Numerical Method for Horizontal and Vertical Spatial Resolutions of Seismic Acquisition Geometries in Complex 3D Media

Abstract

Spatial sampling, finite bandwidth, and overlying-strata shielding are three key issues to affect the spatial resolution of seismic imaging for deep targets. Some factors have a great impact on the horizontal resolution, whereas others influence the vertical resolution. How to quantify these effects remains controversial for complex media. Most previous studies on seismic acquisition geometries focus on the horizontal resolution for layered media but neglecting to measure the vertical resolution especially in complex media. Conventional criteria for vertical resolution are based on the theory of geometric seismology with the assumption of a simple medium. As a practical alternative for resolution estimation in complex media, numerical methods with wavefield extrapolation for focal-beam analysis can provide comprehensive insight into the combined effect of acquisition geometries, bandlimited frequencies, and complex media on the horizontal and vertical spatial resolutions of acquisition geometries. We incorporate some classic criteria into the focal-beam numerical analysis to measure the spatial resolutions. Four parameters are used to quantify the performance of acquisition geometries. The horizontal (vertical) resolution is defined as the main-lobe width of a focal beam along the horizontal (vertical) direction, whereas the square root of the peak-to-total ratio of energies is referred to as the horizontal (vertical) sharpness. These parameters describe the horizontal and vertical spatial resolution and sharpness to image the target. Numerical examples with typical acquisition geometries demonstrate the performance of numerical resolution analyses in complex media.

Automated Seismic Acquisition Geometry Design for Optimized Illumination at the Target: A Linearized Approach

“…However, certain targets can be inadequately imaged due to the complex geological overburden [2], [3]. The hampered image quality at the target area of interest can cause high uncertainties in reservoir monitoring and production, which can lead to high expenses.…”