Multiple-frequency attribute blending via adaptive uniform manifold approximation and projection and its application on hydrocarbon reservoir delineation

Abstract

Multifrequency attribute blending is a highly effective tool for characterizing hydrocarbon reservoirs. It begins by extracting multifrequency attributes of seismic data based on time-frequency transformation. Subsequently, a blending algorithm is used to fuse the extracted multifrequency components, thereby obtaining the interpretation results of the interested reservoirs. The red-green-blue (RGB) algorithm is commonly used to fuse the multifrequency components. However, it should be noted that the RGB blending algorithm can only fuse three frequency components, i.e., the low-, middle-, and high-frequency components. Moreover, it can occasionally introduce ambiguities, making it difficult to interpret areas that appear white or yellow. To address these issues, we develop a workflow for multiple-frequency component analysis to delineate hydrocarbon reservoirs. First, we apply the generalized S-transform to obtain the multiple-frequency components of seismic data. Then, the correlation analysis is developed and implemented to select the sensitive frequency components. Finally, we use the uniform manifold approximation and projection, a nonlinear dimension reduction algorithm, to blend the extracted multiple-frequency components and obtain reservoir interpretation results. We apply the suggested workflow to synthetic data and a 3D field data volume to evaluate its effectiveness. Our mathematical analysis demonstrates that the suggested workflow can effectively fuse multiple-frequency components to accurately characterize hydrocarbon reservoirs.