Post-migration seismic data conditioning methods on a merged dataset

Abstract

Analyzing amplitude anomalies in seismic data requires a comprehensive understanding of the geological context and the accuracy of the seismic image to faithfully represent the subsurface. Over the past four decades, numerous surveys in mature basins like the US Gulf of Mexico have undergone reprocessing and merging to enhance imaging quality. While this reprocessing primarily aims to optimize imaging for historical targets, it may yield suboptimal results for current objectives, such as identifying and characterizing shallow targets mandated by government regulations to prevent oil blowouts. The merging of seismic data volumes demands careful attention during processing, as the different volumes are often acquired at different times with different hardware, acquisition geometries, and exploration objectives. If insufficient care is taken, significant differences in the amplitude and spectra of the merged survey components can pose challenges when used as input for machine learning techniques or seismic attribute studies. To address discrepancies in the Matagorda Island merged survey, we implemented spectral balancing followed by structure-oriented filtering. Spectral balancing equalizes high and low frequencies, creating a more uniform frequency spectrum. Structure-oriented filtering eliminates random and cross-cutting coherent noise while preserving structural and stratigraphic features. This workflow ameliorates the discrepancies between the areas covered by the individual surveys, resulting in a more consistent interpretation across the seam between the two surveys. However, the application of this workflow posed a challenge in improving features observed at the tuning frequency and also exacerbating the high-frequency noise due to the presence of footprint, thus resulting in a more challenging interpretation of faults and fractures in some areas.