouped by machine learning methods on a voxel-by-voxel basis. This allowed for error quantification and the computation of metrics such as F1 score and accuracy through correlation matrices. Key findings revealed that (1) GTM and SOM exhibited similar performance, with a clustering configuration of 81 for GTM slightly outperforming others. (2) Error rates were approximately 2% for the predominant facies (background shale) but significantly higher for individual channel-related facies, suggesting that channel clusters might represent multiple facies. (3) Resolution and imbalanced data distribution impacted seismic facies predictability, resulting in nonuniqueness in cluster generation. (4) Using synthetic seismic data proved valuable for experimenting with different unsupervised ML, highlighting the need for assessing uncertainty in these methods, given their implications for crucial economic decisions reliant on reservoir interpretation, modeling, and volumetric estimations.