Probability Estimation of Direct Hydrocarbon Indicators Using Gaussian Mixture Models

Abstract

This work aims to delimit the Direct Hydrocarbon Indicators (DHI) zones using the Gaussian Mixture Models (GMM) algorithm, an unsupervised machine learning method, over the FS8 seismic horizon in the seismic data of the Dutch F3 Field. The dataset used to perform the cluster analysis was extracted from the 3D seismic dataset. It comprises the following seismic attributes: Sweetness, Spectral Decomposition, Acoustic Impedance, Coherence, and Instantaneous Amplitude. The Principal Component Analysis (PCA) algorithm was applied in the original dataset for dimensionality reduction and noise filtering, and we choose the first three principal components to be the input of the clustering algorithm. The cluster analysis using the Gaussian Mixture Models was performed by varying the number of groups from 2 to 20. The Elbow Method suggested a smaller number of groups than needed to isolate the DHI zones. Therefore, we observed that four is the optimal number of clusters to highlight this seismic feature. Furthermore, it was possible to interpret other clusters related to the lithology through geophysical well log data.