Sensitivity analysis of seismic attributes parametrization for interpretation of a multi-story deepwater channel system: Tres Pasos Formation, Magallanes Basin Chile

Abstract

Mapping reservoir architecture (geobody size, shape, and stacking patterns) in the subsurface is critical for exploring and producing hydrocarbons, CO2 storage, and geothermal resource development since it can define connectivity or compartmentalization of flow zones (Meirovitz et al. 2020). However, our capacity to interpret depositional system architecture is limited by seismic resolution. In addition to limited bandwidth, the resolution of discrete geologic features mapped by seismic attributes can be mixed through the vertical analysis window. In this work, we use synthetic seismic data derived from an outcrop analogue to better understand how seismic bandwidth affects the vertical and areal resolution of stacked stratigraphic features. We studied five synthetic seismic volumes from low to high-frequency bandwidths of 15 Hz, 30 Hz, 60 Hz, 90 Hz, and 180 Hz from a deepwater channelized slope system in the Magallanes Basin, Chile. We analyze the effect of different seismic attributes: coherence, dip magnitude, dip azimuth, root mean square amplitude, and Laplacian filters on our different bandwidth data to understand how much “mixing” of stratigraphic features there is by comparing with the true geological model. We explore how the attributes’ parametrization affects the imaging of differently sized features by modifying the analysis window in each case from +/-2ms to +/- 100 ms. Results show that the “mixing” occurs as a result of 1) the seismic bandwidth, 2) the algorithm used for each seismic attribute calculation, and 3) the attribute analysis window. Broad band, higher frequency data and small analysis windows provide clear images of the stacked channels. In contrast, low-frequency data and larger analysis windows result in more mixing or “composite” appearance, affecting interpretations and NTG estimates. The Laplacian filter’s use proves to enhance the distinction of the different channel architectures providing high-resolution edge detection even for the lower frequency data.